WPS8259


Policy Research Working Paper                   8259




  Asset Prices and Macroeconomic Outcomes
                              A Survey

                              Stijn Claessens
                              M. Ayhan Kose




Development Economics
Development Prospects Group
November 2017
Policy Research Working Paper 8259


  Abstract
 This paper surveys the literature on the linkages between                          survey addresses these questions in the context of four
 asset prices and macroeconomic outcomes. It focuses on                             major asset price categories: equity prices, house prices,
 three major questions. First, what are the basic theoret-                          exchange rates and interest rates, with a particular focus
 ical linkages between asset prices and macroeconomic                               on their international dimensions. It also puts into per-
 outcomes? Second, what is the empirical evidence sup-                              spective the evolution of the literature on the determinants
 porting these linkages? And third, what are the main                               of asset prices and their linkages with macroeconomic out-
 challenges to the theoretical and empirical findings? The                          comes, and discusses possible future research directions.




  This paper is a product of the Development Prospects Group, Development Economics. It is part of a larger effort by the
  World Bank to provide open access to its research and make a contribution to development policy discussions around the
  world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be
  contacted at Stijn.Claessens@bis.org and akose@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
         issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
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                                                       Produced by the Research Support Team
             Asset Prices and Macroeconomic Outcomes: A Survey


                               Stijn Claessens and M. Ayhan Kose




      JEL classifications: D53, E21, E32, E44, E51, F36, F44, F65, G01, G10, G12,
      G14, G15, G21
      Keywords: Equity prices, exchange rates, house prices, interest rates, credit,
      output, consumption, investment, real-financial linkages, macro-financial
      linkages, imperfections, frictions.



  Claessens (Monetary and Economic Department, Bank for International Settlements; CEPR;
Stijn.Claessens@bis.org); Kose (Development Prospects Group, World Bank; Brookings Institution;
CEPR; CAMA; akose@worldbank.org). We are grateful to Olivier Blanchard for his helpful comments
at the early stages of this project of surveying the literature on macro-financial linkages when we were
with the Research Department of the International Monetary Fund. This paper has a companion,
“Macroeconomic implications of financial imperfections: a survey.” We would like to thank Boragan
Aruoba, Claudio Borio, Menzie Chinn, Ergys Islamaj, Jaime de Jesus Filho, Raju Huidrom, Atsushi
Kawamoto, Seong Tae Kim, Grace Li, Raoul Minetti, Ezgi Ozturk, Eswar Prasad, Lucio Sarno, Hyun
Song Shin, Kenneth Singleton, Naotaka Sugawara, Hui Tong, Kamil Yilmaz, Kei-Mu Yi, Boyang
Zhang, Tianli Zhao, and many colleagues and participants at seminars and conferences for useful
comments and inputs. Miyoko Asai, Ishita Dugar and Xinghao Gong provided excellent research
assistance. Mark Felsenthal, Sonja Fritz, Krista Hughes, Serge Jeanneau, Graeme Littler, Tracey
Lookadoo, and Rosalie Singson provided outstanding editorial help. The views expressed in this paper
are those of the authors and do not necessarily represent those of the institutions they are affiliated
with or have been affiliated with.
ii
“...They [economists] turned a blind eye to the limitations of human rationality that often
lead to bubbles and busts; to the problems of institutions that run amok; to the imperfections
of markets – especially financial markets – that can cause the economy’s operating system to
undergo sudden, unpredictable crashes ...”
Paul Krugman (2009a)


““Hello, Paul, where have you been for the last 30 years?”... Pretty much all we have been
doing for 30 years is introducing flaws, frictions and new behaviors... The long literature on
financial crises and banking ... has also been doing exactly the same....”
John H. Cochrane (2011a)


“I believe that during the last financial crisis, macroeconomists (and I include myself among
them) failed the country, and indeed the world. In September 2008, central bankers were in
desperate need of a playbook that offered a systematic plan of attack to deal with fast evolving
circumstances. Macroeconomics should have been able to provide that playbook. It could
not...”
Narayana Kocherlakota (2010)


“... What does concern me of my discipline, however, is that its current core – by which I
mainly mean the so-called dynamic stochastic general equilibrium (DSGE) approach – has
become so mesmerized with its own internal logic... This is dangerous for both methodological
and policy reasons... To be fair to our field, an enormous amount of work at the intersection
of macroeconomics and corporate finance has been chasing many of the issues that played a
central role during the current crisis... However, much of this literature belongs to the
periphery of macroeconomics rather than to its core...”
Ricardo Caballero (2010)


“One can safely argue that there is a hole in our knowledge of macro financial interactions;
one might also argue more controversially that economists have filled this hole with rocks as
opposed to diamonds; but it is harder to argue that the hole is empty.”
Ricardo Reis (2017)


“The financial crisis ... made it clear that the basic model, and even its DSGE cousins, had
other serious problems, that the financial sector was much more central to macroeconomics
than had been assumed...”
Olivier Blanchard (2017a)




                                               iii
                                         Table of contents

1. Introduction ........................................................................................ 1

2. Asset prices and macroeconomic variables ............................................ 5
     A. Basic mechanisms ............................................................................ 5
     B. Empirical evidence .......................................................................... 9
     C. Challenges to the standard models ................................................ 12

3. International dimensions of asset prices ...............................................17
     A. Determination of asset prices in open economy models .................. 18
     B. Empirical evidence ........................................................................ 18
     C. International dimensions of asset pricing puzzles ........................... 20

4. Exchange rates and macroeconomic outcomes .....................................21
     A. The basic mechanisms ................................................................... 22
     B. Empirical evidence ........................................................................ 25
     C. Exchange rate puzzles ................................................................... 30

5. Interest rates and macroeconomic outcomes ........................................35
     A. Basic mechanisms .......................................................................... 35
     B. Empirical evidence ........................................................................ 37
     C. Challenges to the standard models ................................................ 39

6. Conclusions ........................................................................................42
     What are the main takeaways? ............................................................ 42
     Possible avenues for future research ..................................................... 44

References ...............................................................................................62




                                                      iv
1. Introduction

The past quarter century has seen dramatic movements in asset prices and real economic
activity. Equity prices rose significantly during the second half of the 1990s and then fell
abruptly in 2000–01 with the unwinding of the high-tech bubble. The large decline in equity
markets coincided with recessions in many advanced economies. House prices increased
substantially over 1996–2007 but declined sharply after that. The collapse in house prices was
accompanied by the Great Financial Crisis (GFC) of 2007–09, which led to deep recessions
in almost all advanced economies after an extended period of macroeconomic stability – the
so-called Great Moderation.1 Policymakers reduced interest rates to zero or even below as
they attempted to mitigate the adverse effects of recession and establish a durable recovery.
Exchange rates also swung widely, especially during periods of intense financial stress.
    These developments highlight the importance of understanding the linkages between asset
price movements and macroeconomic outcomes. Specifically, the GFC was a bitter reminder
of how pronounced fluctuations in asset prices can have a dramatic impact on the balance
sheets of households, corporations, financial intermediaries and sovereign nations. As asset
prices fell sharply and the global financial system edged to the brink of collapse in late 2008,
the global economy experienced its deepest contraction in more than half a century. This led
to unprecedented challenges for fiscal, monetary and financial sector policies.
    The links between asset prices and macroeconomic outcomes are obviously at the centre
of broader macro-financial linkages – the two-way interactions between the real economy and
the financial sector. Shocks arising in the real economy can be propagated through asset prices
via the operations of the financial sector, thereby amplifying business cycles. Imperfections in
financial markets can intensify shocks to asset prices and consequently lead to more
pronounced macroeconomic fluctuations. Conversely, developments in financial markets can
be sources of shocks, which can, in turn, result in more pronounced asset price movements
and macroeconomic fluctuations. Through cross-border connections, these developments can
lead to international spillovers.
    This paper surveys the literature on the linkages between asset prices and macroeconomic
outcomes. Given the enormous volume of work on this topic, we focus on three specific
questions. First, what are the basic theoretical mechanisms linking asset prices and
macroeconomic outcomes? Second, what is the empirical evidence supporting these linkages?
And third, what are the main challenges to the theoretical and empirical findings? Our survey
only scratches the surface of this large literature by providing a broad perspective on these
questions in the context of the following asset price categories: equity prices, house prices,
exchange rates and interest rates.2


1
  A number of books, academic studies and “popular” literature pieces, discuss various macro-financial
linkages that have contributed to the devastating impact of the GFC on the real economy: Krugman
(2009b), Sorkin (2009), Wessel (2009), Lewis (2010), Kose and Prasad (2010), Paulson (2010), Gorton
(2012), Turner (2012), Bernanke (2013), Blinder (2013), Borio (2014), Claessens et al (2014a), Mian
and Sufi (2014), Wolf (2014), Geithner (2014), Farmer (2016), King (2016) and Taylor (2016). Lo
(2012) reviews a set of 21 books on the GFC.
2
  Reference books analysing the dynamics of asset prices and their connections to macroeconomic
outcomes include: Campbell et al (1996), Duffie (2001), Cochrane (2005, 2006, 2017), Singleton (2006),
Pennacchi (2007), Brunnermeier et al (2013), Quadrini (2014), Morley (2016) and selected chapters of
Constantinides et al (2013). See also Campbell (2014) who discusses the research on asset pricing
conducted by the laureates of the 2013 Nobel Prize in Economics (Eugene Fama, Lars Peter Hansen



                                                  1
     Our survey contributes to the literature on the links between asset prices and
macroeconomic outcomes in several dimensions. First, it presents a broad review of the
theoretical and empirical work on the main determinants of asset prices and the basic linkages
between asset prices and economic activity. Second, it documents that basic models are able
to capture many linkages as documented by a wide range of empirical studies. However, it
also shows that a number of puzzles remain regarding the behaviour of asset prices and their
interactions with macroeconomic outcomes. Third, it emphasises the global dimensions of
asset price determination, the linkages between asset prices and economic activity and the
critical role of such prices in the transmission of shocks across borders. Finally, to help guide
future research, the survey attempts to identify the major gaps in knowledge on these issues.
     Given the complex nature of the linkages between asset prices and macroeconomic
outcomes, a survey on the topic comes with a number of caveats. First, both asset prices and
macroeconomic outcomes are endogenous variables and, as a result, the nature of the
relationships between them ultimately depends on the models employed for the analysis. Our
objective here is to present the basic linkages in the context of standard models and review
the most relevant empirical studies that test for the presence of these links. Second, while we
study the linkages between asset prices and macroeconomic outcomes, we are keenly aware
that many micro-level factors drive macro-level variables. Hence, whenever possible, we draw
lessons from theoretical and empirical studies at the microeconomic level for macroeconomic
aggregates.
     Third, our focus is mostly on standard models, ie models that do not necessarily account
for financial market imperfections. We consider these “frictionless” models as the benchmark
frameworks to study the linkages between asset prices and macroeconomic outcomes. We
present a discussion of models with financial imperfections in our companion survey
(Claessens and Kose (2017)). Finally, although we did our best to include all major studies
on the topic, it is unavoidable that a survey on such a vast literature would miss some
contributions.
    We start with a general discussion of the determinants of asset prices with standard
models of “complete markets”. These models often apply to all types of asset. But to simplify
the presentation, we first focus on equity and house prices within a closed economy context.
We then analyse the international dimensions of asset prices. Next, we discuss in more detail
two other major asset prices: exchange rates and interest rates (and related bond prices). In
order to make our survey more accessible, each section provides a self-contained review of the
relevant segments of the literature. We also structure each section to address systematically
the three questions posed above.
    Section 2 starts with a brief analysis of the determination of asset prices in standard
models. Models operating within a “complete markets” paradigm provide the basic analytical
foundations for the determination of asset prices. Asset prices, like other prices, are
endogenous and adjust to clear markets, including “anonymous” financial markets in these
models. The standard models provide useful frameworks as they highlight the basic linkages
between asset prices and agents’ decisions, including through the well-known channels
associated with wealth and substitution effects. Asset prices also provide economic agents
with signals that allow them to make optimal saving and investment decisions. They also
carry information about future profitability and income growth.



and Robert Shiller). Woodford (2003) analyses interest rates and prices and the role of monetary policy
in shaping them. Sarno and Taylor (2002), Klein and Shambough (2010), Chinn (2012), Rossi (2013)
and Engel (2014) provide extensive surveys of different aspects of exchange rates.


                                                  2
    This section then turns to the empirical literature, analysing the linkages between asset
prices, especially equity and house prices, and real variables. Empirical studies support many
of the predictions of standard models with respect to linkages between asset prices and
macroeconomic outcomes. First, movements in asset prices are associated with changes in
investment and consumption that are broadly consistent with the predictions of many
standard models. In particular, studies that use microeconomic data support various
theoretical predictions regarding the impact of asset prices on household and firm behaviour.
Second, asset prices appear to play a “signalling role” as they tend to comove with (or lead)
various measures of current and future activity.
    Although the basic models provide helpful guidance, a number of puzzles remain,
particularly with respect to inconsistencies between the predictions of models and the data.
First, asset prices are much more volatile than fundamentals would imply. They can at times
deviate substantially, or at least appear to, from predicted values based on fundamentals.
Second, there are many questions about the quantitative importance of the linkages between
asset prices and macroeconomic aggregates. The strength of these empirical linkages appears
to depend on various factors. Investment and consumption, for example, respond differently
to changes in asset prices than standard models would predict, with a significant role for non-
price factors in influencing agents’ behaviour. Third, there are limits to the predictive power
of asset prices for economic activity. Empirical evidence also suggests that the channels
leading to the predictive power of prices may be different from those suggested by the basic
models.
    Section 3 briefly reviews the international dimensions of linkages between asset prices
and macroeconomic outcomes. Given the extent of cross-border integration of real and
financial markets today, any discussion of the linkages between asset prices and activity has
to take into account the international dimensions. Like their closed economy counterparts,
however, many of the international asset pricing models are based on partial equilibrium
analysis. Moreover, these models often do not consider whether international asset holdings
are consistent with observed prices. While recent theoretical studies have taken significant
steps to remedy these shortcomings and analyse the dynamics of asset prices in richer, general
equilibrium environments, there is a broad realisation that gaps remain.
    Empirical studies find that certain asset prices tend to move together and emphasize the
importance of common (global) shocks (factors) in explaining fluctuations in asset prices.
This is a natural outcome of the major role that international financial integration has played
in shaping asset price movements in recent decades. In addition, the empirical literature
documents that domestic financial development and trade integration affect the degree of
comovement of asset prices across countries. Even prices of non-tradable assets, such as real
estate, tend to move together across countries, suggesting that there are indeed global factors
driving asset price movements.
    However, there are many puzzling aspects associated with the international dimensions
of asset prices. First, international financial integration appears to amplify both the volatility
and comovement of asset prices beyond what standard models would suggest. Second, the
lack of international diversification of portfolio investment, the so-called home bias, has been
hard to reconcile with the predictions of most asset pricing models. Moreover, the prices of
internationally-traded assets continue to depend on local risk factors, suggesting some de
facto segmentation of markets despite the removal of many barriers to cross-border trading
(especially of equities).
     Section 4 starts with a brief review of the determinants of exchange rates. The theoretical
literature on exchange rate determination has gone through several phases, from basic
arbitrage-related models to fully fledged general equilibrium models. These models point to


                                                3
long-run relations between exchange rates and a wide range of real and nominal variables.
They also show that the exchange rate can play an important role in the transmission of
monetary policy for small open economies. The more recent literature, often classified under
the rubric of “new open economy macroeconomics”, is making increasing use of advances from
the closed-economy macroeconomic literature to help explain the properties of exchange rates
in environments featuring nominal rigidities, imperfect competition and rational agents.
    There has been a large theoretical literature analysing the linkages between fluctuations
in exchange rates and macroeconomic fundamentals. Theoretical models are used to study
how changes in exchange rates relate endogenously to various macroeconomic variables and
how these relationships are affected by a variety of factors, including: the heterogeneity of
sectors; economies of scale and imperfect competition; type of exchange rate regime; country-
specific elements and time horizons. However, some of the theoretical linkages remain
ambiguous, including the impact of exchange rates on investment and the effects of
devaluations on output. Recent models employ richer environments and consider the roles of
financial variables and valuation effects to get a better understanding of existing linkages
between exchange rates and real and financial aggregates.
     Empirical studies provide mixed evidence about the strength of linkages between
exchange rates and macroeconomic outcomes. First, while most studies show that a
depreciation (appreciation) tends to be associated with a contraction (expansion) of
investment, the potency of this relationship varies across sectors, countries and time horizons.
Second, while the exchange rate appears to play a supportive role in facilitating the reversal
of current account imbalances, the quantitative importance of this role is ambiguous. Third,
the exchange rate is a transmission channel through which monetary policy could affect the
real economy but the strength of this channel appears to depend on many factors, including
the sensitivity of interest rates to exchange rates, the degree of openness, the exchange rate
regime, and the currency composition of debt and, related, any mismatches.
    Moreover, a number of puzzles remain about the interactions between exchange rates and
macroeconomic variables, and even more so about the linkages between exchange rates and
financial variables. The key puzzle is the disconnect between exchange rates and
macroeconomic aggregates, as reflected in the limited success of exchange rate models relating
future exchange rates to underlying short-run fundamentals. The roles played by financial
variables in driving the behaviour of exchange rates have yet to be explained satisfactorily.
    Section 5 reviews the links between interest rates and macroeconomic outcomes. Interest
rates, real and nominal, play key roles in financial intermediation and can drive
macroeconomic outcomes. The theoretical mechanisms that relate changes in (short-term)
interest rates to fluctuations in output are well captured by standard models. The short-term
nominal interest rate is, of course, closely related to the conduct of monetary policy. One of
the main channels of monetary policy transmission, the direct interest rate channel, for
example, focuses on the impact of interest rates on investment and consumption.
    Empirical research confirms the special role played by interest rates in shaping activity.
First, interest rates have a substantial effect on investment, consumption and overall activity.
Second, there is evidence supporting the presence of a direct interest rate channel of monetary
policy. Third, long-term interest rates relate to short-term rates through expectations and
arbitrage in ways that are often consistent with the predictions of the standard models.
Moreover, the spread between long- and short-term interest rates and other characteristics of
the yield curve help predict the timing of recessions and the behaviour of some macroeconomic
aggregates.




                                               4
    Much evidence supports the key channels but research also suggests there are other
factors that affect the transmission of monetary policy. First, the quantitative importance of
the direct interest rate channel has been debated. While empirical results are not necessarily
inconsistent with the existence of a direct channel, they do suggest the need to consider firm,
household and financial system heterogeneity, and variations over time, including in the state
of domestic and international financial conditions. Second, there has been debate about the
predictive value of the (slope of the) yield curve for future economic activity. More generally,
the shape of the yield curve is determined by a variety of factors, including risk premia that
can vary with economic and financial conditions. Lastly, the experience with unconventional
monetary policies (UMP) since the GFC has introduced new aspects about the linkages
between asset prices and activity that require further research.
    Section 6 concludes with a summary and a discussion of future research. It highlights the
key findings and documents some of the major gaps in the literature. It also discusses possible
directions for future research, stressing the need for richer theoretical models, more robust
empirical work and better quality data so as to advance knowledge and help guide
policymakers going forward.



2. Asset prices and macroeconomic variables

This section examines the basic determinants of asset prices and their linkages to
macroeconomic variables in the context of standard models without frictions. These models,
which often assume a world of complete markets in an Arrow-Debreu sense (as discussed
below), provide the basic analytical foundations for the determination of asset prices.
    The section comprises three parts. It starts with a brief summary of the basic price
determination mechanisms contained in standard models and the implications of changes in
asset prices for economic activity. It then reviews empirical studies, providing evidence
relating to these mechanisms. It concludes with a summary of the major shortcomings of the
models.

A. Basic mechanisms
Determination of asset prices
In competitive market models without frictions, the prices of assets, like the prices of goods,
are determined by the forces of supply and demand. Assets studied typically include a broad
array of tradable claims, such as bonds, equities, real estate, plant and equipment, patents
etc. In these models, asset prices, as for other prices, reflect the equilibrium outcome of
aggregate demand and supply forces, with no clear feedback from asset prices to aggregate
demand or supply. This is clear in the most complete version, ie in an Arrow-Debreu world,
where contingent claims span every possible state of the world, allowing agents to insure
against any event, which in turn simplifies the choices they make.3 The absence of feedback
effects in these models makes them different from classes of models with a so-called financial
accelerator or other feedback mechanisms arising from frictions that give rise to macro-


3
 This is also referred to as the Arrow-Debreu-McKenzie model. For a short conceptual review of asset
pricing, see Geanakoplos (2008), and for an extensive treatment, see Cochrane (2005). For an overview
of the empirical determinants and properties of various asset prices, see Hordahl and Packer (2007).


                                                 5
financial linkages (see Claessens and Kose (2017)). That said, by providing signals to economic
agents, asset prices “help” households and corporations in making optimal decisions with
respect to saving, investment and consumption.
     In these models, asset prices reflect the present (discounted) value of future cash flows or
services. They recognise that asset ownership constitutes a claim on the income derived from
an asset (ie it is not the asset itself that is valued). The price of an asset is simply the present
value of its future cash flows (dividends). The canonical representation of this idea is described
by the “Gordon equation”, which is often used in the context of the determination of equity
prices (Gordon (1959 and 1962)). It implies that the price of an asset with a perpetual stream
of dividends can be expressed as its current dividend divided by the appropriate discount rate
for holding the asset minus the nominal growth rate of the dividends it pays. This calculation
requires one to project the path of future cash flows. Depending on the type of asset under
consideration, this entails the analysis of a wide range of factors influencing the stream of
cash flows, including macroeconomic variables – such as output, household consumption,
corporate investment and productivity – as well as uncertainty relating to these variables and
correlations among them.
     In addition, asset price determination requires the use of the “right” discount rate. The
risk-adjusted discount rate used in present value calculations is the sum of the risk-free rate
and the risk premium applicable to a specific asset. The risk-free rate can often be observed,
eg the interest rate on Treasury bills or government bonds. The risk premium depends on the
specific behaviour of an asset’s cash flow and can be determined using an asset pricing model,
assuming that markets are complete and without financial frictions. In partial equilibrium
models – for example, when only the behaviour of financial variables is modelled – this is
relatively easy as the risk-adjusted discount rate simply reflects movements in financial
variables. For example, in the basic capital asset pricing model (CAPM), the required
premium is determined by the degree to which an asset’s risk is non-diversifiable with respect
to all other assets, captured by its beta, and the excess of the rate of return on all assets (the
market rate of return) over the risk-free rate.4
    In general equilibrium, preferences, technology and real factors (physical endowments)
determine the discount rate and cash flows. In his seminal general equilibrium model, Lucas
(1978) links the required rate of return on assets to investors’ risk aversion and endowments
(“cash flows”). Other general equilibrium asset pricing models extend this basic idea. The
pricing model (“kernel”) has gradually become more complex in these frameworks, but the
underlying principle has remained the same: preferences, technology and the behaviour of real
factors determine jointly the risk-adjusted discount factor. In turn, they affect investment
and consumption decisions, with capital stocks and shocks to technology subsequently driving
future cash flows and output. Shocks to technology and/or preferences can then generate
correlated movements in investment, consumption, output and asset prices. The joint role of




4
  The CAPM was developed (in several stages) by Treynor (1962), Sharpe (1964), Lintner (1965) and
Mossin (1966). In a simple, one-factor CAPM, beta is equal to the regression coefficient of the observed
rate of return of an asset on the market rate of return. In more general settings, beta equals the
covariance of the cash flows of an asset with the cash flows of all assets traded in the economy relative
to the variance of the cash flows of all assets. The determination of the rate of return, or of the cash
flow process, is often left unspecified in such partial equilibrium models.



                                                   6
technology and preferences can be seen most easily in the context of a special class of general
equilibrium models, the so-called consumption capital asset pricing models (CCAPMs).5
     These general equilibrium models are highly stylised, however, and rely on a wide range
of assumptions. The standard ones, including those used in the real business cycle (RBC)
literature, most often assume complete markets, an absence of transaction costs and no
financial imperfections. These assumptions are typically similar to those made in deriving the
path-breaking Modigliani-Miller result of the irrelevance of financing structures for firm value
(see Brealey et al (2016) for a textbook treatment).
    In particular, as Modigliani and Miller (1958) showed, the market value of a firm is
independent of the way it is financed under the following assumptions: (i) neutrality of
taxation between debt and equity; (ii) no capital or financial market frictions (ie no
transaction costs, agency issues, asset trade restrictions or bankruptcy costs); (iii) symmetric
access to credit markets (ie firms and investors can borrow or lend at the same rate); and
(iv) no information relating to prospective financial policies of the firm. Other common
assumptions include no barriers across (international) markets, no heterogeneity among
participants and perfect divisibility of real and financial assets.
    The main implication of these assumptions is that the price of any asset, like the market
value of a firm, is solely determined by the present value of the cash flows it generates.6 While
many of the assumptions underlying the Modigliani-Miller irrelevance result and the complete
markets paradigm clearly do not apply in the “real” world, and have subsequently been
questioned by the literature, such frameworks have been very useful in establishing some
fundamental relationships. Adjustments correcting for specific deviations from these
assumptions have been made. For example, the differential treatment of taxes on debt and
equity can be accounted for by including the present value of tax shields (since interest
payments are tax deductible) to the value of the firm (see Graham (2013) for a review on
how taxes affect corporations’ financial decisions). Corrections can also be made to capture
the effects of inflation: for example, the real interest rate, rather than the nominal rate, may
need to be used.7


5
  In most models, the pricing formula equates the expected rate of return on an asset in excess of the
risk-free rate (its risk premium) to (the negative of) the covariance between that asset’s returns and
the (stochastic) discount factor (the investor’s intertemporal rate of substitution). Hence, the more
negative the covariance between an asset’s return and its discount factor, the higher its risk premium
is (Campbell (2003), Cochrane (2006), and Campbell et al (2015)). In consumption-based asset pricing
models, the discount factor is proportional to the covariance of the return on the asset’s cash flow with
consumption growth, thus creating a link between real (macroeconomic) aggregates and asset prices
(Cochrane (2000)). Ludvigson (2013) presents a review of the recent literature on consumption-based
asset pricing models.
6
  Another set of implications relates to the predictability of asset prices and the absence of arbitrage
opportunities. The notion that asset returns are impossible to predict if asset prices reflect all relevant
information (“follow a random walk”) goes back a long time (Bachelier (1900)). Fama (1970), in his
review of the existing literature, which included his own important empirical contributions (Fama
(1963, 1965)), introduced the term “efficient market hypothesis” to capture this concept. He also
provided a typology of possible empirical tests and related insights. See Fama (1991) for a review of
the subsequent literature (up to the late 1980s).
7
  In addition, the time profile of the cash flows may matter as the maturity of the discount rate needs
to match the profile of the cash flows. For example, the long-term interest rate, rather than the short-
term rate, plays a major role for corporate investment, housing and the consumption of durable goods.
The long-term interest rate is often a reflection of expected future short-term interest rates (the



                                                    7
    For a long period of time, the Arrow-Debreu framework and the related Modigliani-Miller
theorem greatly influenced the research agenda on macro-financial linkages (see Claessens
and Kose (2017) for an overview of the evolution of this literature). Methodological advances
in the 1970s contributed to this influence. Whereas money and credit featured prominently
in earlier work, researchers focused increasingly on the real side of the economy and relied on
the simplifying assumption that financial structures and intermediation did not matter for
firm value or for the real economy in general. Although some empirical studies employing
vector autoregressions (VARs), first proposed by Sims (1972), focused on the role of money
as the key financial aggregate, until the early 1980s the literature considered mostly
movements in real aggregates.

Asset prices and activity
The standard models make clear predictions about how asset prices relate to individual
agents’ investment and consumption decisions. Implicit in most models is that asset prices,
like any other prices, play an “allocative” role. This is clearly seen in corporations’ investment
and households’ consumption decisions, ie the allocation of resources across different
objectives, states of nature and time. This is probably best captured by Tobin’s q theory,
which posits that asset prices can be used to determine the market value of a firm’s existing
fixed capital stock. Tobin (1969) defines q as the market price of a firm, assuming that it is
traded, relative to the replacement (“book”) value of its assets. If q is high, new plants and
equipment capital are cheap to add relative to the market value of the firm. The firm can
then raise new equity and other external financing to expand its capital, or replace it, and
thereby increase overall firm value. Value can be added in this way until q converges to its
equilibrium level of one. Thus, the q theory establishes a natural link between asset prices
and corporate investment.8
    Models also show how asset prices influence households’ consumption and saving decisions
through wealth and substitution channels. In most such models, consumption decisions are
based on households’ lifetime wealth, including current and future income and current
financial and physical assets. Changes in asset prices can then influence current consumption
as they change individuals’ financial and real wealth.9 In addition, by altering the rate of
substitution between consumption allocations over time (the intertemporal marginal rate of
substitution), asset prices can affect households’ saving behaviour.10




expectations hypothesis). Textbooks, such as Brealey et al (2016), discuss many of the “corrections”
employed. Even with corrections, however, the market value of a firm and of other asset prices can
differ from the ones implied by the standard theoretical models. There has consequently been an
extensive research programme analysing the various deviations from the benchmarks.
8
  There are conceptual complications associated with the links between asset prices and investment,
even in the absence of financial frictions. An important one, pointed out by Hayashi (1982), is that it
is the marginal q which matters, not the average q. Since only the average q is observable, not the
marginal q, this requires further assumptions to allow for empirical tests. More importantly, though,
the presence of financial frictions can affect the basic relationship between q and investment.
9
   Wealth effects on consumption relate to Ando-Modigliani’s (1963) life-cycle/permanent income
hypothesis (see Deaton (1992) for a detailed analysis). Given households ’ limited ability to borrow
against future labour income and smooth consumption over their lifetime (ie in the presence of liquidity
constraints), a form of market incompleteness or of financial friction, changes in asset prices can
influence current consumption more than what is predicted by these models.
10
   Implications of changes in asset prices for household balance sheets have been an active area of
research (see a recent review by Guiso and Sodini (2013)).


                                                   8
    Another channel operates through the information that asset prices incorporate about
future profitability and income growth. Private fixed investment depends on expected output
growth. Financial markets can aggregate efficiently information about the state of the
economy and future prospects into asset prices (see Allen (1993)). For example, when
prospects of future corporate earnings improve, equity prices are expected to increase. To the
extent that asset prices reflect fundamentals or provide information about future output (or
sales) growth of a corporation or its competitors, the corporation will tend to respond to asset
price changes by adjusting its investment (in terms of the selection of specific projects or their
timing). At the aggregate level, changes in, say, expected productivity due to technological
advances can lead to movements in asset prices. Similarly, households’ consumption depends
on expectations about future income. Movements in asset prices can provide information to
households regarding current or future fundamentals, for example, by signalling faster or
slower future income growth. This can lead households to adjust their consumption and saving
behaviour.

B. Empirical evidence
There is extensive empirical evidence supporting a number of mechanisms linking asset prices
to microeconomic and macroeconomic outcomes. This section focuses on two major aspects
of this evidence. First, asset prices are associated with changes in investment and consumption
behaviour in manners predicted by the standard models. Second, the informational value of
asset prices can play a signalling role as such prices tend to comove with activity and appear
to help predict its future direction.11

Asset prices, investment and consumption
Empirical evidence, both at the micro- and macroeconomic levels, suggests that asset prices
appear to affect corporate investment decisions, as predicted by the basic models. Brainard
and Tobin (1968) established an empirical relationship between Tobin's q and investment.
Using firm-level data, Abel and Blanchard (1986) subsequently showed that firms’ marginal
q is positively related to their investment. At the aggregate level, various studies documented
the links between asset prices and private investment. Based on evidence for 19 OECD
countries, for example, Davis and Stone (2004) found a large elasticity, with a 1% change in
equity prices being associated on average with a 1% change in long-run investment. However,
others find minor or insignificant effects.12
    A number of studies provide evidence that such links can arise because of other channels.
Although most of these studies generally support the conclusion that equity prices are an
important determinant of investment, notably in countries with more developed financial
systems, some question this finding, in part due to the presence of channels arising from
financial frictions. Such frictions can, for example, lead to a relationship between Tobin's q
and investment independently of the investment adjustment and information channels
outlined above. Chirinko (1997) and Gomes (2001) show that financial constraints, such as


11
   Anecdotal evidence also indicates that the basic price determination models discussed here are widely
used for the valuation of firms, projects and assets (Benninga (2008)).
12
   Caballero (1999) and Altissimo et al (2005) provide additional discussions. See also Davis (2010a,
2010b) for a review of empirical papers on the relationship between asset prices and consumption and
investment as well as some specific regression results for 23 OECD countries. Estimating investment
functions for the G7 countries, Ashworth and Davis (2001) show that Tobin’s q only has a long-run
effect on investment in Japan and France.


                                                   9
when the cost of external finance depends on leverage, are likely to be reflected in q, making
that variable endogenous to firm choice. In a related paper, Erickson and Whited (2000) point
out that measurement error in q can lead to a positive relationship between a firm’s internal
cash flow and its investment, even when there is no direct relation (which otherwise would
suggest a deviation from the q model).
     While results depend on methodology and data samples, empirical studies at the
household level find significant wealth effects of asset prices on consumption. For the United
States, estimates of the marginal propensity to consume (MPC) out of overall wealth
(financial assets and illiquid assets, including housing) range between 4% and 7%. For
financial wealth only, estimates for the United States suggest changes in consumption of the
order of 0.03% to 0.07% for every 1% change in equity value.13 Empirical estimates vary by
country though. Bayoumi and Edison (2003) report that equity wealth effects are much
weaker for countries other than the United States: for every 1% change in equity value, the
change in consumption is 0.015% to 0.03% in Japan and 0.01% to 0.03% in various European
countries. Others find long-run elasticities ranging from very small (or insignificant) to 0.35%
on average for OECD countries (Catte et al (2004)).
    The effects of asset prices also vary depending on financial market characteristics. In
emerging market economies (EMEs), for example, wealth effects are small, possibly due to a
limited and concentrated participation of households in capital markets. Slacalek (2009)
reports an average wealth effect of 0.015% for a 1% change in equity value for 22 EMEs over
1985–2007. Funke (2004), in a study of 16 developing economies, reports that a 10% decline
in annual real equity market return is associated with a reduction in real private consumption
of about 0.2–0.4% on average.14
    Linkages between asset prices and macroeconomic outcomes also tend to vary by type of
asset. In theory, housing wealth could have a smaller effect on consumption than equity
market wealth because it is less clearly connected with future increases in productive potential
(Mishkin (2007)). Moreover, since housing services are a component of consumption, increases
in house prices can negatively affect consumption. At the same time, real estate wealth tends
to be a significant share of households’ wealth and households can often borrow against this
wealth and leverage the increase in house value for consumption purposes.
     Both house and equity prices are much more volatile than output (Table 1A). House
prices appear to be less volatile than equity prices, though, implying that changes in house
prices are likely to be perceived more permanent than changes in equity prices (Cecchetti
(2008)). Kishor (2007) reports that while 99% of the change in housing wealth is permanent,
ie it remains after one quarter, only 46% of the change in equity wealth is. Housing also
typically constitutes a larger share of total wealth, possibly making changes in house prices
more important for household consumption decisions.15


13
   For overall wealth estimates, see Gale et al (1999), Kiley (2000), Davis and Palumbo (2001), Barrell
and Davis (2007a) and Case et al (2013). Using a broader definition of equity wealth (including both
corporate equity and other types of security), the effect has been estimated at about 3 1/2 cents per
dollar change in wealth (Ludvigson and Steindel (1999)).
14
   See Kim (2004) for Korea, Peltonen et al (2012) for 14 EMEs, and IMF (2008) for cross-country
evidence.
15
   Lettau and Ludvigson (2004) also find that transitory shocks dominate variations in wealth in the
United States while permanent shocks dominate variations in aggregate consumption, helping explain
why little of the variation in household net worth relates to the variation in consumer spending. Kaplan
et al (2014) find that households holding (illiquid) housing wealth behave like hand-to-mouth



                                                  10
    Overall, the available evidence suggests that changes in house prices have a more
pronounced impact on consumption than equity prices. For the United States, Carroll et al
(2011) report that the propensity to consume from a $1 increase in housing wealth ranges
between two (short-run) and nine (long-run) cents, twice as large as that estimated for equity
wealth (see also Case et al (2013)). Kim (2004) shows that in Korea the elasticity of
consumption with respect to housing wealth is larger than with respect to equity market
wealth between 1988 and 2003. Gan (2010) reports a significant impact in Hong Kong of
housing wealth on consumption using a large panel data set of households. Case et al (2005),
using annual data for 14 advanced economies, show that house prices are more important
than stock prices in influencing consumption. These findings are consistent with the idea of
a more permanent nature of house price changes and a larger share of housing in total wealth.
Furthermore, changes in house prices also appear to have a differential impact on age groups,
which is consistent with the relative importance of housing in overall financial wealth.16 In
addition, there is some evidence of asymmetric effects, with negative shocks to asset prices
having a greater impact on consumption than positive shocks (Peltonen et al (2012)).
    Recessions associated with asset price busts and recoveries accompanied by asset price
booms tend to be more pronounced than those without such episodes. In particular, recessions
associated with asset price busts are significantly longer than recessions without such
disruptions (Claessens et al (2012), Drehmann et al (2012) and Muir (2017); Table 1B). They
also result in significantly larger drops in output and correspondingly greater cumulative
output losses. Given that about one third of recessions are accompanied by a house price
bust, these results point again to the relevance of asset prices movements for economic
activity.

Comovement between asset prices and macroeconomic outcomes
Confirming the predictions of most general equilibrium-type models, asset prices tend to be
correlated with current and future aggregate activity. Since they reflect current economic
developments, both equity and house prices comove with business cycles (Table 1C). The
contemporaneous correlations between house prices and output, however, tend to be higher
than those between equity prices and output while the definitions of asset price cycles affect
their correlations with the real sector.
    Being forward-looking variables, equity prices provide the market’s aggregated views of
future economic prospects. Empirical evidence for advanced economies confirms that changes
in equity prices tend to lead output growth by a few quarters (Table 1C). The channel appears
to run through investment. Indeed, for a wide variety of countries, including EMEs, equity
prices seem to be better leading indicators of investment than GDP or consumption (Aylward
and Glen (2000)). However, linkages between asset prices and activity also depend on country-
and market-specific features (for reviews focusing on the predictive power of asset prices, see
Stock and Watson (2003) and Cochrane (2008)).




consumers, implying that their consumption is quite sensitive to transitory shocks to income. Mian et
al (2013) estimate the MPC out of housing wealth using US households data and find that more
leveraged and poorer households suffer higher losses in consumption in response to changes in housing
wealth. Some earlier studies also examine the interactions between house prices and the real economy
across countries (Borio et al (1994), Kennedy and Anderson (1994) and Tsatsaronis and Zhu (2004)).
16
   Research documents that house prices have a stronger impact on the consumption of older, less
indebted households (see Campbell and Cocco (2007), Calomiris et al (2013) and Attanasio et al
(2011)).


                                                 11
     House prices also display some predictive power for activity. There are several reasons
for this (see Leamer (2007)). Housing market developments are sensitive to the same
underlying factors affecting the overall economy (such as the level of interest rates and
aggregate demand). As the economy expands or contracts, housing demand will change. This
is highly relevant because the housing market is an important part of the overall economy.
The housing market also exhibits long lags and is lumpy: it takes considerable time to start
new housing projects, wealth effects associated with housing tend to operate with a lag and
buying or selling of houses often involves large transactions costs. Together, these factors
make various measures of housing sector activity, such as housing starts and prices, useful
leading economic indicators. Although the predictive power of house prices for output growth
is generally found to be somewhat weaker than that of equity prices, perhaps because housing
is traded in relatively less liquid markets, changes in house prices have a greater power than
equity prices in predicting future output gaps for some countries (IMF (2000)).
    Equity and house prices are not only related to the overall business cycle but are also
helpful in predicting cyclical turning points, albeit imperfectly. Recessions are often preceded
by declines in equity prices or slowdowns in their growth (with the opposite for recoveries).
For a large sample of countries for a period of almost 50 years, Claessens et al (2009) show
that in the first year of a typical recession, equity prices decline on a year-to-year basis by
roughly 35%. Anticipating the end of a recession, equity prices also often start registering
positive growth after about three quarters into a recession (Figure 1). House price cycles
generally lag business cycles, as reflected in the fact that house prices do not resume positive
annual growth until at least 12 quarters after a recession has started whereas equity prices
do after six to seven quarters.17

C. Challenges to the standard models
The linkages between asset prices and activity differ from the predictions of standard models
in a number of ways. First, asset prices are much more volatile than fundamentals would
imply. They can at times deviate substantially, or at least appear to do so, from the predicted
fundamentals-based values. Second, investment and consumption respond differently to asset
prices than what standard models would predict, with a larger role for non-price factors in
driving agents’ behaviour and macroeconomic aggregates. Third, there are limits to the
predictive ability of asset prices for real activity and the channels leading to the predictive
power may be quite different from what the basic models would suggest. This sub-section
discusses each of these issues in turn.

Asset pricing puzzles
Asset prices are more volatile than what fundamentals suggest. An extensive literature,
starting with Shiller (1979) for bond prices and Shiller (1981) for equity prices, has
documented the “excess” volatility of asset prices. Shiller (1981) observed that if equity prices
equal the expected sum of discounted dividends, then equity price volatility should face an
upper limit determined by the volatility of what he called “ex-post rational” stock prices
(defined as the sum of actual discounted dividends). However, he found that this was not
born by the data. Studies using different approaches have also confirmed this finding (see


17
   Bluedorn et al (2016) find that asset prices, especially equity prices, are helpful predictors of
recessions in the G7 countries. Borio and Drehmann (2009) document that house prices, combined
with credit, tend to predict financial crises.



                                                12
Cochrane (2011b) for a review). In subsequent research, Campbell and Shiller (1987) showed
that the excess volatility result remained even when the time series for prices and dividends
were non-stationary (see LeRoy (2008)). Research has also shown that many other asset prices
are much more volatile than the discounted value of the corresponding streams of dividend
would suggest.18
    Moreover, asset prices appear to move away at times from their predicted fundamentals-
based values. This is not easy to confirm, however, because one does not know whether the
mispricing represents a deviation from the “true” model or the use of a “mis-specified” model,
including not knowing or using the “right” fundamentals. This can happen for individual
assets (simply representing “arbitrage” opportunities) or for the market as a whole. Simple
arbitrage opportunities are limited for most traded assets but markets may deviate at times
from developments in fundamentals (Lo and MacKinlay (1999), Shiller (2000) and Akerlof
and Shiller (2009, 2015)).
    There is also ample evidence of stock price “bubbles”.19 As an illustration, one can
compare the aggregate price-earnings ratio, the dividend yield and the implied equity
premium in 1999, just before the stock market peak in advanced economies, with their
historical averages over the period 1980–99 (IMF (2000)). Such a comparison shows that, in
the late 1990s, the valuations implied by equity prices were considerably higher than their
historical averages in terms of price-earnings ratio but lower relative to dividend yields and
implied equity risk premia. At the same time, real GDP growth was not very different from
its historical average.20 These comparisons suggest some overvaluation at the time, with
markets indeed experiencing a major correction after mid-2000.
    The high volatility of asset prices relative to their fundamental values appears to stem
partly from the volatile nature of discount rates. Asset price volatility can be decomposed
into two parts: the volatility of expected future cash flows (eg dividends) and the volatility
of the discount rate applied to those cash flows. Campbell and Shiller (1988a, 1988b)
developed a methodology for decomposing the variation in the dividend-to-price ratio into
variations in expected dividends and discount rates. Their research and subsequent work
suggest that the variation in expected dividends accounts for no more than one-fourth of
stock market volatility whereas variation in the discount rate accounts for the bulk of
volatility (Cochrane (2011b) for a review).
    This relates to the finding that most asset pricing models, including the basic
consumption-based model, cannot fully explain the magnitude of the risk premium actually

18
   See Mankiw et al (1985, 1989), West (1988), Schwert (1989) and Barsky and De Long (1993).
19
   A bubble can be defined as: “...the part of a grossly upward asset price movement that is
unexplainable based on fundamentals” (Garber (2000)). Patterns of exuberant increases in asset prices,
often followed by crashes, figure prominently in many accounts of financial instability for all types of
economy and going back centuries (Claessens and Kose (2014)). See Brunnermeier and Oehmke (2013),
Scherbina and Schlusche (2014) and Williams (2013) for recent surveys on asset price bubbles. Some
argue that fully irrational asset bubbles are not necessarily harmful and could even be beneficial
(Kocherlakota (2009)).
20
   At the time, one explanation for the high level of stock prices was the strong labour productivity
growth observed in the United States in the second half of the 1990s. This fuelled discussion of a “new
economy” driven by information technology. However, later data revisions showed much lower labour
productivity growth. The changes in (perceived or expected) productivity growth could well explain
some of the run-up in prices and the following sharp corrections (Pastor and Veronesi (2006), Griffin
et al (2011)). Lettau et al (2008) provide a summary of studies explaining persistently high stock
market valuations, as observed in the late 1990s. They argue that a fall in macroeconomic risk or in
economy-wide volatility can lead to such high stock prices.


                                                  13
observed for equities – the spread between the rate of return required for holding the market
portfolio and the risk-free rate (for a discussion of the credit risk premium, see Amato and
Remolona (2013)). Using the CCAPM, Grossman and Shiller (1981) were the first to show
that the premium is much higher and more volatile over time than most plausible risk aversion
parameters would suggest. The phenomenon was also noted by Shiller (1982) and Modigliani
and Cohn (1979) but the literature truly emerged with the ground-breaking paper by Mehra
and Prescott (1985).
     Mehra and Prescott highlighted the difficulty that traditional models have in matching
the observed excess return of stocks relative to a risk-free asset (with the degree of risk
aversion viewed as realistic from a microeconomic perspective for a typical investor). The
literature on the (excessive) equity risk premium in discount rates is large but inconclusive.
The equity premium was subsequently refined by Epstein and Zin (1989) and Weil (1989),
who developed models allowing preferences for risk and intertemporal substitution to be
separated. Others explored the puzzle further, showing that one needs to distinguish between
the intertemporal rate of substitution and the rate of risk aversion when trying to interpret
the premium (see further Kocherlakota (1996), Mehra and Prescott (2003) and Rieger and
Wang (2012)).
    Higher asset price volatility can also arise if investors' risk aversion depends on
macroeconomic volatility, as is the case, for example, in asset pricing models with habit
formation (Campbell and Cochrane (1999)). Barro (2006) and Gabaix (2011) show that a
standard model extended to allow for realistically calibrated rare-disaster probabilities can
generate a high volatility of asset returns, high equity premia and low risk-free rates, which
are all close to what is observed in practice.21
    Apart from the overreaction of asset prices to swings in cash flows, the presence of various
anomalies and other indications of “mispricing” also constitute a source of puzzles (see
Schwert (2003) for a review). A number of possible answers to these puzzles have been
proposed. For example, in some models, the rational expectations assumption that investors
optimally use current information to forecast future dividend growth is relaxed. Instead,
various market failures and forms of behavioural biases are introduced, such as investors’
herding behaviour and sentiment (Barberis et al (1998)). Thaler (2005, 2015) presents a
survey of the related behavioural finance literature (see also Shleifer (2000), Barberis and
Thaler (2003), Barberis et al (2001) and Barberis (2013)).
    Indications that investors tend to overestimate the persistence of variations in dividend
growth – or, equivalently, to underprice risk – have motivated many studies (Barsky and De
Long (1993)). Numerous reasons related to the functioning of markets – including limited
market liquidity, “excessive” financial innovation, the perverse trading behaviour of large
investors and the role of hedge funds – have also been mentioned as causes of the excessive
volatility of asset prices (Bikhchandani and Sharma (2000)). Although this strand of research
provides analytical models and some empirical evidence showing that asset prices are not
simply determined by the present discounted value of future cash flows, it has not been able
to identify definitive reasons driving the deviations from the basic models (Duffie (2010)
reviews other examples of deviations).




21
  For additional information, see also Campbell et al (2013), Chen et al (2012), Ju and Miao (2012),
Albuquerque et al (2016) and Krueger et al (2016))


                                                14
Limits to the linkages between asset prices and activity
A number of empirical studies report that firm investment reacts less to asset prices than
what standard models predict. Research casts some doubt about the role of asset prices, in
general, and Tobin’s q, in particular, in explaining investment. Blanchard et al (1993), for
example, find a limited role for market valuation in explaining investment given fundamentals
and current profits (see also Stein (2003) and Butzen et al (2003)). Other research finds that
factors other than q or growth opportunities also drive investment (even though these may
also be correlated with q).22 Some studies suggest that, consistent with the presence of
financial frictions, an important channel operates through the quantity rather than the cost
(as reflected in q) of external financing.
    The impact of asset price volatility on investment and other macroeconomic aggregates
has been a fertile area of research. In theory, uncertainty associated with volatility has
ambiguous effects on investment. On one hand, as Abel (1983) argues, uncertainty can
increase the value of a marginal unit of capital and lead to more investment. On the other,
as Dixit and Pindyck (1994) suggest, volatility may create incentives to delay investment, as
more information about future payoffs becomes available over time, particularly given that
investment may be irreversible (Bernanke (1983)). Households’ response to high uncertainty
can be similar to that of firms; they reduce their consumption of durable goods as they wait
for uncertainty to abate. On the supply side, firms’ hiring plans are also negatively affected
by higher uncertainty because of the cost of adjusting personnel (Bentolila and Bertola
(1990)).
    Some recent empirical studies report that the macroeconomic uncertainty associated with
volatile asset prices tends to lead to a decline in output. Evidence based on VAR models
points to a significant negative impact of uncertainty shocks on output, investment and
employment (Bloom (2009, 2014), Hirata et al (2012), Nakamura et al (2017) and Kose et al
(2017b)). For example, a 1% increase in uncertainty is associated with a slightly larger than
1% decline in output in the first year (Figure 2). Using disaster data as instruments, Baker
and Bloom (2013) offer evidence that causality runs from uncertainty to recessions and Bloom
et al (2012) also report that (low) growth does not cause uncertainty. Predictions of
theoretical models and findings from empirical studies collectively indicate that uncertainty,
including that relating to asset prices, can play a dual role over the business cycle: it can be
an impulse and a propagation mechanism (Gilchrist et al (2014) and Kose and Terrones
(2012)).
    Studies of household behaviour also suggest many deviations from the predictions of the
standard models of consumption smoothing over the life cycle. Although the empirical
linkages between financial wealth and household consumption are less contested, numerous
puzzles remain. For example, household consumption depends as much on disposable income
as on lifetime wealth. This is in large part due to financial “imperfections”: households have
limited ability to borrow against future labour income, leading to liquidity constraints. Zeldes
(1989) presents a test of the permanent income hypothesis against the alternative hypothesis
that consumers optimise subject to borrowing constraints. He finds supportive evidence from
household data that an inability to borrow against future labour income affects the



22
  For evidence and reviews, see Fazzari et al (1988), Hubbard (1998) and Davis (2010b). Another
channel is between productivity and q (Gomes (2001) and Abel and Eberly (2011)). Cuthbertson and
Gasparro (1995) show that the average q alone is not a sufficient statistic but that output, capital
gearing and the average q jointly provide an adequate model for capturing aggregate manufacturing
investment behaviour in the United Kingdom (see also Bolton et al (2013)).


                                                15
consumption of a significant portion of the population (for reviews of this literature, see Caroll
(2001), Meghir (2004) and Jappelli and Pistaferri (2010)).
     Many studies also report that household consumption reacts more strongly to changes in
asset prices, especially house prices, than consumption-smoothing models predict. This finding
suggests that asset prices affect borrowing capacity, in large part because real estate can be
used as collateral, with the relationship again arising from financial frictions. The value of
housing does not represent net wealth for the aggregate household sector because an increase
in house value is also an increase in the implicit rental cost of housing (see Buiter (2010)).
Similar to other asset prices, the effects of changes in house prices on overall economic activity
must therefore either be due to distributional factors (eg households with different MPCs are
affected diversely by changes in house prices) or arise because of imperfections (such as limits
to collateralised borrowing that affect in turn current consumption) or due to various types
of bounded rationality, behavioral and informational issues.23
    The links between asset prices and investment also vary across countries. The relationship
between changes in equity prices and investment tends to be stronger in the United States
than in France and Germany while changes in property prices appear to have a closer
relationship to investment in continental Europe and Japan (IMF (2002)). Barrell and Davis
(2007b) show that equity price declines have an impact on US output that is three times
larger than on euro area output. This is consistent with a stronger role for equity finance for
firms in the United States than in other, more bank-based financial systems. It is also
consistent with the presence of financial frictions that vary by country and institutional
environment.24
    The reaction of investment and consumption to changes in asset prices also appears to
depend in part on legal regimes and traditions. Empirical analysis by Claessens et al (2014b)
suggests that the responses of investment to changes in q are faster in countries with better
corporate governance and information systems. They interpret this as evidence of fewer
financial frictions in such countries. The effects of house price changes on household
consumption can also depend on a country’s financial system and institutional environment
in ways that suggest the presence of certain financial frictions.

Limits to the predictive power of asset prices
There are limits to the predictive value of asset prices. Such limits appear to vary by type of
asset and financial system. The standard theory implies that asset prices should be good
proxies for expected growth (at both microeconomic and macroeconomic levels) because they
are forward-looking. Equity prices, however, with their low signal-to-noise ratio and their
(excess) volatility, do not have a good record of forecasting general economic developments.
While equity prices have some predictive ability for investment, they do not generally increase
the out-of-sample forecasting ability of GDP when compared with other economic variables
(see Aylward and Glen (2000)). This observation is succinctly described by the well-known



23
   For reviews of these issues, see Davis and Van Nieuwerburgh (2015), Iacoviello (2004) and Piazzesi
and Schneider (2016).
24
   Barrell and Davis (2005) analyse 13 European Union countries and the United States, and find a
stronger association between equity prices and output in market-based than in bank-based financial
systems. Using data for 16 OECD countries, Ludwig and Slok (2004) show that the long-run
responsiveness of consumption to permanent changes in stock prices is higher for market-based than
for bank-based systems.



                                                 16
saying that: “The equity market has forecasted nine of the past five recessions.”25 Indeed, the
leading indicator property of asset prices appears to be limited to certain classes of asset and
depends on the depth of markets. In their review, Stock and Watson (2003) conclude that:
“Some asset prices have been useful predictors of inflation and/or output growth in some
countries in some periods.” (page 822).26
     The nature and direction of causality is also unclear. Some interpret the linkages between
asset prices and future economic activity as evidence that financial markets correctly
anticipate future earnings growth and other fundamentals while others argue that asset prices
affect output because of some form of amplification mechanism, such as the “financial
accelerator”. In the first view, asset prices relate to current consumption and investment
decisions because they are leading indicators of changes in activity. This suggests, however,
no causal relationship and only an informational link between current prices and future
output. In the other view, changes in asset prices have an impact on access to finance, partly
because of frictions, and thereby influence current consumption and investment and thus help
predict GDP growth (see Estrella and Mishkin (1998), Stock and Watson (2003) and Diebold
et al (2006)).
    Both effects are likely to be at play but their relative importance is hard to disentangle.
The discussion above shows that linkages between asset price changes and output growth are
complex and that the exact direction and source of causality can be difficult to identify. Some
studies document a long-run and two-way causal linkage between stock market performance
and consumption, in which stock prices act, on the one hand, as leading indicators of
consumption, and, on the other hand, are explained by consumption and real economic
activity. More generally, though, the direction and source of causality between changes in
asset prices and activity are not entirely clear.



3. International dimensions of asset prices

Any discussion of the linkages between asset prices and macroeconomic outcomes has to take
into account the international dimensions of these linkages given the highly integrated nature
of the real economy and financial markets. Like their closed economy counterparts, many of
the international asset pricing models are based on partial equilibrium constructions that
often imply relatively weak linkages between real and financial variables. Moreover, a number
of puzzles remain with respect to the predictions that could be derived regarding the
international dimensions of asset prices. This section first presents a short review of the
theoretical approaches to asset price determination in open economy models. It then briefly

25
   One caveat is that most studies have only considered aggregate stock price indices. There is evidence
(Di Mauro et al (2011)) that considering information about the return and volatility of individual
equity prices, in addition to aggregate financial market information, can lead to significant
improvements in the forecasting of business cycle developments in major economic areas (eurozone,
Japan, the United Kingdom and the United States) at various horizons.
26
   One reason why equity prices, or asset prices more generally, could be weak predictors is that they
are themselves influenced by shocks that do not have a clear impact on real activity and by shocks
that do. If the only shock were a persistent total factor productivity shock, for example, then current
equity prices and future economic activity could be tightly linked. However, there are many other
types of shock and some affect real activity but not asset prices while some operate vice versa. This
means that simply observing asset prices is not sufficient to predict real activity. This does not
necessarily represent a failure of the underlying model. It could also stem from an incomplete
specification of the environment when conducting reduced-form data analysis.


                                                  17
examines the empirical evidence and concludes with a summary of the main challenges faced
by international asset pricing models.

A. Determination of asset prices in open economy models
Both domestic and international factors affect asset prices in open economy models. Early
models (Solnik (1974), Stulz (1981) and Adler and Dumas (1983)) extended the domestic
asset pricing models (mostly the CAPM) to an international context. These models suggest
that the determination of (relative) asset prices is based on a trade-off between exchange rate
risk and the diversification benefits of global investment, in addition to the domestic factors
discussed earlier. Accordingly, the required rate of return is derived from global benchmarks,
such as the correlations of domestic asset returns with those of world market portfolios.
     As is the case with many closed economy models, international asset pricing models tend
to be based on partial equilibrium frameworks (see Dumas (1994) for an early review).
Typically, cash flow processes are assumed to be predetermined and little attention is devoted
to whether changes in the domestic supply of securities and asset prices are consistent with
actual cross-border portfolio holdings. Uppal (1993) and Engel (1994) are early exceptions:
they develop general equilibrium models that take into account holdings of international
assets and liabilities in the determination of asset prices. Engel and Matsumoto (2009) revive
this class of models while Devereux and Sutherland (2009) extend them to dynamic settings
with incomplete asset markets. However, these general equilibrium models continue to face
difficulty in matching some of the basic statistical moments, such as variance and persistence
of asset prices, including exchange rates (see Coeurdacier and Rey (2013) for a review).
    A strand of the literature has focused on the implications of various barriers to cross-
border investment and of home bias, ie the tendency of investors to invest close to their base.
Although many models in this literature are simple extensions of the standard closed economy
setup – and assume perfectly integrated financial markets – some have devoted greater
attention to the effects of market segmentation, eg when some financial markets are only
accessible to resident investors or when no outward investment is allowed. These and other
types of (indirect) barrier, such as ownership restrictions, have been shown to alter the
determination of asset prices. This in turn leads to “deviations” from the predictions of
standard models since an identical asset can be priced differently in two different markets.27
Another branch of this research considers how changes over time and differences across
markets, including the degree of financial openness, can affect the determination of asset
prices and portfolio allocation and can lead to home bias (see Karolyi and Stulz (2003) and
Bekaert et al (2016) for reviews and Sa (2013) for recent evidence on bilateral financial
linkages).

B. Empirical evidence
Consistent with theoretical predictions, global factors play an increasingly important role in
determining asset prices. Cross-country correlations of asset prices are well documented,
especially the high and growing correlations between equity prices (Figure 3). Over the past
two decades, asset price movements (and output) have been explained increasingly by
common factors (Figure 4). Correlations have increased not only among advanced economies


27
  For a detailed discussion of such deviations, see Black (1974), Stulz (1981), Errunza and Losq (1985)
and Eun and Janakiramanan (1986).


                                                  18
and EMEs but also between these two groups of country (Ehrmann et al (2011), Rey (2015),
Passari and Rey (2015) and Miranda-Agrippino and Rey (2015)). This was to be expected.
Owing to technological advances and liberalisation, financial markets have become ever more
closely integrated and gross international financial flows have increased sharply. Evidence
shows that increases in comovements between assets are due to both de jure capital account
liberalisation (as stock return correlations and market betas increase after liberalisation) and
actual increases in international capital flows.28
     In addition to financial integration, financial development, the liquidity and depth of
equity markets and real economic integration (including trade intensity) have been shown to
affect the comovement of asset prices across countries. Forbes and Chinn (2004) and Beine
and Candelon (2011) show that bilateral financial and trade intensity drive stock market
synchronisation. Dellas and Hess (2005) show how the liquidity and depth of equity markets
can determine the synchronisation of equity returns. The adoption of a single currency (Walti
(2011)), lower real exchange rate volatility and asymmetry in output growth (Tavares (2009))
also increase correlations. In addition, there is an extensive literature on the importance of
financial linkages for international spillovers (see Diebold and Yilmaz (2009, 2015), Hirata et
al (2012) and Helbling et al (2011)).
    Prices of non-traded assets, such as houses, also tend to move together across countries.29
While there are limited fundamental linkages between housing markets – housing being the
quintessential non-traded good – house prices move together considerably across countries
and have become more synchronised over time (Figure 3). Hirata et al (2012) report that the
degree of concordance of housing cycles has increased from 51% during the period between
Q1 1971 and Q4 1984 to more than 63% during the period between Q1 1985 and Q3 2011.
The fraction of the variance of house prices explained by a global house price factor has
increased from about 20% to 35% over the two periods (Figure 4). In addition, downturns in
house prices tend to be synchronised across countries and overlap more frequently than
recessions do, especially so during the most recent cycle (Figure 5).
    As is the case for other asset prices, the comovement of house prices is driven by real and
financial global factors. Examples of global factors include the global business cycle,
commodity prices and a measure of the “world” rate of interest (Hirata et al (2012)). The
fact that house prices appear to be partly determined by current and past income growth
(and real interest rates or some other proxy for mortgage costs) is not surprising. As the
supply of land is fixed and that of residential dwellings and offices can only increase slowly,
property prices tend to be largely demand-determined in the very short run. Over the business
cycle, though, supply catches up with house prices, as prices and investment are driven by
similar factors. And, indeed, prior to the GFC, countries that experienced housing price booms
in 2000–06 also saw substantial growth in residential investment (Figure 6, upper panel).
However, this trend reversed over the 2007–08 period (Figure 6, lower panel).



28
   See Bekaert and Harvey (2000), Goetzmann et al (2005) and Quinn and Voth (2010). Conversely,
growing financial integration has reduced the cost of capital for firms in integrating countries. Chari
and Henry (2004) find that liberalisation reduces systematic risk, thereby lowering the cost of capital
for individual firms. The effects are quantitatively important since the covariance of the median
“investible” (ie with no barriers to ownership) firm’s stock return with the local market is 30 times
larger than its covariance with the world market. At the same time, increased financial integration
means that there might be smaller diversification gains for investors (Kose et al (2009) and Bekaert et
al (2016)).
29
   See BIS (1993), Borio et al (1994), Sutton (2002), Borio and McGuire (2004), Claessens et al (2011),
Cesa-Bianchi et al (2015), Harding and Pagan (2016) and Miles (2017).


                                                  19
    In addition, the common factor that captures cross-country house price movements is
related across advanced economies to mortgage-to-GDP ratios (reflecting the depth of
mortgage markets) and home ownership ratios (reflecting institutional structures and policies
aimed at fostering home ownership). The impact of global factors on house prices
consequently varies across individual countries, depending in part on the development of local
mortgage markets, income growth and structural and policy factors, such as tax and subsidies
(Terrones (2004)).
    In related research, Vansteenkiste and Hiebert (2011) show that spillovers from country-
specific house price shocks are relatively low in the euro area. Some studies document the
coincidence of global recessions with sharp downturns in global house and equity prices (Kose
and Terrones (2015); Figures 7A and 7B). Collectively, these findings underscore the
importance of the international dimensions of real and financial linkages in driving asset
prices. That said, some puzzles remain.

C. International dimensions of asset pricing puzzles
Similar to the domestic context, there are a number of puzzles relating to the international
dimensions of asset prices. For example, cross-country correlations of equity prices tend to be
higher than those implied by fundamentals. Similar to the weak link between equity prices
and firms’ fundamentals within a country, comovements in asset prices appear to not (just)
reflect the commonality of cash flow streams as would arise from synchronised business cycles.
This delinking is partially attributed to comovements in risk premia because investors in one
market are likely to be exposed to other markets as well, triggering common price
adjustments. Indeed, Engle and Susmel (1993) show that the high correlation of price
volatility across countries is related to the degree of international financial integration.
    Financial integration may also increase herding behaviour among investors, which can
then lead to more volatile capital flows and cause asset prices to move significantly in one
direction or the other, again amplifying correlations beyond what fundamentals-based models
would suggest. Goodhart (1999) argues that a key factor for the high correlations of second
moments is asymmetric and incomplete information. The high correlations observed across
asset prices and the large volatility of capital flows suggest that various other channels of
transmission are at play, including contagion. However, those channels require further study.30
    The so-called home bias – ie the limited extent of international portfolio diversification –
has been hard to reconcile with the predictions of most asset pricing models. Models that
include barriers to cross-border investment, both direct, such as capital controls and
ownership restrictions, and indirect, such as information asymmetries, are not very good at
explaining the limited degree of actual cross-border asset holdings (they tend to underestimate
home bias) and the behaviour of the rates of return (which are highly correlated).
Furthermore while restrictions on international capital flows may have been a viable
explanation for the home bias thirty years ago, they no longer do so today.
    With barriers diminishing over time, the bias should have fallen. In fact, until the late
1990s the home bias among advanced countries changed little.31 While there is some evidence


30
   For research on these channels and related work, see Dornbusch et al (2000), Karolyi (2004), Pritsker
(2011), Forbes (2012) and Cesa-Bianchi et al (forthcoming).
31
   See Lewis (1999, 2011), Karolyi and Stulz (2003) and Sercu and Vanpee (2007) for reviews of the
literature on home bias. Research points to the importance of indirect barriers, such as those related



                                                  20
of a decline in the bias in more recent years (see Sorensen et al (2007)), international holdings
still appear to be far below the levels determined by portfolio models, with correspondingly
lower risk-adjusted rates of return (see also Sercu and Vanpée (2007) and Kho et al (2009)
for evidence on the home bias in equity holdings).
     Related to the home bias, prices of internationally-traded assets continue to depend on
local risk factors. Firm-level equity prices, for example, depend significantly on domestic
equity risks factors, such as value, size and market returns, even when conditioned on global
equity counterparts (see Lewis (2011) for a review of global asset pricing models and the
associated empirical evidence). Similarly, bond prices depend more than expected on local
factors. These patterns suggest that there is still de facto segmentation, even though legal
and other formal barriers among equity markets have largely been removed, at least for
advanced countries. The predictable deviations from interest rate parity also suggest other
failures of the standard international asset pricing model. Factors other than formal barriers,
such as heterogeneous information and other asymmetries, may be behind these anomalies
(Engel (2014)). Coeurdacier and Rey (2013) present a review of models focusing on home bias
and its macroeconomic implications.



4. Exchange rates and macroeconomic outcomes

A rich research programme has analysed the determinants of exchange rate movements and
their implications for macroeconomic outcomes. However, providing models that can
satisfactorily explain the multiple linkages between macroeconomic and financial variables
while accounting for the role of the exchange rate as the relative price of both domestic goods
to foreign goods and domestic assets to foreign assets, has been a challenge. This section
provides an overview of the literature on the linkages between exchange rates and
macroeconomic outcomes. It begins by presenting a summary of the basic theoretical
mechanisms regarding the determination of exchange rates and their impact on
macroeconomic aggregates and financial variables. Next, it reviews empirical findings
regarding the linkages between real exchange rates and activity. It concludes with a discussion
of a number of puzzles regarding the dynamics of exchange rates and their relationships with
macroeconomic and financial variables.32




to differences in corporate governance, ownership structures and information asymmetries, which may
lead to home bias (see Stulz (2005)). These factors, however, would also suggest a reduction in home
bias as countries have tended to converge in these dimensions. Berriel and Bhattarai (2013) emphasise
the importance of government spending in explaining the home bias puzzle. For discussions of
international diversification, see Baxter and Jermann (1997), Coeurdacier and Guibaud (2011) and
Heathcote and Perri (2013, 2014).
32
   The discussion here touches upon the main puzzles only. We discuss the role of financial frictions in
Claessens and Kose (2017). For a discussion of abrupt movements in exchange rates associated with
financial crises, see Claessens and Kose (2014).



                                                  21
A. The basic mechanisms
Determinants of exchange rates
A large body of literature seeks to understand the determinants of exchange rates.33 The basic
building blocks used in this literature include two parity conditions: i) purchasing power
parity (PPP), which posits a relationship between exchange rates, on one hand, and local and
foreign goods prices, on the other; and ii) interest rate parity (covered or uncovered), which
stipulates the existence of arbitrage conditions between the exchange rate, on one side, and
the interest rates on domestic and foreign assets, on the other.
     To analyse exchange rate movements, early studies often employed extensions of closed
economy models. For example, the Mundell-Fleming model, set forth (independently) by
Mundell (1963) and Fleming (1962), is the open economy extension of the traditional IS-LM
model. The model shows that an economy cannot simultaneously maintain a fixed exchange
rate, free capital movement and an independent monetary policy (so called “the impossible
trinity”; see Obstfeld et al (2005) for a review). Under a flexible exchange rate regime, shocks
to money or goods markets can lead to capital flows through an equalisation of the local
interest rate with the global rate, resulting in changes to the exchange rate and trade flows.
Under a fixed exchange rate regime, the money supply adjusts to external or domestic shocks
affecting the balance-of-payments. The model thus shows in a simple way the roles played by
real and nominal variables in exchange rate determination.
    The exchange rate can also play an important role in the transmission of monetary policy
in small open economies. In early models, such as the monetary model of Frenkel and Johnson
(1978), the nominal exchange rate simply reflects the relative demand for and supply of money
in different countries. Changes in the quantity of domestic money then immediately affect
the exchange rate. In these and other models, a lowering of the policy rate leads to a decline
in the return on domestic assets relative to that on foreign assets. Consequently, the currency
depreciates, leading to expenditure switching and a rise in net exports and aggregate demand.
     The more recent literature, often under the rubric of “new open economy
macroeconomics”, incorporates advances in the domestic macroeconomic literature to help
explain the main features of exchange rate movements. This literature has employed dynamic
stochastic general equilibrium (DSGE) models to analyse the role played by nominal rigidities
in environments featuring imperfect competition and rational optimising agents. Much of this
literature is based on the canonical model developed by Obstfeld and Rogoff (1995a, 1995b).
Like earlier models, such models try to predict the dynamics of exchange rates, including the
possibility of overshooting. They are also used to analyse the dynamics of the current account,
(net) debt, the exchange rate and the impact of exchange rate uncertainty on international
transactions (see further Obstfeld and Rogoff (2000a)).34




33
   For more detailed discussions of the linkages between exchange rates and macroeconomic and
financial variables, see Obstfeld and Rogoff (1996), Mark (2001) and Sarno and Taylor (2003). Engel
(2014, 2016) present a discussion of the theoretical and empirical exchange rate literature, with a focus
on interest rate parity and other financial arbitrage conditions.
34
   See Corsetti (2008) for a review of DSGE models. See also Woodford (2010b) and Christiano et al
(2011a) for an open economy model incorporating financial frictions.



                                                   22
Exchange rates and activity
Many models examine the linkages between fluctuations in exchange rates and
macroeconomic fundamentals. When focused on the real side, such models detail how changes
in exchange rates endogenously relate to consumption, investment, exports and imports, both
in terms of volumes and prices. Research has also looked at how these relationships are
affected by a variety of factors, including the heterogeneity of economic sectors, economies of
scale, imperfect competition, type of exchange rate regime, country-specific elements and time
horizons (see Lane (2001) for a review).35
    In theory, some of the linkages between real exchange rates and macroeconomic outcomes
are ambiguous. For example, a depreciation can lead to an increase in investment as the
marginal profit from an additional unit of capital is likely to go up as future foreign sales rise.
However, the higher price of imported capital and inputs can reduce profits and, in turn,
lower investment. The overall impact of exchange rate changes on investment hinges then on
which of these forces dominates (see Landon and Smith (2009)).
     The theoretical literature on the effects of exchange rate devaluations on output is
similarly inconclusive. On the one hand, devaluation can lead to an increase in the production
of tradable goods and be associated with an expansion of output. On the other hand, it can
have a contractionary impact on the non-tradable goods sector and translate into a decline
in overall output. Related to these inconclusive finding are the financial effects of exchange
rate changes, including those operating through adjustments in balance sheets, which are
reviewed below. There is also an extensive theoretical literature looking at how linkages
between exchange rates and output can depend on exchange rate regimes (see Uribe (1997)
and Mendoza and Uribe (1996)).36

Exchange rates and financial variables
Most of the early models sidestepped financial variables or resorted to simplifying
assumptions, such as perfect financial markets. They only considered “real” environments,
focusing on the role of the (real) exchange rate as the price that cleared goods markets in
open economies. Although some models attempted to incorporate financial markets, this was
typically under restrictive assumptions. The standard uncovered interest rate parity (UIP)
condition, for example, requires at a minimum the joint presence of rational expectations and
risk neutrality, which are strong assumptions already. Early models rarely considered the role
of financial intermediation or market imperfections and assumed instead perfect financial
markets (ie perfect substitutability between assets and no financial frictions or defaults).37
While later models took into account the links existing between the exchange rate and other
asset prices, this was largely through arbitrage conditions, notably with short-term interest
rates linking the forward exchange rate to the current exchange rate and local and foreign
interest rates.


35
   See, among others, chapters in Volume 3 of the Handbook for International Economics (Grossman
and Rogoff (eds) (1995)) and Volume 4 (Gopinath et al (eds) (2014)).
36
   Dornbusch (1981) provides a review of the early literature on the macroeconomic implications of
devaluations and Marston (1995) presents a review of exchange rate policies in open economies.
37
   For example, some models (eg Stockman (1980) and Lucas (1982)) use cash in advance constraints,
but this is a short cut to introduce money rather than a proper model of financial intermediation.
Others (eg Branson and Henderson (1985)) use portfolio balance models to consider the trade-offs
involved in the holding of various assets but do not include financial market imperfections, such as
information asymmetries or principal-agent issues.



                                                23
     More recently, researchers have studied how the role of the exchange rate in
macroeconomic adjustment is affected by financial variables during “normal” times.38 In
particular, models have been developed to see how real linkages are impacted by capital flows
and stocks and balance sheets valuation effects. These models (Tille (2008), Benigno (2009),
Coeurdacier et al (2010) and Tille and van Wincoop (2014b)) allow for the exchange rate to
influence the adjustment of current and capital accounts through two financial channels: (i)
capital gains and losses on external assets and liabilities due to exchange rate movements;
and (ii) portfolio adjustments (trading in securities).
    These models still employ rather restrictive assumptions and continue to face difficulties
in explaining the behaviour of exchange rates. They often assume endowment economies,
perfect foresight and exogenously-determined initial asset positions. Moreover, the types of
asset considered are often limited and the possibility of investing in equity and bonds is ruled
out. Notably, the current account has little role to play in intertemporal dynamics. In many
models based on the framework of Obstfeld and Rogoff (1995a), the current account follows
basically a random walk (as does the economy’s net foreign asset position). These models are
essentially “modern versions” of the monetary model (of Frenkel and Johnson (1978), for
example) in that the exchange rate is determined by relative money supplies. Although more
recent general equilibrium models provide richer environments, their basic predictions do not
seem to square with the empirical evidence (Corsetti and Pesenti (2001) and Cavallo and
Ghironi (2002)). In particular, the forward risk premium has been hard to incorporate, except
in environments allowing unrealistically high rates of substitution and risk aversion (Obstfeld
(2008)).
    Recent classes of general equilibrium models with valuation effects and liquidity
preference appear more promising. Such models have been harder to calibrate but they obtain
simulated results that are more consistent with those of empirical studies. For example, the
model by Gourinchas and Rey (2007) allows for international financial adjustments to affect
the exchange rate. It highlights the role that valuation effects on the US net foreign asset
position might have had in relaxing the country’s external constraint.39
     Others study how capital gains and losses on external portfolios can affect exchange rate
and current account dynamics in general equilibrium settings (Devereux and Sutherland
(2010) and Pavlova and Rigobon (2012)). Some of these models allow for international equity
trading in a two-country DSGE model with production under monopolistic competition, and
separate asset prices and quantities to account for capital gains and losses and portfolio
adjustments (Ghironi et al (2015)). These models appear to deliver more realistic findings.
Limited participation models have also had some success in explaining the behaviour of
exchange rates. For example, Alvarez et al (2009) build a two-country model in which the
fraction of agents that participate in financial markets varies over time due to transaction
costs across assets. The exchange rate in their model is much more volatile than consumption
– something difficult to achieve in models with complete markets but more consistent with
real-world observations.
    Some recent models examine the impact of learning on the linkages between exchange
rates and fundamentals. The literature reviewed above clearly suggests that there is no


38
   The behaviour of exchange rates during financial crises and their role in macroeconomic adjustment
and activity is discussed in Claessens and Kose (2014).
39
   See Gourinchas and Rey (2014) and Coeurdacier and Rey (2013) for extensive reviews of such models.
Gabaix and Maggiori (2016) present a model in which exchange rates are driven by capital flows in
imperfect financial markets. For research on the effects of “ambiguity aversion” or “taste for
robustness” in the determination of exchange rates, see Ilut (2012) and Djeutem (2014).


                                                 24
consensus on a specific model that fully captures the relationships between exchange rates
and macroeconomic and financial variables (Ca’ Zorzi et al (2017) and Eichenbaum et al
(2017)). A reasonable assumption consequently is that agents do not know the true model or
at least do not know the true parameters linking exchange rates to economic and financial
fundamentals. Therefore, they need time to learn about the structure of the economy.
Bacchetta and van Wincoop (2004, 2006, 2013) and Bacchetta et al (2010) show how, if
structural parameters are unknown (or imperfectly known) and time-varying, market
participants can change the weight they give to certain fundamentals. Hence, their models
provide a framework that allows for a disconnect between observed macroeconomic
fundamentals and exchange rates in the short- to medium-run but still exhibit a close
relationship in the long-run (Hassan et al (2016)).

B. Empirical evidence
Tests of models of exchange rate determination
Although there is broad acceptance of the basic building blocks of exchange rate models,
there remains a large gap between the predictions of such models and empirical regularities.
Each successive generation of models has provided new insights into the determination of
exchange rates and empirical testing has much improved. But the more complete models that
have emerged have met with limited empirical success (Frankel and Rose (1995)). As Meese
and Rogoff (1983a, 1983b), initially, and Cheung et al (2005), later, have shown, models do
not necessarily work consistently well across time and different countries.
    The purchasing power parity (PPP) hypothesis provides a useful framework for thinking
about the relevance of exchange rate models. PPP was initially found to have little
explanatory value, especially when considering very short periods (Taylor and Taylor
(2004)).40 However, when considering longer time periods, the evidence relating to PPP is
more favourable (Flood and Taylor (1996)). Furthermore, although there may be little
response of the exchange rate to deviations from PPP when the exchange rate is close to
parity, even in the short- to medium-run, there can be a rapid response when the rate is far
away from it (see Sarno and Taylor (2002)). More generally, it has been found that there can
be non-linear dynamics in exchange rate adjustment, with “bands of inaction” around the
PPP rate and faster adjustment as the rate moves further away from the level consistent
with PPP (see further Taylor et al (2001) and Taylor and Taylor (2004)).
    There is also some empirical support for the monetary approach to exchange rate
determination. Early studies provided evidence for the flexible-price monetary model (Frenkel
(1976) and Balassa (1978)) but later studies were less supportive. Notably, Meese and Rogoff
(1983b) showed that monetary models fit poorly out of sample.41 In a related paper,
Eichenbaum and Evans (1995) showed how, in response to a monetary shock, expected
exchange rates displayed a hump-shaped pattern rather than the sharp depreciation implied
by the overshooting model of Dornbusch (1976). Mark and Sul (2001), using exchange rate

40
   Ghironi and Melitz (2005) also find this effect with a micro-founded model allowing for heterogeneous
firms and productivity shocks.
41
   Meese and Rogoff (1983b) and most of the subsequent literature use the root mean square error
(RMSE) as the main criterion for judging models. Since the RMSE values under- and over-predictions
equally, it is not necessarily a good yardstick for examining the profitability of trading strategies
because trading depends on the quality of the directional forecast (“buy or sell”). See further Elliott
and Ito (1999) and Abhyankar et al (2005).



                                                  25
data for 17 industrialised countries to implement a panel version of the test developed by
Mark (1995), found that monetary fundamentals outperform a random walk (as well as PPP
fundamentals) at short and long horizons. Rapach and Wohar (2002), using long historical
series for 14 advanced economies, documented some support for a simple form of the long-
run monetary model. Cerra and Saxena (2010) also provided evidence supporting the
monetary approach.42
     Some studies confirm the Balassa-Samuelson hypothesis of a link between productivity
and exchange rates, especially over the long run (Balassa (1964) and Samuelson (1964)).
However, the channels that generate this effect appear to be more complex than the
traditional view suggests (Chong et al (2012) and Bordo et al (2017)). In particular, while
higher labour productivity tends to lead to real exchange rate appreciation, which is consistent
with the traditional view that richer countries have stronger exchange rates, the productivity
effect is transmitted through relative prices between tradable goods rather than through the
relative prices of tradable and non-tradable goods (Lee and Tang (2007) and Ricci et al
(2013)). Other evidence regarding the effect of productivity on the real exchange rate is more
ambiguous. Chinn and Johnston (1999) and Fitzgerald (2003), for example, find little evidence
of a long-term relationship between real exchange rates and productivity differentials (see
further Froot and Rogoff (1995), Tica and Druzic (2006) and Bordo et al (2017)).
    The literature also considers the roles played by many other “fundamentals” in explaining
the (real) exchange rate. For example, several papers have studied the effects of fiscal
spending and deficits on the real exchange rate (Bouakez and Eyquem (2015) and Alves da
Silva et al (2015)). Following work by Monacelli and Perotti (2011), Ravn et al (2012) find
that increases in government spending can lead to a depreciation of the real exchange rate in
some advanced economies. However, this finding contradicts the predictions of many
traditional models (see Kim and Roubini (2008) for a review and estimates for the United
States).

Exchange rates, prices and activity
There is a vast empirical literature on the linkages between exchange rates, prices and
macroeconomic outcomes (including the current account and external adjustment), with the
major caveat that many of these relationships are endogenous and simultaneous. Moreover,
a number of empirical puzzles involving those linkages remain, as discussed further in the
next sub-section.
    Exchange rates and prices. Empirical evidence supports some of the basic channels
through which exchange rates affect export and import prices (Burstein and Gopinath
(2014)). Understanding the quantitative importance of linkages between exchange rates and
prices is an important step since these linkages are influential in shaping how fluctuations in
exchange rates may subsequently affect macroeconomic aggregates. While deviations from
the law of one price remain one of the most fundamental puzzles, exchange rates have been
found to have a quantitatively significant impact on both import and export prices, especially
in the long run. For example, in advanced countries about 64% of the change in exchange
rates is estimated to be transmitted to import prices after one year (IMF (2007) and Choudhri
and Hakura (2015)).


42
  The hump-shaped behaviour documented by Eichenbaum and Evans (1995) has been shown by
Steinsson (2008) to be consistent in the context of a two-country sticky-price business cycle model. See
Engel et al (2008) for a view suggesting that exchange rate models do not perform as poorly as
commonly thought.


                                                  26
     However, the extent of pass-through (ie the impact of exchange rate movements on prices)
varies over time and across countries (Forbes et al (2017)). Reflecting differences in market
size and sectoral composition of imports (the lack of which in earlier estimations was part of
the reason for the limited estimated impact on aggregate prices), pass-through coefficients
have been found to vary greatly across sectors: they are higher for commodities and lower for
highly differentiated manufacturing products (Amiti et al (2014) and Chen and Juvenal
(2016)). They also vary across countries. An important determinant of pass-though is the
currency of invoice, which is often the US dollar. The United States has a low pass-through
of 0.4, for example, while smaller, more open economies have coefficients estimated to be
closer to one (Gopinath (2015) and Casas et al (2017).43
    Exchange rates and macroeconomic aggregates. Exchange rates, real and nominal, are
much more volatile than output (see Burstein et al (2007)). For example, the real effective
exchange rate for advanced economies in the post-Bretton Woods era is on average more than
two times more volatile than output (Table 2A). At the same time, the contemporaneous
correlations (as well as the lead and lag relations) between real and nominal exchange rates
and output are very low for advanced economies (Table 2B).
     A large research programme analyses the direct linkages between movements in exchange
rates and macroeconomic aggregates (Cordella and Gupta (2015)). Most studies show that
currency depreciations (appreciations) are associated with a contraction (expansion) of
investment (see Landon and Smith (2009), Goldberg (1993) and Campa and Goldberg
(1999)). The strength of this relationship, however, varies across sectors, countries and time
horizon. Some other studies consider the impact of real exchange rate volatility on investment
and international trade (see Darby et al (1999), McKenzie (1999), Chowdhury (1993),
Caballero and Corbo (1989) and Grier and Smallwood (2013)). Others, such as Harchaoui et
al (2005), find ambiguous results of exchange rate volatility on investment, which is consistent
with theoretical models. Habib et al (2017) find that a real appreciation (depreciation) is
associated with significantly lower (higher) GDP growth but only for developing and
currency-pegging countries.
     Although standard models of international risk sharing with complete asset markets
predict a positive association between relative consumption growth and real exchange rate
depreciation, empirical studies investigating this relationship do not report conclusive results
(Backus and Smith (1993) and Obstfeld (2007)). One strand of the literature considers the
effects of exchange rate devaluations. In many cases, real depreciations are contractionary,
which is not consistent with the predictions of basic models for which a positive output effect
results from an increase in net exports (see Burstein et al (2005) and Kearns and Patel
(2016)). Other studies, though, suggest that any contractionary impact of devaluations tends
to disappear in the longer run (IMF (1999, 2005)).
    Exchange rates and the current account. Although the empirical linkages between
exchange rates, trade volumes and current accounts are weak in the short run, they reappear
in the longer run (IMF (2015)). Because of limited pass-through, low short-run elasticities


43
  This difference relates to the stronger domestic competition for imported goods in the United States
and the international use of the US dollar in the invoicing of exports and imports (Goldberg and Tille
(2008). Another factor here is the role of vertical specialisation. Chinn (2010) shows that, combined
with changing tariff rates and transportation costs, it can account for the high-income elasticities
typically found for trade. A number of studies analyse the extent of pass-through, see Campa and
Goldberg (2005), Hellerstein et al (2006), Thomas and Marquez (2009), Frankel et al (2010) and IMF
(2006). Campa and Goldberg (2005) also document that import prices in local currencies reflect 60%
of exchange rate fluctuations in the short run, with this fraction increasing to 80% in the long run.


                                                 27
and the presence of imported intermediate goods, the expenditure-switching effect of exchange
rate changes on trade volumes is muted in the short run (Engel (2010)). Fratzscher et al
(2010) show that shocks to the real exchange rate explain less than 7% of the movements in
the US trade balance.
    However, the impact of exchange rate changes on the current account materialises over
time. Many studies (eg McKinnon (1990)) test the empirical relevance of the well-known J-
curve effect, which describes how the current account worsens immediately after a
depreciation and improves only with a time lag. While studies often report mixed findings on
short-run effects (see Bahmani-Oskooee and Ratha (2004) for a review), the terms of trade
and the flows of exports and imports seem to relate to the real exchange rate in expected
ways in the longer run, although with a different quantitative impact across countries (Hooper
and Marquez (1995)).
    Exchange rates can play a supportive role in reversing current account imbalances, albeit
with a lag. Many studies argue, for example, that the large US current account deficit of the
2000s could not be reduced without a significant depreciation of the real exchange rate
(Blanchard et al (2005), Obstfeld and Rogoff (2000b, 2007) and Blanchard and Milesi-Ferretti
(2012)). The role of the exchange rate in facilitating external adjustment is reported for a
wide range of countries (Calvo (2005), IMF (2007), Gervais et al (2016) and Martin (2016)).44

Exchange rates and financial variables
As noted earlier, the exchange rate has been identified as one of the transmission channels
through which monetary policy affects the real economy. The potency of this channel depends
on three main factors. First, of course, the exchange rate regime matters in this relationship.45
Second, the sensitivity of the exchange rate to the interest rate appears to vary across models.
Early models found the sensitivity to be small, even though theoretical models that imposed
UIP suggested a large role for this channel (Boivin et al (2011)). More recent models, however,
explicitly acknowledge the tenuous and complex empirical links between monetary policy and
the exchange rate (see Walsh (2010) and Engel (2017) for reviews; and see also Bruno and
Shin (2015)).



44
   The quantitative importance of exchange rates in reducing (global) imbalances has been a hotly
debated issue (Blanchard and Milesi-Ferretti (2012) and Claessens et al (2010)). Exchange rates are
strongly related to capital flows and external financing during financial crises (Claessens and Kose
(2014)). Fluctuations in exchange rates can also have a strong impact on the allocation of resources in
small open economies, especially during crises (Calvo (2005)).
45
   Countries vary greatly in the exchange rate regime they pursue. Moreover, regimes can also change
over time with their choice mattering for macroeconomic developments, including for growth and
inflation. Aizenman et al (2011) find that, controlling for other factors, greater monetary independence
– as captured by greater exchange rate flexibility – is associated with lower output volatility while
exchange rate stability implies more output volatility. Ghosh et al (2010) show that pegged exchange
rate regimes tend to provide a useful nominal anchor and deliver lower inflation without compromising
growth. Floating rate regimes, however, are associated with a lower susceptibility to financial crises
and faster and smoother external adjustment than other regimes. Chinn and Wei (2013) question
whether a flexible regime facilitates current account adjustment. Similarly, Engel (2010) concludes that
exchange rate adjustment may have only a modest effect on current account imbalances in the short
run. Klein and Shambaugh (2010) also analyse exchange rate regimes and Rose (2011) surveys the
literature on the incidence, causes and consequences of a country’s choice of exchange rate regime (see
also Gagnon (2011)).



                                                  28
     Third, as one would expect, this channel is more pronounced for small open economies.
Devereux et al (2006) illustrate that the effectiveness of this channel depends on the degree
of exchange rate pass-through. Indeed, for small open EMEs, especially those targeting
inflation, the exchange rate appears in practice to play an important role in monetary policy
frameworks, much more so than is the case for advanced economies (Mishkin (2008)). This
appears to reflect, among others, concerns about second-round effects, notably of exchange
rate depreciations on inflation expectations.
    Research supports the expected links between exchange rates and interest rates but with
some caveats. Until the GFC, covered interest rate parity (CIP) was the norm in normal
times as few arbitrage opportunities emerged. Akram et al (2008), for example, showed that
deviations dissipated in a matter of minutes. However, the GFC was a notable exception.
Heightened counterparty risk (and other risks) led to significant deviations from CIP (see
Baba et al (2008), Baba and Packer (2009), Coffey et al (2009) and Griffoli and Ranaldo
(2010)). Since then, deviations have declined but not disappeared.46
     Evidence also suggests that, while there can be substantial deviations from UIP in the
short-to-medium term (leading to “carry-trade”, as discussed below), local interest rates are
affected by global rates in the longer run, especially for countries with fixed or pegged
exchange rates (see Engel (1986) for an earlier survey).47 Chinn and Meredith (2004) show
that the UIP hypothesis obtains empirical support when interest rates on longer-maturity
bonds of G7 countries are used, which is consistent with models where “fundamentals” drive
exchange rates over longer periods. Chinn (2006), using data for major advanced economies
and EMEs, shows that the evidence against UIP in the current floating rate era is not as
strong as is commonly thought (see also Ismailov and Rossi (2017)).48 Based on a new measure
of sovereign credit risk, “the local currency credit spread”, defined as the spread of local
currency bonds over a synthetic local currency risk-free rate based on cross-currency swaps,
Du and Schreger (2016) find that local currency credit spreads are positive and sizeable.
However, they are lower than credit spreads on foreign currency-denominated debt as well as
less correlated across countries and less sensitive to global risk factors.
    Fluctuations in equity and other asset prices have also been found to relate to exchange
rates. Individual firms are affected by exposures to exchange rates in expected ways, with
inter alia firm size, multinational status, foreign sales, international assets and
competitiveness found to matter (see Dominguez and Tesar (2006)). Moreover, the direction
of exposure depends on the evolution of the exchange rate vis-à-vis other countries as firms
dynamically adjust their operational behaviour in response to exchange rate risk. With the
usual caveats about endogeneity and causality, aggregate and individual stock prices in


46
    See, among others, Borio et al (2016), Sushko et al (2016), Avdjiev et al (2017), Du et al (2017),
Rime et al (2017) and papers presented at the conference “CIP – RIP?” and Levich (2017) for a review
of the general literature on CIP deviations.
47
   Flood and Rose (2002) found that UIP worked better on average in the 1990s than in previous eras
as the slope coefficient from a regression of exchange rate changes on interest rate differentials was
positive. Moreover, UIP worked systematically better for fixed and flexible exchange rate countries,
less so for countries experiencing financial crises. And there was no statistically significantly difference
between rich and poor countries.
48
   In general, the relationship between interest rates and exchange rates can be complex. For example,
Hnatkovska et al (2013) argue that higher interest rates have three distinct effects: raise the fiscal
burden, reduce output (due to a higher cost of capital) and raise the demand for domestic currency
assets. The first two effects act to depreciate the currency while the last one tend to appreciate it. The
net effect depends on the relative strength of these opposing forces.



                                                    29
advanced economies as well as in many EMEs have also been found to be affected by exchange
rates in ways that were expected (Phylaktis and Ravazzolo (2005), Jorion (1990, 1991) and
Cenedese et al (2016)).

C. Exchange rate puzzles
There are a number of puzzles associated with the behaviour of exchange rates. Indeed, some
of the six puzzles of international macroeconomics identified by Obstfeld and Rogoff (2000c)
are intimately related to exchange rates: McCallum's home bias in trade puzzle; the Feldstein-
Horioka saving-investment puzzle; the French-Poterba equity home bias puzzle; the Backus-
Kehoe-Kydland consumption correlation puzzle; the PPP puzzle; and what they call the
exchange rate disconnect puzzle.49
    The key puzzle is the disconnect between exchange rate movements and macroeconomic
aggregates. This is reflected in the limited success of models relating exchange rates to
underlying short-run fundamentals. As highlighted by Obstfeld and Rogoff (2000c), this
disconnect can be considered as an umbrella of puzzles that all refer to “the remarkably weak
short-term feedback links between the exchange rate and the rest of the economy.”
     Some of these “disconnect puzzles” are closely related. By explicitly introducing costs in
international trade (including transport costs, tariffs, non-tariff barriers and other trade
costs), Obstfeld and Rogoff (2000c) argue that they can explain several puzzles, including the
PPP puzzle and the exchange rate disconnect puzzle. Some of the puzzles also relate to the
widespread use of linear models in empirical exchange rate economics, which leaves no room
for transaction costs.50 Engel (2011) points out how another set of puzzles comes into play:
any successful model of the exchange rate must simultaneously explain why high interest rate
currencies tend to earn excess returns (the forward premium puzzle) and why high real
interest rate currencies tend to be stronger than what their fundamental values would imply
(the discounted rationally expected future real interest differentials). The joint observation
of these puzzles means, in turn, that if there is an exchange risk premium, it should tend to
shrink as real interest rates rise.

Exchange rates and fundamentals
Many exchange rate puzzles mimic those reported in the literature on other asset prices in
that the ability of models to explain and predict exchange rates using fundamentals remains
limited. First, exchange rates can be modelled as the present value of expected fundamentals
(Frenkel (1981)). Relative to fundamentals, however, exchange rates appear to exhibit much
higher volatility. This is similar to the excess volatility of stock prices relative to underlying
dividend streams (Shiller (1981)), raising the puzzle of “excess volatility” (see Baxter and
Stockman (1989) and Flood and Rose (1995)).51 Second, macroeconomic and financial news



49
   The latter includes both the Meese-Rogoff exchange rate forecasting puzzle and the Baxter-Stockman
neutrality of exchange rate regime puzzle. See Engel and Zhu (2017) and Eaton et al (2016) for recent
reviews and empirical work on the major puzzles.
50
   The incorporation of transaction costs creates an intrinsically non-linear relationship. This means
that in the presence of such costs, the estimation of linear models is inappropriate. A true empirical
test of the validity of the hypothesis must be based on non-linear models.
51
   See Killeen et al (2006), Hau (1998) and Jeanne and Rose (2002) for discussions on the sources of
excess volatility in exchange rates.



                                                 30
appear to affect exchange rates “too much”, which is also similar to how bond and other asset
prices overreact to news.52
     Importantly, the forward exchange rate is not an unbiased predictor of the future
exchange rate. Much work has rejected the speculative efficiency hypothesis (which posits
that the forward rate is the expected spot rate without a risk premium). Rejection can be
due to a departure from rationality and/or to risk premia (Froot and Frankel (1989)).
Empirically, the recent literature is converging towards the view that the forward bias and
the resulting profitability of carry trades are driven by a foreign exchange risk premium that
is non-zero on average and varies over time according to global factors (Menkhoff et al (2012),
Lustig et al (2011) and Burnside et al (2011b) review the literature on carry trade). This is
akin to the presence of an equity premium that appears to be excessively high for most asset
pricing models (see Fama (1984), Mehra and Prescott (1985) and the review by Mehra and
Prescott (2003)).
    One of the enduring puzzles involves the difficulty of forecasting exchange rates out of
sample. Meese and Rogoff (1983b) were the first to show that asset market-based models do
not outperform a simple random walk in predicting exchange rates. Although there is some
evidence that models perform better than a random walk at longer horizons (Mark (1995)),
in part due to the changing weight of fundamentals, the success of such models remains
limited for predictive purposes (Sarno and Valente (2009)).53 Cheung et al (2017) assess the
success of exchange rate predictions using a wide variety of models (interest rate parity,
productivity-based, a composite specification, PPP and the sticky-price monetary model) and
find that a model that works well in one period may not necessarily work well in another, or
for all countries or all horizons.54 This suggests that, while each model has merits, none is
able to capture completely the determinants of exchange rates.55



52
   For recent studies on excess sensitivity to news, see Jaimovich and Rebelo (2008), Bacchetta and
van Wincoop (2006) and Ehrmann and Fratzscher (2005). For early evidence, see Goodhart (1999)
and Goodhart and Figliuoli (1991). For an examination of the impact of news in explaining the
relationship between exchange rates and consumption using general equilibrium models, see Lambrias
(2016), Opazo (2006) and Nam and Wang (2010).
53
   Three exchange rate models often used in practice are: i) the macroeconomic balance approach,
which builds on Obstfeld and Rogoff (1996) and focuses on flows (ie current account equilibrium) over
the medium term; ii) the equilibrium real exchange rate (ERER) approach, which looks for consistency
of the real effective exchange rate (REER) with trend fundamentals (including the stock of net foreign
assets (NFAs)) over the medium term (see Rogoff (1996) for a survey); iii) and the external
sustainability approach, which checks for stock-flow consistency and budget constraint (see Lee et al
(2008) for the application of these types of model and their forecasting power).
54
   Sarno and Valente (2009) report that: (i) the weak out-of-sample predictive ability of exchange rate
models is caused by a poor performance of model selection criteria rather than a lack of information
content of the fundamentals; and that (ii) the difficulty of selecting the best predictive model is largely
due to frequent shifts in the set of fundamentals driving exchange rates, including swings in market
expectations (see also Della Corte et al (2016a) and Menkhoff et al (2017)).
55
   Rossi (2013) provides a comprehensive review of the literature on the predictability of exchange rates
and concludes that: “Overall, our analysis of the literature and the data suggests that the answer to
the question: "Are exchange rates predictable?" is, "It depends" on the choice of predictor, forecast
horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when
one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is
linear, and a small number of parameters are estimated. The toughest benchmark is the random walk
without drift.”



                                                    31
    Some studies show, though, that exchange rates and fundamentals are connected in a
way that is broadly consistent with asset pricing models. As Frankel and Meese (1987) noted
early on, empirical tests of the “excess volatility” of exchange rates are hard to implement.
In a world with sticky prices (Dornbusch (1976)), for example, the exchange rate can be
volatile because of overshooting but this does not necessarily imply excess volatility relative
to what the fundamental determinants of exchange rates would predict. Engel and West
(2005) show that if fundamentals are integrated of “factor one” and the factor for discounting
future fundamentals is near one, then the exchange rate exhibits a behaviour that
approximates a random walk. Sarno and Sojli (2009) empirically confirm the assumption of
near unity of the discount factor. The results by Engel and West (2005) thus help explain
the exchange rate disconnect puzzle since they imply that fundamental variables (such as
relative money supplies, output, inflation and interest rates) offer little help in explaining
changes in exchange rates.
    Conversely, exchange rate movements can help predict changes in fundamentals.
Standard present-value models suggest that exchange rates are driven by expected
fundamentals. Sarno and Schmeling (2014) test the implication that exchange rates contain
information about future fundamentals. Employing a variety of tests in a sample of 35
currency pairs ranging from 1900 to 2009, they find that exchange rates have strong and
significant predictive power for nominal fundamentals (inflation, money balances and nominal
GDP). They also find that the predictability of real fundamentals and risk premia is much
weaker and largely confined to the post-Bretton Woods era.56

Exchange rates and financial factors
Some studies have had partial success in incorporating financial variables into exchange rate
models. Since financial conditions play a significant role in affecting expectations, they have
been found to be helpful in predicting exchange rates, even though the underlying mechanisms
are not entirely clear. For instance, recent research reports that out-of-sample exchange rate
forecasting can be improved by incorporating monetary policy reaction functions (Taylor
rules) into standard models.57
     Combining monetary fundamentals and policy reaction functions with yield curve factors
reflecting expectations and risk premia further helps to explain exchange rate movements and
excess currency returns one month to two years ahead, outperforming the random walk (Chen
and Tsang (2013)). Conversely, as Engel and West (2005) show, exchange rates are useful in
forecasting future monetary policy, consistent with the idea that they reflect market
expectations of policy. These findings suggest that excess currency returns reflect both real
(business cycle) and financial factors.




56
   A related study is Chen et al (2010) who show that the exchange rates of commodity-exporting
countries can help predict commodity prices but conversely that commodity prices do not help predict
exchange rates (Ferraro et al (2015)). They attribute this asymmetry to the forward-looking nature of
exchange rates. Hassan (2013) shows that a large fraction of currency returns is explained by differences
in the size of economies.
57
   For example, Molodtsova et al (2008) and Molodtsova and Papell (2009) find that incorporating
Taylor rule variables improves short-term predictability more than conventional interest rate,
purchasing power parity or monetary models. See also Benigno (2004), Engel and West (2006), Mark
(2009), Corsetti et al (2011) and Engel et al (2010) for models and empirical evidence relating to
monetary policy rules that can, among others, generate some of the observed persistence in real
exchange rates.


                                                   32
    The literature has also established linkages between movements in exchange rates and
order flows in foreign exchange markets. In the very short run, order flow – the volume of
buy and sell requests and the related willingness of dealers to trade at certain prices – affects
exchange rate behaviour over periods varying from minutes to a couple of months (see Lyons
(1995, 2001) and Sarno and Taylor (2002) for reviews). This link seems to reflect in part the
micro market structure of trading as well as the information gleaned by traders from the
positions of other market participants. It also appears to be related to the information
contained by order flows about the underlying macroeconomic factors and parameters of
exchange rate processes.
     In particular, traders respond to economic news in deciding what currencies to buy and
sell, and that order flow is a powerful predictor of future exchange rates (Breedon et al (2016)
and Menkhoff et al (2016)). Rime et al (2010) argue that taken together the two results imply
that economic variables are indeed linked to exchange rates but that the link is likely to be
partly indirect in the sense that it is established via the trading decisions of dealers rather
than via the macroeconomic channel posited by standard rational expectations models. On a
related note, Evans (2010) presents evidence that order flow information reaching dealers
provides signals concerning the slowly evolving state of the macroeconomy (see also Evans
(2011) for a review of this and the associated literature).
    Another possible channel is that the order flow provides information about the (true or
perceived) parameters of the exchange rate process – information, which, in turn, affects
exchange rate behaviour. Consistent with this hypothesis, Chinn and Moore (2011) show that
combining a standard monetary model with order flow information can improve out-of-sample
exchange rate forecasting. Furthermore, Fratzscher et al (2015) find that a large fraction of
the variation and directional change in exchange rates can be explained by a combination of
order flow and survey data on the relative importance attributed by traders to various
fundamental factors. This supports the “scapegoat” theory of exchange rates (Bacchetta and
van Wincoop (2004), Bacchetta et al (2010) and Tille and van Wincoop (2014a)).
     Exchange rate behaviour could also be linked to the level of financial development and
to global saving-investment dynamics. Caballero et al (2008) argue that the lack of well-
developed financial systems leads developing economies to run persistent current account
surpluses with countries that can generate “sound” or liquid financial assets (such as the
United States). In addition, they show that shifts in growth rates, in the presence of home
bias in consumption and portfolio holdings, can lead to changes in exchange rates. In their
model, the exchange rate moves in response to financial shocks rather than to the current
account balance. Using this model, they explain how the GFC exacerbated the shortage of
liquid assets and induced a rush to dollar assets (see also Della Corte et al (2016b)).
     There is also evidence suggesting that the safe haven function of some currencies allows
them to enjoy a privileged cost of capital and liquidity. The United States, for example,
appears to pay less on its external liabilities than it earns on comparable external assets,
correcting for exchange rate movements (Gourinchas et al (2010)). The relatively sharp
appreciation of the US dollar following the GFC appears to be related to this role.58 Curcuru
et al (2013), however, find that the hypothesis of an exorbitant privilege – insofar as portfolio
claims are concerned – suffers from a number of weaknesses, including measurement problems
and statistical insignificance, and can largely be explained by differences in the relative

58
  Gourinchas and Rey (2014) review the literature on the so-called “exorbitant privilege” enjoyed by
the United States (see also Gorton (2017) for a review of the general literature on safe assets and
Cohen et al (2017) for a review of the literature on global liquidity). McCauley and McGuire (2009)
for their part focus on bank behaviour in the context of the US dollar’s appreciation after the GFC.


                                                33
composition of asset holdings between US residents and non-residents (see Rogoff and Tashiro
(2015) for a discussion of the safe haven privilege in the context of Japan).
    Another phenomenon that needs more work is how a shortage of dollar liquidity in Europe
and other markets seems to have created upward pressure on the dollar during the GFC
(Engel and West (2010)). Why the safe haven and liquidity roles of a currency arise and why
they do not apply equally to various major currencies (eg the euro) is still unclear. One
possible reason is that lender of last resort facilities have traditionally been limited to local
commercial banks and have not been available to foreign banks in other markets. Since
lending in dollars is large outside the United States, including in EMEs with extensive liability
dollarisation, in times of stress there is a large demand for US dollars (Obstfeld (2004), Engel
and West (2010) and Rajan and Tokatlidis (2005)).
    Many studies find evidence of the importance of balance sheet variables and related
valuation effects (Gourinchas and Rey (2014) review the literature). Balance sheet and
valuation effects appear to be important in driving exchange rates and, in turn, real variables
(as first formally documented by Gourinchas and Rey (2007), followed by Lane and Milesi-
Ferretti (2009)). Gourinchas and Rey (2007) find that the effects of exchange rate changes
on financial variables have contributed to about 30% of US external adjustment since the
1950s. This is not to deny that exchange rates affect real variables, net exports in the case of
their work, thereby aiding adjustment; rather, this financial adjustment channel results in a
high degree of predictability of the exchange rate over a two- to four-year horizon.59
    The results of the model developed by Gourinchas and Rey have been examined in various
studies. Using aggregate data, Alquist and Chinn (2008) compare the relative predictive
power of the sticky-price monetary model, UIP and the Gourinchas and Rey model and find
some support for the latter model but only at short horizons for bilateral exchange rates.
Moreover, they find that no model outperforms a random walk. The Gourinchas and Rey
model also predicts that cyclical external imbalances in the United States are linked to future
movements in the dollar. Della Corte et al (2012) test this prediction and find a negative
correlation between nominal exchange rate returns and lagged measures of bilateral external
imbalances. Specifically, using exchange rates, data on valuation effects and the ratio of net
exports to net foreign assets, they show that a model using cyclical external imbalances
provides substantial economic value to a risk-averse investor when compared to a random
walk.
    The carry trade puzzle illustrates that the literature is still struggling to integrate a
number of financial factors. One can clearly exploit interest differentials in the short run, as
exchange rates do not satisfy UIP. The carry trade, however, appears to be a more persistent
phenomenon – even though risk-adjusted UIP should preclude this over the longer term.60
Carry trade is likely to be behind some of the increase in cross-border holdings of assets
although this is hard to confirm given data limitations (see Galati et al (2007)). Since the


59
   Despite its empirical importance, the source of this financial adjustment channel effect remains
unclear. Gourinchas and Rey (2007) argue that the effect is consistent with a home bias in asset
holdings.
60
   For evidence, see Jordà and Taylor (2012) and Brunnermeier et al (2009). Clarida et al (2009) show
that the forward rate bias disappears during periods of high volatility. Burnside et al (2011a) show
that rare disasters (or “peso” problems) can be significant in explaining returns on carry trades. Hassan
and Mano (2014) show that carry trade has little to do with the forward premium puzzle: carry trade
exploits persistent differences in interest rate differentials across currencies while the premium puzzle
seems to be driven by the interest rate movements of all currencies against the dollar.



                                                   34
profitability of carry trade is the flip side of the forward premium bias coin, any explanation
must be consistent with risk premium patterns (Engel (2011) and Cenedese et al (2014)).61
     One such possible risk premium is a reward for assuming global foreign exchange volatility
risk. Indeed, Menkhoff et al (2012) show that a global premium can explain more than 90%
of cross-sectional excess returns on five carry trade currencies with high interest rates. While
liquidity risk also matters, volatility risk appears to be more important.62 They document
that there is a clear connection between global foreign exchange volatility “innovations” and
portfolio returns on carry trades. They also report that when volatility innovations are high,
carry trades perform poorly, implying that low interest rate currencies perform better than
high interest rate currencies.



5. Interest rates and macroeconomic outcomes

This section surveys the interactions between interest rates and economic activity. It begins
with a summary of the basic mechanisms that relate changes in interest rates to fluctuations
in output in standard models. Next, it reviews the empirical evidence supporting these
mechanisms. It concludes with a brief discussion of arguments challenging the mechanisms.

A. Basic mechanisms
The main channel of monetary policy transmission is the so-called interest rate channel.
Conceptually, by adjusting the policy rate, such as the fed funds rate in the United States,
the central bank affects the nominal short-term rate at which banks and financial
intermediaries borrow.63 A change in nominal interest rates alters the real interest rate given
some degree of price stickiness, ie the price level does not adjust fully in the short run.
    When the short-term interest rate changes because of monetary policy decisions, long-
term interest rates can also be affected. Long-term interest rates are directly linked to short-
term rates by expectations and arbitrage relationships, at least in the standard models.
However, the degree to which a central bank can affect long-term rates depends, among
others, on the monetary policy regime, the credibility of the central bank, the structure of




61
   One “fundamentals-based” explanation could be the presence of disaster risk. Farhi et al (2009) show
that, for a large set of advanced economies over the 1996 –2008 period, disaster risk premia accounted
for about 25% of excess returns on carry trades (see also Farhi and Gabaix (2016)).
62
   Adrian et al (2015) provide evidence that the funding liquidity aggregates of US financial
intermediaries forecast dollar exchange rate returns – at weekly, monthly and quarterly horizons –
both in-sample and out-of-sample and against a large set of currencies. They attribute the association
to time-varying risk premia.
63
   See Mishkin (1995), Smets (1995), Borio (1997) and Boivin et al (2011) for reviews of monetary
policy transmission channels. Our presentation is a highly stylised summary of how the direct interest
rate channel of monetary policy operates. In reality, in the case of the United States, the fed funds
rate, which is the interest rate prevailing in the overnight interbank market, is not controlled by the
Federal Reserve, ie it is not a set rate. Instead, the Federal Reserve sets a target for that rate and tries
to ensure that the actual rate remains close to the target by buying and selling securities in the open
market. Similar mechanisms operate in other countries. For a detailed review, see Woodford (2003).



                                                    35
financial markets and various external factors (Walsh (2010), Duffee (2013) and Vavra
(2014)).64
    The real interest rate affects the user cost of capital and thereby economic activity.
Standard neoclassical models of investment, such as Tobin's q model (reviewed above), imply
that the user cost of capital is one of the factors that determine the demand for investment
and durable goods, including housing and consumer durables. In response to changes in the
real cost of capital, corporations adjust their decisions with respect to production and
investment. A decline in interest rates, for example, leads to a rise in investment as the user
cost of capital falls relative to the return on investment. Interest rates also affect household
spending on durable goods and saving and investment decisions. In turn, through these
mechanisms, real activity responds to changes in interest rates.
    Such mechanisms are found in a wide variety of models, including the textbook IS-LM
model and the new Keynesian (NK) models. The latter group of models starts from the
standard real business cycle (RBC) framework but adds monopolistic competition in the goods
market and rigidities in nominal price-setting. Christiano et al (2005) and Smets and Wouters
(2007) provide NK models that include features, such as a sluggish response of prices and a
large and delayed response of real variables. Similar mechanisms are also at play in recent
DSGE models (see Blanchard (2009), Walsh (2010) and Christiano et al (2011b) for reviews).
In many of these models, however, financial intermediation is largely irrelevant because there
are no financial frictions. This means, in turn, that important channels by which interest rate
changes could affect the real economy are left out. While this deficiency has been widely
acknowledged following the GFC (see Hall (2010), Woodford (2010a), Ohanian (2010),
Caballero (2010) and Blanchard (2017b)), progress with modelling has been slow (see further
Claessens and Kose (2017)).
    Recent developments, notably the UMPs under which interest rates were brought at or
near the zero lower bound (ZLB) in many advanced economies have raised many questions
because the standard transmission channels are no longer effective. At the ZLB, a central
bank loses its conventional policy instrument, the short-term rate (Woodford (2012b), IMF
(2013), Borio and Zabai (2016), Farmer and Zabczyk (2016), Gourinchas and Rey (2016) and
Rogoff (2017)). It can then try to target real long-term yields and inflation expectations
directly through forward guidance and purchases of government bonds and other assets.
     Forward guidance can convince markets that rates will remain low for longer than what
is consistent with the usual policy rule.65 If successful, this can impart downward pressure on
expected nominal and real rates (ie flatten the yield curve) and stimulate current spending.
Since forward guidance poses a time consistency problem, in order to be effective it needs to
be employed by a central bank with a solid reputation. Given the extraordinary nature of
these policies, however, more research is required to gain a better understanding of their
effects on the real economy.66



64
   This discussion, as most text-books do, ignores the possibility of default on domestic public debt,
which is not uncommon (see Reinhart and Rogoff (2011) and Claessens and Kose (2014)).
65
   See Eggertsson and Woodford (2003), Gertler and Karadi (2011), Del Negro et al (2017) and McKay
et al (2016).
66
   Pre-announced thresholds for the timing and pace of the interest rate “lift-off” from the ZLB and
purchases of long-term assets are thought to help enhancing the credibility of this policy. Other
solutions have been proposed involving policy rules, such as price-level or nominal GDP-level targeting
that allow for temporarily higher inflation. Woodford (2012a) reviews this literature with a focus on
the implications for the conduct of US monetary policy (see also Gust et al (2017)).


                                                  36
B. Empirical evidence
Short- and long-term interest rates, both nominal and real, display considerable variation in
advanced economies (Table 3A). While, as expected, nominal long-term rates have been less
volatile than short-term rates, real long-term rates have been as volatile as short-term rates,
at least in advanced economies. That said, the volatility of interest rates has declined since
the mid-1980s. Consistent with the expectation hypothesis, evidence indicates that monetary
policy shocks affect, albeit not necessarily at all times, the whole yield curve (eg Estrella and
Hardouvelis (1991) and Evans and Marshall (2007) for the United States and Estrella and
Mishkin (1997) for a panel of European economies).67
    Many studies provide evidence that is consistent with the theoretical mechanisms
described above. Early work found large effects of short-term interest rate movements on
output through changes in consumption and investment (see Taylor (1995) for a summary).
Romer and Romer (1994) document that changes in the fed funds rate are negatively
correlated with US output fluctuations. Christiano et al (1999) provide evidence suggesting
that, following an unexpected change in the interest rate, prices respond sluggishly, leading
to movements in the real rate and hence activity. Uhlig (2005), on the other hand, shows
with a less restrictive identification strategy that, although prices still move sluggishly, one
cannot reject neutrality (ie real variables do not display a significant response).
    Other evidence also supports the relevance of the interest rate channel. For example,
results for the euro area suggest that the interest rate channel completely (or substantially)
characterises the direct transmission of monetary policy in most countries (BIS (1995) and
Angeloni et al (2003)). The traditional channel of monetary transmission is also embedded in
most “policy” models. Indeed, a number of large-scale macroeconometric models used by
central banks and other policy institutions exhibit a negative interest rate elasticity of
investment.68 Coenen et al (2012) present a review of seven structural models commonly used
by policymaking institutions.
     The interest rate channel operates with lags and can have asymmetric effects on output.
Nominal short-term rates tend to have a higher correlation with output than other interest
rates (Table 3B). Consistent with policy operating with a lag, nominal rates lead the business
cycle, that is, rates rise (decline) slightly before output growth goes down (up) (see Cooley
and Hansen (1995), Stock and Watson (1999) and Aruoba (2011)). Albeit reflecting various
mechanisms (not only the direct interest rate channel), a typical finding for the United States
is that a 1 percentage point decrease in the federal funds rate is associated with an increase
in quarterly output growth over the following two years of about 0.5 percentage points. There
are asymmetries, however: a 1 percentage point increase in the federal funds rate, for example,
is associated with a reduction in quarterly output growth over the following two years of
about 1.2 percentage points, more than double the effect of a similar decrease (see Angeloni
et al (2003) and Boivin et al (2011)).


67
   Deviations from the simple arbitrage-free model and other conundrums exist. For example, the period
of low long-term US interest rates between 2004 and 2006 could not easily be explained by existing
term structure models (with residuals amounting to some 40 –50 basis points) and was attributed at
the time by some to a “global savings glut” and globalisation more generally (see Rudebusch (2010)).
Since the GFC, there have been many studies on the drivers of the natural interest rate (eg Laubach
and Williams (2016) and Holston et al (2016)).
68
   For reviews of the various DSGE models employed by central banks, see Smets and Wouters (2003)
for the euro area; Edge et al (2008) and Chung et al (2010) for the United States; and Dorich et al
(2013) for Canada).


                                                 37
     Empirical studies also report high cross-country correlations of nominal interest rates and
significant correlations of real interest rates. These correlations have risen over the past
25 years (Figures 8 and 9), due in part to greater financial market integration but also to the
adoption of more similar monetary policies (see King (2012) who reviews the experience with
inflation targeting). Henriksen et al (2013), in a model that replicates the high degree of
comovement of interest rates, argue that, in response to cross-border spillovers of technology
shocks, central banks’ reactions can lead to highly correlated interest rate movements (see
further Rey (2016) regarding the international comovements of interest rates and the global
transmission of US monetary policy shocks).
    The spread between long- and short-term interest rates appears to help predict the timing
of recessions.69 Estrella and Mishkin (1998) document that term structure models based on
the three-month and ten-year US Treasury rates provide a reasonable combination of
accuracy and robustness in predicting US recessions. A number of studies have since
documented that the slope of the yield curve or the term spread – the difference between the
long- and the short-term rate – has significant power in predicting economic slowdowns
(Estrella and Trubin (2006), Rudebusch and Williams (2009) and Croushore and Marsten
(2016)).70 Indeed, Figures 10A and 10B show how the difference between the 10-year Treasury
bond yield and the three-month Treasury-bill rate can be useful in predicting recessions in
the United States. The slope also helps predict changes in certain components of real economic
activity, including consumption and investment (see Estrella and Hardouvelis (1991) and Ang
et al (2006)).71
     Evidence on the effects of UMP measures is somewhat limited. A rigorous assessment of
the effects of asset purchases and forward guidance on aggregate demand is difficult, in part
because it requires establishing a counterfactual scenario and elucidating an unstable
transmission channel.72 With this caveat in mind, the literature generally finds that central
bank statements affect not only current interest rates but also their future path. Campbell et
al (2012), for example, performing event studies over narrow time windows, find that 90% of
the variation in the expected federal funds rate four quarters-ahead can be attributed to
factors related to surprises in the timing of changes to the policy target. It also appears that


69
   If the market expects economic activity in the longer term to be stronger than what it is today, then
the short-term real interest rate should be higher in the future relative to today because the central
bank would be expected to increase its target rate to avoid an increase in inflation. An expected
increase in the short-term rate would, in turn, increase the long rate today through a no-arbitrage
relation. This would lead to a higher slope of the yield curve today. By contrast, a negative slope of
the yield curve would signal expectations of a recession (see also Smets and Tsatsaronis (1997)).
70
   Analysing different spreads, Bernanke (1990) shows that the spread between US commercial paper
and Treasury bill rates is the best predictor. Also for the United States, Boulier and Stekler (2000)
show that the spread between the 10-year Treasury bond yield and the 90-day Treasury bill rate is
positively associated with real growth rates. For the euro area, Moneta (2005) finds the spread between
the ten-year government bond yield and the three-month interbank rate to be the best in predicting
recessions. The slope of the yield curve has a greater predictive power than an index of leading
indicators, real short-term interest rates, lagged growth in economic activity and lagged rates of
inflation (see Wheelock and Wohar (2009) and Duffee (2013) for recent reviews). Gilchrist and
Zakrajsek (2012) document the predictive ability of credit spreads for economic activity in the United
States.
71
   Mehl (2009) reviews the literature on the predictive ability of the yield curve in EMEs and shows
that, depending on the extent of market liquidity, it has informational value for future inflation and
growth.
72
   See IMF (2013), Krishnamurthy and Vissing-Jorgensen (2013), Baumeister and Benati (2013), Gust
et al (2017) and Arteta et al (2015, 2016) for reviews.


                                                  38
there are temporary effects of UMPs on output and inflation. Evidence also suggests that
asset purchases can significantly reduce long-term yields, especially during times of financial
market turmoil (see also Krishnamurthy and Vissing-Jorgensen (2012) and Hancock and
Passmore (2011)).

C. Challenges to the standard models
While much research supports the basic transmission channels, evidence suggesting that other
factors play a role is accumulating. First, the quantitative importance of the direct interest
rate channel has been questioned. While results are not necessarily inconsistent with other
evidence that the direct channel exists, they do suggest the need to consider firm, household
and financial system heterogeneity and variations over time in the transmission of policy.
Second, there is intense debate about the causal factors underlying the predictive value of
interest rates and the slope of the yield curve for economic activity. These findings suggest
collectively that other elements, possibly associated with financial frictions, could be quite
significant for transmission.

Strength of the interest rate channel
A number of studies reveal that the direct impact of interest rate changes on economic activity
tends to be weak. Hall (1988) and Yogo (2004) find the effects of interest rates on aggregate
consumption not to be significantly different from zero (Elmendorf (1996) and Islamaj and
Kose (2016)). Other studies using micro data also fail to find strong evidence of a direct
interest rate impact on investment (Chirinko (1993)). This result appears to be related to
heterogeneity among firms. Since corporations engage in both saving and borrowing decisions,
the effects of interest rate changes depend on the structure of balance sheets, including the
maturity of assets and liabilities (Riddick and Whited (2009)). This could explain in part
why the empirical macroeconomic literature finds a weaker effect for the direct interest rate
channel.
    Other studies find that the impact of interest rates varies according to individual
household characteristics. Some studies find a weak effect for (financially) constrained
households and a stronger impact for non-constrained ones. Vissing-Jørgensen (2002), for
example, reports the elasticity of intertemporal substitution to be close to zero for agents that
do not hold stocks and bonds but about 0.3–0.4 for stockholders and around 0.8–1.0 for
bondholders. Changes in interest rates would therefore rarely matter for agents that do not
participate in asset markets (Wong (2016)).
    The potency of the direct interest rate channel also depends on the state of the economy
and the financial sector. In a weak economy, or one with an undercapitalised financial system,
interest rate changes tend to have a smaller impact on activity. In particular, in recessions
and periods of financial stress, pass-through from the policy rate to the cost of borrowing gets
smaller as more firms and households are excluded from credit markets. Moreover, even when
they do not face borrowing constraints, corporations and households could well be more
constrained during recessions for many other reasons and could change their investment and
consumption decisions for other reasons than interest rate changes (see Borio and Hofmann
(2017) for the non-linearity of interest rate effects).
    The direct interest rate channel can depend especially on the capitalisation of banks.
When banks try to restore profitability in the face of recession-impaired balance sheets, a
change in the policy rate may only be passed through partially to lending rates. This would
act to limit the lending response to a lower policy rate in the short run (see Woodford (2003),



                                               39
Eggertsson and Woodford (2004) and Bernanke et al (2004)). Conversely, low interest rates
can lead to more risk-taking when bank balance sheets are stronger. De Nicolò et al (2010)
show theoretically that when the policy rate is low, high-charter value (well capitalised) banks
may increase risk-taking and low-charter value (poorly capitalised) banks may do the opposite
(see further Dell’Ariccia and Marquez (2013) for a discussion of the theoretical literature on
the links between interest rates, bank capitalisation and risk-taking).
    Suggestive of market imperfections and financial frictions, many studies document that
the indirect effects of interest rates on activity can be quite large. Those studies, notably
Bernanke and Gertler (1995), argue that additional factors related to financial imperfections
can amplify and propagate the quantitative effect of the conventional direct interest rate
transmission channel. Using aggregate time series data for the United States, Bernanke and
Gertler (1995) show that because of frictions in financial intermediation, the transmission of
monetary policy largely operates through the financial system and depends on the balance
sheets of firms and households.
    The quantitative importance of different mechanisms also depends on country-specific
financial and institutional environments. In bank-based financial systems, where retail
deposits typically play an important role in funding and a large share of investment is financed
by banks, the direct interest channel can be expected to be more influential in transmitting
the effects of interest rates (see Allen and Gale (2000)). Indeed, while the overall quantitative
impact of changes in interest rates in the euro area is comparable to that reported for the
United States, there is a larger direct effect on investment (relative to consumption) in the
euro area. Angeloni et al (2003), for example, find that the interest rate channel characterises
monetary policy transmission in some euro area countries.73 By contrast, in the United States,
complementary channels through which interest rates affect consumption and investment are
more important (see Boivin et al 2011)).

Limits to the predictive power of the yield curve
Some interpret the predictive power of the slope of the yield curve for economic activity as
evidence of other channels. While many argue that the slope reflects expectations about the
monetary policy stance, and its relationship to economic activity, others question the
analytical foundations of this channel (Stock and Watson (2003)). Some give more credence
to the view that the slope of the yield curve affects intertemporal consumption choices that
lead to sales or purchases of assets in anticipation of changes in income (Campbell (1986)).
    Others point out that the direction of influence can be from activity to the yield curve.
In a dynamic model with rational expectations, Estrella (2005) shows that the term spread
contains information about expectations of future activity and is affected by current monetary
policy, which is, in turn, influenced by current activity. Using a state-space model that
comprises yield curve and macroeconomic factors, Diebold et al (2006) document that the
two sets of factor interact in both directions: macroeconomic variables affect the yield curve
(perhaps through the central bank’s reaction function) and the yield curve influences
macroeconomic variables.
     Furthermore, in the finance literature, movements in the term structure are thought to
reflect changes in (inflation) risk premia rather than (just) real activity. Advanced term
structure models, which employ data on interest rates at different maturities, explain the


73
  For the eurozone, see Kok Sørensen and Werner (2006) and van Leuvensteijn et al (2011). See also
Mojon (2000) for an analysis of differences in financial structures across euro area countries and their
implications for the interest rate channel.


                                                  40
predictive power by allowing for time-varying risk premia. For example, changes in the term
structure largely reflect changes in risk premia in the canonical affine no-arbitrage term
structure models augmented with macroeconomic variables. This is related in part to shifts
in the perception of inflation risk, with risk premia guiding, albeit imperfectly, macroeconomic
and financial developments (Gürkaynak and Wright (2012)).74
    Some studies report that risk premia are affected by macroeconomic factors. Using a
dynamic term structure model, Joslin et al (2014) find that macroeconomic variables have
significant predictive power over and above the level, slope and curvature of the yield curve.
Specifically, they report that macroeconomic risks that cannot be hedged by financial
variables (so called “unspanned risks”) explain a substantial portion of the variation of the
forward term premium, with unspanned real economic growth being the key driving factor.75
Baele et al (2010) show that macroeconomic factors are important in explaining bond return
volatility. This research suggests that macroeconomic factors affect not only the level of short-
term interest rates but also the term structure and other moments of the yield curve.
     There are also limits to the predictive ability of the yield curve, which depend on the
time horizon, country-specific circumstances and external factors. In particular, predictive
ability is largely relevant for up to one year in advance, especially in forecasting the timing
of recessions. The predictive value also varies across time periods (Bordo and Haubrich
(2004)), with its power possibly having declined over time (Stock and Watson (2003)). In
related research, Mody and Taylor (2003) find evidence of predictive power in the 1970s and
1980s in the United States, possibly due to high and volatile inflation but not in the 1990s
and 1960s. Chinn and Kucko (2015) report that predictive power has deteriorated in the
United States and in some European countries in recent years. Bonser-Neal and Morley (1997)
find that the yield spread explains 30% to 50% of future real economic activity in Canada,
Germany and the US, and less than 10% in Switzerland and Japan.
    Predictive power also seems to depend on global financial market conditions. The close
relationship between the risk premium in swap markets and real activity, for example, breaks
down around the year 2000 and during the 2006–07 period (Joslin et al (2014)). Since these
were periods of elevated financial stress, it suggests that predictive ability results from
developments in the financial system rather than from the direct effects of changes in interest
rates on activity (see also Adrian (2017) for a review).


74
   For empirical evidence, see Diebold et al (2005, 2006) and Rudebusch and Wu (2008). Affine models
are a special class of arbitrage-free term structure models, in which bond yields are affine (constant
plus-linear) functions of some (vector of) state variables (see further Rudebusch (2010)). Campbell et
al (2014, 2017), Christiansen and Ranaldo (2007), David and Veronesi (2013), Guidolin and
Timmermann (2007) and Viceira (2011) further examine the drivers of bond returns. For additional
reviews of the literature on the linkages between the nominal and real term structures of interest rates
and macroeconomic outcomes, see Dai and Singleton (2003), Gürkaynak and Wright (2012), Duffee
(2013) and Adrian (2017).
75
   In addition to the models that add macroeconomic variables to the canonical arbitrage-free term
structure model, Rudebusch (2010) identifies two additional strands: those that examine the financial
implications of bond pricing in DSGE models and those that use the Arbitrage-Free Nelson-Siegel
(AFNS) model. The first strand augments the standard RBC model with habit or recursive preferences
(Epstein and Zin (1989) and Hansen and Sargent (2008)) but has difficulty in replicating the size and
volatility of bond premia. Furthermore, the financial sector in these models remains very rudimentary
in terms of frictions and intermediation. The second class, AFNS-models, lacks theoretical foundations
but can be easily estimated and often exhibits a superior forecasting record. These models are
extensively used by policy institutions.



                                                  41
6. Conclusions

The GFC of 2007–09 revived an old debate in the economics profession about the importance
of macro-financial linkages. Some argue that the crisis was a painful reminder of our limited
knowledge of such linkages. Others claim that the profession has already made substantial
progress in understanding them but that there is too much emphasis on certain approaches
and modelling choices.76
    At the centre of the macro-financial nexus lies the relationship between asset prices and
macroeconomic outcomes. A long-held view among some academics, market participants and
policymakers is that asset prices are set in an efficient manner by market forces, which helps
guide the allocation of resources among competing projects. However, the apparent disconnect
between asset prices, fundamentals, market volatility and other phenomena, on the one hand,
and the predictions of standard models, on the other, has led many to question the “efficient
markets” framework, especially after the GFC. A broad review of “what we know” and “what
we do not know” about the linkages between asset prices and macroeconomic outcomes is
thus called for.
    This paper provides a survey of the literature on the linkages between asset prices and
macroeconomic outcomes, which is the natural starting point for an analysis of macro-
financial relationships. Since the literature covers a wide array of topics, there are many
caveats associated with a survey like ours. In light of these caveats, our survey focuses on a
small set of specific questions. First, what are the basic theoretical linkages between asset
prices and macroeconomic outcomes? Second, what is the empirical evidence supporting these
linkages? Third, what are the main challenges to the theoretical and empirical findings? We
analyse these questions in the context of the following asset price categories: equity prices,
house prices, exchange rates and interest rates. In this section, we first summarise our answers
to these three questions. We then offer pointers about promising avenues for future research.

What are the main takeaways?

Basic theoretical mechanisms. A broad lesson of the survey is that standard models provide
elegant benchmarks that allow the main mechanisms between asset prices and macroeconomic
variables to be analysed. Asset prices play a significant role in determining the allocation of
real and financial resources. They influence consumption, saving and investment decisions

76
  We presented some quotes reflecting the tenor of the debate at the beginning of the survey. Krugman
(2009a) criticises the macroeconomics literature because of its failure to recognise the strong
relationship between the financial sector and the real economy while Cochrane (2011a) provides a
critical response to Krugman’s views. Kocherlakota (2010), Caballero (2010), Romer (2016) and Reis
(2017) assess the state of research on macroeconomics, but arrive at different conclusions. Blanchard
(2017a) looks at the state of macroeconomics, focusing on the need to include distortions other than
nominal price rigidities, including financial frictions. For perspective, Blanchard (2000, 2009) and
Mankiw (2006) provide general reviews of the state of macroeconomics before the GFC. Many others,
including Bernanke (2010), Blanchard et al (2010, 2013), Woodford (2010a), Taylor (2011), Turner
(2012), Borio (2014), Claessens et al (2014a), Kose and Terrones (2015) and Blanchard and Summers
(2017) and several papers in the Fall 2010 issue of the Journal of Economic Perspectives look at how
the GFC may have influenced research. These and other reviews cover a broader set of issues than
macro-financial linkages and the intersection between macroeconomics and finance (see for example
Gopinath (2017) who provides a review of the recent macroeconomic policy-related work in
international economics). In addition, contributions have taken a broader perspective on how economic
policies have been reassessed (Blanchard et al (2012, 2016) and Akerlof et al (2014)).


                                                 42
through wealth and substitution effects. Through the information they carry about future
profitability and income growth, they also affect activity at both micro- and macroeconomic
levels. Moreover, the linkages between asset prices and macroeconomic outcomes play critical
roles in the cross-border spillovers of real and financial shocks.
    The linkages between exchange rates and macroeconomic outcomes are also
multidimensional. Many models look at how exchange rates are endogenously related to
macroeconomic variables and how these relationships are affected by a variety of factors,
including the heterogeneity of economic sectors, economies of scale, imperfect competition,
the type of exchange rate regime, country-specific elements and time horizons. Recent
theoretical models employ richer environments, including a consideration of the role of
financial variables and valuation effects in developing a better understanding of the linkages
between exchange rates and real and financial aggregates. However, some of the links between
exchange rates and macroeconomic outcomes remain ambiguous, including with respect to
the effects of devaluations on investment and output.
     The short-term interest rate is a special asset price since it is the main tool of monetary
policy. One of the key channels of monetary transmission, the direct interest rate channel,
focuses on the impact of interest rates on economic activity. In standard models, changes in
real interest rates lead to movements in the cost of capital and these, in turn, affect business
and household investment and consumption decisions.
Empirical evidence. Empirical studies provide evidence supporting some of the basic
mechanisms linking asset prices and activity. For example, there is evidence indicating that
asset prices affect corporate investment. Research also suggests that linkages arise through a
wide range of channels, with their impact depending on the characteristics of financial markets
and the types of asset under consideration. Moreover, consistent with the predictions of most
models, asset prices tend to be correlated with current and future aggregate activity. And
global factors play an increasingly important role in driving variations in asset prices.
    The empirical literature documents economically meaningful long-run relationships
between exchange rates and economic activity. The strength of these relationships, however,
varies across sectors, countries and time horizons. While empirical studies have been
inconclusive about the link between exchange rate depreciation and consumption growth,
there appears to be stronger long-run links between changes in exchange rates, volume of
trade and current account. A number of studies emphasize that exchange rates can play
supportive roles in facilitating reversals of current account imbalances. The exchange rate can
also affect aggregate activity (as a transmission mechanism of monetary policy), especially in
small open economies. In addition, there is a large empirical literature analysing the
interaction between exchange rates, financial variables and macroeconomic outcomes.
    The role of interest rates in shaping macroeconomic outcomes has also been extensively
documented. A number of empirical studies show that interest rates affect investment,
consumption and overall activity. The interest rate channel of monetary policy operates with
lags and can have asymmetric effects on output. Moreover, certain characteristics of the yield
curve can help in explaining the behaviour of various macroeconomic aggregates and can help
in predicting the timing of recessions.
Challenges to theoretical and empirical findings. The links between asset prices and activity
differ from the predictions of standard models in a number of ways. First, asset prices are
much more volatile than fundamentals would imply and can at times deviate, or at least
appear to do so, from their predicted fundamental values. The term structure of interest rates
is not fully consistent with the simple expectation hypothesis. Although exchange rates can
be modelled as the present value of expected fundamentals, they appear to be overly volatile,


                                              43
as is the case between equity prices and their underlying dividend streams (the puzzle of
“excess volatility”). Moreover, macroeconomic and financial news seem to have an
exaggerated effect on asset prices: equities, bonds and currencies overreact to news about cash
flows and other fundamentals.
    Second, investment and consumption respond differently to asset prices from what
standard models would suggest, with a larger role for “non-price factors” in driving agents’
behaviour and macroeconomic aggregates. Firm investment reacts less strongly to asset prices
than predicted by models while household consumption reacts more vigorously to changes in
asset prices, especially house prices, than consumption-smoothing models would suggest. In
addition, the links between asset prices and macroeconomic outcomes appear to vary across
countries depending on financial, institutional and legal structures. Research also questions
the strength of the direct impact of interest rate changes on activity and highlights its
dependence on the state of the economy and the financial sector, and institutional
arrangements. Recent studies emphasize the importance of uncertainty (measured among
others by the volatility of asset prices) in explaining macroeconomic outcomes.
    Third, there are limits to the predictive ability of asset prices for real activity. The basic
theory implies that asset prices should be good proxies for expected growth as they are
forward-looking variables. Equity prices, however, with their low signal-to-noise ratio and
their (excess) volatility, have a mixed record in forecasting activity. There are also limits to
the predictive ability of the yield curve, which depends on the time horizon, country-specific
circumstances and external factors. Although this remains a topic of intense research, recent
studies suggest that movements in exchange rates help only to a limited degree in predicting
changes in fundamentals.
     Fourth, similar to the domestic context, there are many puzzles involving the
international dimensions of asset prices. As is the case for the weak link between equity prices
and firms’ fundamentals within a country, comovements in asset prices appear to not (just)
reflect commonality in cash flow streams. The observed high correlations across asset prices
suggest other channels of transmission, including contagion, as suggested by the high
volatility of capital flows. The limited international diversification of investment, the so-called
home bias, has been hard to reconcile with the predictions of most asset pricing models.
     Fifth, recent research emphasises the important role played by financial imperfections in
explaining the linkages between asset prices and macroeconomic outcomes. Such imperfections
appear to curtail households’ ability to borrow against future labour income, leading to
liquidity constraints. Similarly, asset prices affect firm behaviour, including their willingness
and ability to issue new equity, in ways suggestive of financial frictions. Imperfections also
appear to amplify and propagate movements in asset prices (including through changes in
agents’ balance sheets). Moreover, financial factors and imperfections seem to influence the
linkages between exchange rates and macroeconomic outcomes.

Possible avenues for future research

Our survey suggests that the profession has made substantial progress in advancing its
understanding of the linkages between asset prices and macroeconomic outcomes. For
example, the standard models have been useful for studying the fundamental determinants
of these linkages. There is also a wealth of empirical evidence supporting some of the channels
posited by these models. However, there are still many qualitative and quantitative differences
between the predictions of the models and data. In addition, empirical evidence points to a
range of puzzles that require exploration. Hence, there is need for additional work to further
our understanding of the linkages. We briefly discuss three promising areas for future research.


                                                44
Data issues. There are large data gaps. The shortage (or outright lack) of data on important
financial and macroeconomic variables has been a severe limitation. For example,
comprehensive cross-country databases on public debt, fiscal space, and business and financial
cycles are only of recent construction (see Kose et al (2017a) for a discussion of the literature
on fiscal space). Although the prices of equities, bonds and other tradable assets are widely
available, obtaining higher frequency and more comparable series on house prices and
measures of credit remains a challenge. More granular data on the balance sheets of firms,
banks and other financial intermediaries would be required to be able to explore the causal
links between finance and the real economy and the systemic risks that can arise from these
links.77
     Data deficiencies are especially large at the international level. There is a dearth of
granular information, including on the bilateral exposures of financial intermediaries, and on
the cross-border activities of banks, institutional investors, hedge funds and other market
participants (see further Cerutti et al (2014)). Moreover, data are often not comparable
because they are compiled under different statistical frameworks. While some recent data
collection efforts have been fruitful, such as under the G20 Data Gap Initiative (FSB-IMF
(2016)), progress in this area has been slow. Obtaining detailed data on the world ’s largest
financial institutions, the so-called global systemically important financial institutions (G-
SIFIs), remains a challenge.
New generation of models. The survey documented a number of puzzles in the context of
asset prices. Some of these puzzles may simply have stemmed historically from a lack of data
to properly test for the predictions of models. However, many puzzles likely reflect the
inability of underlying theoretical models to account for certain features that potentially lead
to macro-financial linkages. The literature has been cognizant of the various factors that could
drive these puzzles (such as financial market imperfections). However, it has not always been
able to relate the puzzles to specific analytical deficiencies or convincingly demonstrate the
role played by particular channels. It is therefore critical to develop models that can better
account for the heterogeneous behaviour of agents, financial imperfections, differences in
financial and institutional structures across countries, and global linkages and spillovers (see
further Blanchard (2017a)). Such models also need to take into account demand- and supply-
driven linkages between asset price movements and macroeconomic aggregates. Potential
research avenues that would take into account financial imperfections are discussed in detail
in Claessens and Kose (2017).
    It is also necessary to develop a better understanding of the roles played by quantities in
driving linkages between asset prices and activity. Most models feature mechanisms that work
through prices, yet quantities appear to matter as well for the behaviour and volatility of
macroeconomic outcomes. For example, the interactions between house prices and lending
appear to affect how house prices relate to macroeconomic outcomes. The impact of interest
rates on activity also varies depending on the state of household and corporate balance sheets.
In addition, exchange rates appear to depend not only on interest rate differentials but also
on (changes in) balance sheets, order flows and valuation effects.
Design of policies. The GFC has highlighted the need for a better integration of “core”
research and operational aspects of policy. Many current policy questions focus on macro-
financial linkages. These include the implications of UMPs for the real economy and financial
markets, including overall financial stability. A better assessment of the role played by
monetary policy during a liquidity trap and the implications of UMPs on activity are of

77
  See the papers collected in Brunnermeier and Krishnamurthy (2014) on the general data needs for
research and policy work on macro-financial linkages.


                                               45
essential interest. In addition, the role of the exchange rate as a monetary policy target
(possibly in addition to the inflation rate) needs further investigation, especially for the design
of policies in small open economies and EMEs. Some recent studies have attempted to advance
the collective understanding of these topics but more work is warranted.




                                                46
Table 1A. House prices, equity prices and output: stylised facts
                                         (in percent)
                               Mean           Volatility     Maximum          Minimum

   House Prices
     1971:1-2016:4             2.17             7.64            59.80           -21.77
     1971:1-1984:4            1.24***          8.92***          59.80           -21.32
     1985:1-2016:4             2.57             6.96            36.74           -21.77

   Equity Prices
      1971:1-2016:4             4.96           23.73           149.64           -65.21
      1971:1-1984:4           -0.07***         23.85           138.50           -63.54
      1985:1-2016:4             7.16           23.34           149.64           -65.21

   Output
     1971:1-2016:4             2.30             2.61            28.08           -9.26
     1971:1-1984:4            2.60***           2.62            10.76           -4.10
     1985:1-2016:4             2.24             2.61            28.08           -9.26

Note: Mean indicates the average year-over-year growth rate. Volatility is the standard
deviation of the growth rate. Maximum (minimum) is the maximum (minimum) growth rate.
The sample consists of 18 advanced economies. *** indicates that the results for the period Q1
1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the
1% level.




                                             47
              Table 1B. Business cycles, asset price busts and booms

                                                                              Output

Recessions associated with                        Number of                              Cumulative
Financial Disruptions                               Events      Duration    Amplitude      Loss        Slope

A. Recessions without House Price Busts               95          3.38         -1.96       -3.08       -0.60
   Recessions with House Price Busts                  46         4.74***      -2.76**     -7.29***     -0.62
   Recessions with Severe House Price Busts           26         5.04**       -2.76**     -5.86***     -0.78

B. Recessions without Equity Price Busts              144         3.55        -2.15        -3.41       -0.57
   Recessions with Equity Price Busts                  76        4.21**      -3.85***     -6.85***    -0.98***
   Recessions with Severe Equity Price Busts           38        4.47**      -5.17***     -9.73***    -1.27***



                                                                         Output

Recoveries associated with                        Number of
Financial Booms                                     Events      Duration    Amplitude      Slope

A. Recoveries without House Price Booms               126         4.76         2.89        0.75
   Recoveries with House Price Booms                   14        2.29***      6.14***     1.35***
   Recoveries with Strong House Price Booms            9         2.44***      6.65***     1.59**

B. Recoveries without Equity Price Booms              161         4.89            3.96      1.11
   Recoveries with Equity Price Booms                  55         4.69            4.36      1.13
   Recoveries with Strong Equity Price Booms           30         5.18            4.46      1.21

Note: All statistics, except “Duration,” correspond to sample medians and are in percent. For “Duration,”
sample means are reported. Duration for recessions is the number of quarters between peak and trough.
Duration for recoveries is the number of quarters it takes to attain the previous peak level of output. The
amplitude of recessions is defined as the decline in output from peak to trough. The amplitude for recoveries
is the one year change in output after trough. Cumulative loss combines information about duration and
amplitude to measure the overall cost of a recession and is expressed in percent. The slope of a recession is the
amplitude from peak to trough divided by duration. The slope of a recovery is the amplitude from trough to
the period when output reached its last peak, divided by duration. Booms correspond to the observations in
the top 25% of upturns calculated by amplitude. Busts correspond to the observations in the worst 25% of
downturns calculated by amplitude. Recessions, recoveries, equity prices and housing busts and booms are
identified following Claessens et al (2012). The sample consists of 21 advanced economies for the period Q1
1960 to Q4 2011. *** and ** denote that recessions (recoveries) with asset price busts (booms) are significantly
different from those without asset price busts (booms) at the 1% and 5% levels, respectively.




                                                      48
         Table 1C. Correlations between asset prices and output
                         (correlation coefficient)
                                   Lags                                         Leads

                         -3         -2          -1          0          1          2           3

Equity Prices
   1971:1-2016:4        0.44       0.49        0.47       0.37       0.22        0.07       -0.05
   1971:1-1984:4        0.47       0.49        0.44      0.26**     0.05***    -0.17***    -0.32***
   1985:1-2016:4        0.44       0.50        0.50       0.41       0.27        0.12        0.01

House Prices
  1971:1-2016:3         0.34       0.41       0.46        0.47        0.44       0.39       0.35
  1971:1-1984:4        0.06***    0.17***     0.27*       0.37        0.37       0.35       0.30
  1985:1-2016:3         0.39       0.45       0.48        0.47        0.44       0.39       0.35

Note: The average within-country correlation between the year-over-year growth rates of asset prices
and output is presented. The sample consists of 18 advanced economies. Lags (leads) indicate that
output is shifted one or more quarters forward (backward) relative to asset prices. ***, ** and *
indicate that the average correlations for the period Q1 1971 to Q4 1984 are statistically different
from those for the period Q1 1985 to Q4 2016 (or Q3 2016) at the 1%, 5% and 10% levels, respectively.




                                                49
               Table 2A. Changes in exchange rates: stylised facts
                                  (in percent)
                                     Mean           Volatility         Maximum     Minimum

           Real effective exchange rate
              1971:1-2016:3       0.22                6.11              47.80        -22.66
              1971:1-1984:4      0.62*               6.50***            47.80        -15.39
              1985:1-2016:3       0.10                5.99              39.02        -22.66

           Nominal effective exchange rate
             1971:1-2016:3        0.30                6.42              43.50        -23.70
             1971:1-1984:4      -0.32***             6.91***            31.92        -20.53
             1985:1-2016:3        0.58                6.18              43.50        -23.70

           Output
             1971:1-2016:4            2.30              2.61            28.08        -9.26
             1971:1-1984:4           2.60***            2.62            10.76        -4.10
             1985:1-2016:4            2.24              2.61            28.08        -9.26


        Note: The mean indicates the average year-over-year growth rate. Volatility is the standard
        deviation of the growth rate. Maximum (minimum) is the maximum (minimum) growth
        rate. The sample consists of 18 advanced economies. *** and * indicate that the results
        for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1
        1985 to Q4 2016 (or Q3 2016) period at the 1% and 10% levels, respectively.



          Table 2B. Correlations between exchange rates and output
                            (correlation coefficient)
                                     Lags                                            Leads

                           -3          -2          -1            0          1          2              3

 Real effective exchange rate
    1971:1-2016:3     -0.11          -0.10       -0.08         -0.04        0.01      0.05        0.08
    1971:1-1984:4     -0.18          -0.21       -0.15         -0.09       -0.05     -0.03       -0.04
    1985:1-2016:3     -0.10          -0.08       -0.06         -0.03        0.02      0.06        0.09

 Nominal effective exchange rate
   1971:1-2016:3      -0.07    -0.06             -0.04         -0.01       0.02       0.04        0.06
   1971:1-1984:4      -0.06    -0.05             -0.01          0.03       0.06       0.06        0.02
   1985:1-2016:3      -0.07    -0.05             -0.03          0.00       0.03       0.06        0.07

Note: The average within-country correlation between the year-over-year growth rates of exchange rates and
output is presented. The sample consists of 18 advanced economies. Lags (leads) indicate that output is
shifted one or more quarters forward (backward) relative to exchange rates.




                                                   50
         Table 3A. Interest rates and output: stylised facts
                             (in percent)
                              Mean           Volatility      Maximum          Minimum

  Nominal short-term interest rate
    1971:1-2016:4      -0.18        2.13                        13.86           -17.50
    1971:1-1984:4     0.21***      2.84***                      12.28           -11.53
    1985:1-2016:4      -0.35        1.70                        13.86           -17.50

  Nominal long-term interest rate
    1971:1-2016:4      -0.10                   1.33             11.59           -13.19
    1971:1-1984:4     0.27***                 1.51***            6.15            -6.88
    1985:1-2016:4      -0.26                   1.21             11.59           -13.19

  Real short-term interest rate
     1971:1-2016:4       -0.07                 2.32             14.82           -16.19
     1971:1-1984:4      0.15***               3.25***           12.37           -10.04
     1985:1-2016:4       -0.16                 1.76             14.82           -16.19

  Real long-term interest rate
     1971:1-2016:4       0.00                  2.17             12.83           -10.65
     1971:1-1984:4     0.21***                2.95***           11.76           -10.65
     1985:1-2016:4      -0.09                  1.72             12.83            -9.53

  Output
    1971:1-2016:4              2.30             2.61            28.08           -9.26
    1971:1-1984:4             2.60***           2.62            10.76           -4.10
    1985:1-2016:4              2.24             2.61            28.08           -9.26

Note: Mean indicates the average year-over-year change (growth rate) in interest rates
(output). Volatility is the standard deviation of the change in interest rates (growth rate of
output). Maximum (minimum) is the maximum (minimum) change in each interest rate
(growth rate of output). The sample consists of 18 advanced economies. *** indicates that
the results for the period Q1 1971 to Q4 1984 are statistically different from those for the
period Q1 1985 to Q4 2016 at the 1% level.




                                             51
           Table 3B. Correlations between interest rates and output
                            (correlation coefficient)
                                      Lags                                            Leads

                           -3          -2          -1           0           1           2           3

Nominal short-term interest rate
  1971:1-2016:4     -0.10       0.04              0.21        0.35         0.42        0.42        0.36
  1971:1-1984:4    -0.23**    -0.11***           0.08**       0.22         0.26        0.28        0.21
  1985:1-2016:4      0.00       0.13              0.28        0.40         0.46        0.44        0.37

Nominal long-term       interest rate
  1971:1-2016:4           -0.17      -0.06        0.06        0.17         0.21        0.20        0.15
  1971:1-1984:4         -0.44***   -0.30***     -0.13***      0.09         0.25        0.29        0.24
  1985:1-2016:4           -0.08       0.02        0.12        0.20         0.21        0.18        0.12

Real short-term interest rate
   1971:1-2016:4      0.05            0.08        0.11        0.12         0.09        0.06        0.01
   1971:1-1984:4      0.03            0.08        0.19        0.24         0.16        0.07       -0.07
   1985:1-2016:4      0.05            0.05        0.07        0.08         0.08        0.07        0.06

Real long-term interest rate
   1971:1-2016:4     0.04             0.00        -0.05      -0.12        -0.18       -0.23       -0.24
   1971:1-1984:4     0.02             0.03        0.05*      0.05**       -0.02*      -0.13       -0.25
   1985:1-2016:4     0.00            -0.06        -0.11      -0.17        -0.21       -0.22       -0.20


Note: The average within-country correlation between the year-over-year interest rate changes and output
growth is presented. The sample consists of 18 advanced economies. Lags (leads) indicate that output is
shifted one or more quarters forward (backward) relative to interest rates. ***, ** and * indicate that the
average correlations for the period Q1 1971 to Q4 1984 are statistically different from those for the period
Q1 1985 to Q4 2016 at the 1%, 5% and 10% levels, respectively.




                                                    52
               Figure 1. Output and asset prices over the business cycle
                                     (in percent)
     8
         Output

     4



     0



    -4


                                                                                                Quarters
    -8
         -12             -8             -4               0             4              8              12

    40
         Equity Prices
    20


     0


   -20


   -40

                                                                                                Quarters
   -60
         -12             -8             -4               0             4              8              12

    15
         House Prices
    10

     5

     0

    -5

   -10
                                                                                                Quarters
   -15
         -12             -8             -4               0             4               8              12
Note: In each panel, the solid line denotes the median year-over-year growth rate of the indicated variable
during recessions while dotted lines correspond to the upper and lower quartiles. Zero is the quarter during
which each recession begins. The data sample consists of 18 countries and covers the period Q1 1971 to Q3
2011.




                                                    53
          Figure 2. Impulse response of output to an uncertainty shock
                                   (in percent)
  2




  1




  0




 -1



                                                                                                     Quarters
 -2
         0        1        2        3       4        5        6       7        8        9       10      11
Note: The graph shows the cumulative impulse response of global output to a global uncertainty shock. The
solid line represents the median estimate and the dotted lines denote the 16% and 84% error bands. This
impulse response is based on a FAVAR model that includes equity prices, uncertainty, interest rates, house
prices and output. Shocks are identified using a recursive identification strategy. Uncertainty is constructed
using the volatility of daily equity prices for G7 countries. The data sample consists of 18 countries and covers
the period Q1 1971 to Q3 2011.




                                                         54
         Figure 3. Cross-country correlations: asset prices and output
                            (correlation coefficient)
    0.8



    0.6

                                                     ***
                                                                                   ***
    0.4



    0.2                 **



    0.0
                 House Prices                  Equity Prices                  Output
                     1971:1-2016:4             1971:1-1984:4           1985:1-2016:4

Note: The average cross-country correlation for each variable in the respective periods is presented. The
sample consists of 18 advanced economies. *** and ** indicate that average correlations for the period
Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the 1%
and 5% levels, respectively.




  Figure 4. Variance due to the global factor: asset prices and output
                              (in percent)
    70

    60

    50                                               ***

    40                                                                             ***

    30
                        **
    20

    10

     0
                 House Prices                  Equity Prices                    Output
                      1971:1– 2011:3          1971:1– 1984:4          1985:1– 2011:3
Note: The average fraction of the variance explained by the global factor is presented. The sample
consists of 18 advanced economies. *** and ** indicate that variance explained by the global factors for
the period Q1 1971 to Q4 1984 is significantly different from that for the period Q1 1985 to Q3 2011 at
the 1% and 5% levels, respectively.




                                                  55
          Figure 5. Synchronisation of recessions and financial downturns
                                    (in percent)
                               A. Fraction of Countries in Recessions
100


 80


 60


 40


 20


   0
       1960   1965      1970      1975      1980      1985     1990      1995      2000      2005      2010

                     B. Fraction of Countries in Equity Price Downturns
100


 80


 60


 40


 20


   0
       1960   1965      1970      1975      1980      1985      1990     1995      2000      2005      2010

                     C. Fraction of Countries in House Price Downturns
100


 80


 60


 40


 20


   0
       1960   1965      1970      1975      1980      1985      1990     1995      2000      2005      2010
Note: Each bar represents the share of countries experiencing recessions or respective financial downturns. The
figures include complete as well as ongoing episodes. The sample contains the quarterly data for advanced
economies. Global recession years (1975, 1982, 1991 and 2009) are shaded in gray. House price data start in 1970.




                                                      56
                 Figure 6. House prices and residential investment
                                    (in percent)
                                                 2000–2006
 100
         Change in House
                                                                      ESP
  80     Prices
                                    FRA
         (Percent)                            NZL
                                                UK
  60                                                          IRL
                                               BEL                     DNK
                                      AUS                                                      SWE
                                                                      CAN
  40                                   ITA         USA
                                                                        NOR
                                             FIN
  20                           NLD
                                           CHE
   0

              DEU
 -20                                                                  Change in Residential Investment
                    JPN
                                                                                              (Percent)
 -40
       -20             0              20                 40                 60            80          100


                                                 2007–2008
   4
         Change in House
         Prices                                                                                 NLD
                                                                                               BEL
         (Percent)                                                                                    AUS
   0                                                                  SWE               CHE
                                                                                  FRA      DEUITA
                                                                                              JPN
                                                                    ESP          FIN    CAN
  -4                                             UK                       NOR


                              USA                                                DNK
  -8                                 NZL

                                                                      Change in Residential Investment
                    IRL
                                                                                              (Percent)
 -12
     -30           -25           -20           -15           -10             -5           0             5
Note: Each figure plots the percent changes in house prices and residential investment during the respective
periods. The sample consists of 18 advanced economies.




                                                      57
                Figure 7A. Evolution of world house and equity prices
                                     (in percent)
                                     World House Price Growth
 9

 6

 3


 0

-3

-6

-9
     1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013


                                    World Equity Price Growth
 40



 20



     0



-20



-40
         1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013
Note: Each panel shows the four-quarter average of market-weighted growth rates of the respective variables for
advanced and emerging market economies. All variables are in real terms. House price data start in 1970. Growth
in world equity prices starts in 1962; the market-weights are three-year rolling averages. Shaded bars indicate
global recessions. The last observation is for 2014.




                                                     58
        Figure 7B. Asset prices during global recessions and recoveries
                                    (index)
                                             Global Recessions
                     House Prices                                              Equity Prices
105                                                       140
      (Index)                                                   (Index)


100
                                                          120


 95


                                                          100
 90




 85                                                        80
        -4      -3     -2    -1   0      1      2                -4       -3     -2   -1   0      1      2

                                             Global Recoveries
                     House Prices                                              Equity Prices
112                                                       220
      (Index)                                                   (Index)
                                                          200
108
                                                          180

104
                                                          160


                                                          140
100

                                                          120
 96
                                                          100


 92                                                        80
        -2      -1     0     1    2     3       4                 -2      -1      0   1    2     3      4
                      1975                   1982                      1991                    2009
                     Average of global recessions
Note: Time 0 denotes the year of a global recession (shaded in gray). All variables are at annual frequency.
All variables are market-weighted by gross domestic product in US dollars, including all advanced and emerging
market economies. Panels on global recessions are index numbers equal to 100 one period before the global
recession year. Panels on global recoveries are index numbers equal to 100 in the global recession year.




                                                     59
      Figure 8. Cross-country correlations: interest rates and output
                          (correlation coefficient)
   0.6

                                   ***
                                                                                          ***
   0.4
                 ***
                                                                        ***
                                                      ***
   0.2



   0.0
             Nominal     Nominal long- Real short-  Real long-                         Output
            short-term term interest term interest term interest
           interest rate     rate         rate         rate
                       1971:1-2016:4          1971:1-1984:4           1985:1-2016:4
Note: The average of cross-country correlations for each variable in the respective periods is presented.
The sample consists of 18 advanced economies. *** indicates that average correlations for the period Q1
1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the 1% level.


 Figure 9. Variance due to the global factor: interest rates and output
                              (in percent)
    60

    50

    40
                                                                        ***
    30

    20

    10

      0
              Nominal Nominal long- Real short-      Real long-                        Output
             short-term term interest term interest term interest
            interest rate   rate          rate          rate
                         1971:1– 2011:3       1971:1– 1984:4        1985:1– 2011:3

Note: The average percent of the variance explained by the global factor is presented. The sample consists
of 18 advanced economies. *** indicates that variance explained by the global factors for the period Q1
1971 to Q4 1984 is significantly different from that for the period Q1 1985 to Q3 2011 at the 1% level.




                                                   60
         Figure 10A. Treasury spread: 10-year Treasury bond yield minus
                           3-month Treasury bill rate
                           (monthly average, in percent)
     6
                                                                       October 2017 = 1.27

     4


     2


     0


   -2


   -4
        1961       1969          1977         1985         1993          2001         2009         2017
Source: Federal Reserve Bank of New York.
Note: This figure shows the US Treasury spread, which is calculated as the difference between 10-year Treasury
bond yield and the three-month Treasury bill rate. The gray areas show recessions in the United States.


    Figure 10B. Probability of US recession predicted by treasury spread
                (12-month ahead, monthly average, in percent)
   100
                                                                       October 2018 = 9.04%

     80


     60


     40


     20


         0
         1961        1969         1977         1985         1993        2001         2009         2017
Source: Federal Reserve Bank of New York.
Note: This figure shows the probability of a US recession predicted by the Treasury spread. This prediction
is based on a model estimated using data from January 1959 to December 2009. Recession probabilities are
predicted using data through October 2017. The gray areas show recessions in the United States.




                                                      61
References

Abel, A. B., 1983, “Optimal Investment under Uncertainty,” American Economic Review, Vol. 73, No. 1,
pp. 228–33.
Abel, A. B., and O. J. Blanchard, 1986, “The Present Value of Profits and Cyclical Movements in
Investment,” Econometrica, Vol. 54, No. 2, pp. 249–73.
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