WPS6922


 Policy Research Working Paper                     6922




Institutional Investors and Long-Term Investment
                        Evidence from Chile

                                 Luis Opazo
                              Claudio Raddatz
                             Sergio L. Schmukler




 The World Bank
 Development Research Group
 Macroeconomics and Growth Team
 June 2014
Policy Research Working Paper 6922


  Abstract
  Developing countries are trying to develop long-term                              companies. The significant difference across maturity
  financial markets and institutional investors are expected                        structures is not driven by the supply side of debt or
  to play a key role. This paper uses unique evidence on the                        tactical behavior. Instead, it seems to be explained by
  universe of institutional investors from the leading case                         manager incentives (related to short-run monitoring and
  of Chile to study to what extent mutual funds, pension                            the liability structure) that, combined with risk factors,
  funds, and insurance companies hold and bid for long-                             tilt portfolios toward short-term instruments, even
  term instruments, and which factors affect their choices.                         when long-term investing yields higher returns. Thus,
  The paper uses monthly asset-level portfolios to show                             the expansion of large institutional investors does not
  that, despite the expectations, mutual and pension funds                          necessarily imply longer-term markets.
  invest mostly in short-term assets relative to insurance




  This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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 sschmukler@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
         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
         of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


                                                       Produced by the Research Support Team
           INSTITUTIONAL INVESTORS AND LONG-TERM INVESTMENT:
                           EVIDENCE FROM CHILE
                      Luis Opazo, Claudio Raddatz, Sergio L. Schmukler *




JEL Classification Codes: G11, G20, G22, G23, O16
Keywords: capital market development, debt maturity, institutional investors, insurance
companies, long-term finance, maturity structure, mutual funds, pension funds


*
  A preliminary version of this paper, using part of the data set and analyzing other aspects of it, came out
as World Bank Policy Research Working Paper 5056 “The Long and the Short of Emerging Market Debt”
in September 2009. We are grateful to Solange Berstein, Matias Braun, Pablo Castañeda, Andrew Foster,
Augusto de la Torre, Randall Dodd, Eduardo Fajnzylber, Jun Qian, Luis Serven, Raimundo Soto, and
participants at presentations held at Boston College (Boston), Columbia University (New York), the
Financial Intermediation Research Society (FIRS) Annual Conference (Florence, Italy), the IMF
(Washington, DC), the LACEA Annual Meeting (Buenos Aires, Argentina), the National Institute of Public
Finance and Policy (NIPFP) Workshop (Delhi, India), the Superintendency of Pensions(Santiago, Chile),
Universidad Adolfo Ibáñez(Santiago, Chile), the Universidad de Buenos Aires-Universidad de San Andres-
Universidad de La Plata Workshop (Buenos Aires, Argentina), Universidad de Chile (Santiago, Chile), and
the World Bank (Washington, DC) for their very useful comments. We are grateful to Alfonso Astudillo,
Leandro Brufman, Juan José Cortina, Ana María Gazmuri, Matías Moretti, Lucas Nuñez, María Mercedes
Politi, Fernando Sepulveda, María Ignacia Valencia, Camilo Vio, and Mario Zambrano for excellent
research assistance at different stages of the paper. We also thank Prof. Juan Antonio Cuesta-Albertos for
generously sharing his code for the functional data version of the Kolmogorov-Smirnov test. The World
Bank provided ample financial support through its Development Economics Department and Knowledge
for Change Program (KCP). This version of the paper is part of the work prepared for the Global
Development Finance Report 2015. Opazo and Raddatz are with the Central Bank of Chile. Schmukler is
with the World Bank Research Department. The views expressed here do not necessarily represent those of
the Central Bank of Chile or the World Bank. Email addresses: lopazo@bcentral.cl, craddatz@bcentral.cl,
and sschmukler@worldbank.org.
1. Introduction

        One important and pending problem in almost all developing countries is the lack

of development of markets for long-term finance. This crucial issue has become more

prominent in the policy discussions, especially after the global financial crisis of 2008-

2009, because having access to long-term funds allows governments and firms to finance

large investments over time and reduce rollover risk and potential runs, which can lead to

costly crises.1 Moreover, from a social point of view, having access to long-term

instruments might give households higher risk-adjusted returns. But despite the

advantages of long-term debt for the debtors, many investors prefer short-term debt as a

way to discipline debtors and cope with moral hazard, agency problems, risk, and

inadequate regulations and institutions, among other things (Rajan, 1992; Rey and

Stiglitz, 1993; Diamond and Rajan, 2001). So obtaining long-term contracts in

equilibrium is not easy.

        Because of their benefits and the difficulties in developing long-term markets,

many countries have actively tried to foster long-term lending through various measures

that tackle different parts of the financial system. One important component in this

strategy is the promotion of institutional investors such as pension funds, which have

grown rapidly since the 1990s. The expectation is that, by managing most domestic

savings including those for retirement purposes, institutional investors would invest long

term (including infrastructure projects) and, thus, foster long-term capital market

development. This view has been expressed in several studies and articles, for example,


1
 In fact, the literature argues that short-termism can explain several well-known financial crises in both
emerging and developed countries (Eichengreen and Hausmann, 1999; Rodrik and Velasco, 2000; Tirole,
2003; Borensztein et al., 2005; Jeanne, 2009; Alfaro and Kanczuk, 2009; Brunnermeier, 2009; Raddatz,
2010; Broner et al., 2013).


                                                    2
Caprio and Demirguc-Kunt (1998), Corbo and Schmidt-Hebbel (2003), BIS (2007),

Borensztein, et al. (2008), Eichengreen (2009), Della Croce, et al. (2011), OECD

(2013a,b), and The Economist (2013, 2014). Institutional investors are also expected to

professionally manage assets, diversify risk, and overcome problems of asymmetric

information and transaction costs that pervade financial markets. But how long

institutional investors invest depends on many factors including their utility function,

their liability structure, and the incentives managers face from markets and regulators

(Bajeux-Besnainou, et al., 2001; Campbell et al., 2001; Campbell and Viceira, 2002).2

        Despite the expectations that many authors have placed on institutional investors

and their large size and continuing rapid growth in many countries, little evidence exists

on whether these investors actually invest long term and how they structure the maturity

of their portfolios. This lack of evidence is mainly due to the difficulty in obtaining

detailed portfolio data on institutional investor holdings. The literature on portfolio

composition has focused almost exclusively on specific institutional investors in

developed countries (to a large extent, US mutual funds). The international evidence has

concentrated on international mutual funds domiciled in international financial centers

and their investments across countries, ignoring the behavior of the large domestic

institutions and the heterogeneity across investor types. Moreover, the literature tends to

center just on equity holding, and is therefore silent on the maturity choices of

institutional investors.3



2
  For general references on their expected impact on capital markets, see, for example, Davis (1995), Davis
and Steil (2001), and Impavido et al. (2003, 2010).
3
  See, for example, Grinblatt and Keloharjub (2000), Kim and Wei (2002), Borensztein and Gelos (2003),
Kaminsky et al. (2004), Gelos and Wei (2005), Broner et al. (2006), Hau and Rey (2008), Jotikasthira et al.
(2012), Raddatz and Schmukler (2012), and Didier et al. (2013).


                                                    3
       This paper sheds new light on the extent to which institutional investors invest

long term and the factors underpinning their maturity choices. To do so, we analyze

unique data on the actual portfolios and bids of the universe of domestic institutional

investors in the benchmark case of Chile. In particular, we assemble asset-level time-

series portfolio holdings of bank deposits, sovereign bonds, and corporate bonds of

medium- and long-term bond mutual funds, pension funds, and insurance companies at

high frequencies (monthly, and also daily for pension funds). We also use detailed data

on the individual biddings at government paper auctions and returns of government bonds

at different maturities. The main data set on asset holdings by Chilean institutional

investors contains 965,209 observations for mutual funds, 6,659,681 monthly

observations for pension funds, and 4,071,927 observations for insurance companies.

       The value added of the paper is twofold. First, it documents in detail the maturity

structure of different kinds of institutional investors to establish to what extent these

investors demand long-term assets. Second, the paper discusses what factors might be

behind the maturity structure of institutional investors’ portfolios by exploiting a rich data

set on Chile, containing different types of investors within a single market. Because these

investors operate in the same macroeconomic and institutional environment and have

access to the same set of instruments, this approach allows us to control for specific

sources of variation across investors and asset classes.

       This paper studies different hypotheses related to the maturity choices of

institutional investors. First, the equilibrium might be short term if borrowers do not issue

long-term paper. Therefore, the paper explores if the supply side of instruments (the

demand side of capital) is the one determining the equilibrium outcome. Second, the




                                              4
paper studies if institutional investors hold short-term instruments for tactical reasons, to

take advantage of large fluctuations in asset prices to purchase securities during crises.

Third, the paper studies what role incentives play in the maturity choice, following the

papers that argue that principal-agent problems can lead managers to invest short term.4

        Three factors that can affect manager incentives are: the risk of different

instruments, short-run monitoring, and the liability structure. Long-term instruments have

more price risk, which can more easily generate deviations of each fund from the industry

(provided that they do not hold the same assets). This is important for open-end mutual

and pension funds that need to mark-to-market their portfolios and are monitored on a

short-run basis by market participants and regulators. Poor performance could lead to

costly outflows and penalties, among other things, that force managers to liquidate assets,

reducing at the same time the pool of assets they administer and their associated fees

(Rajan, 2005; Lim et al., 2013). Insurance companies, on the other hand, have fixed long-

term liabilities and are not subject to these withdrawals due to their closed-end nature.5

Our comparison of the maturity structure across institutional investor types offers useful

information to this literature, which has mostly focused on micro evidence at the

managerial level.

        We find that asset-management institutions in Chile (both mutual funds and

pension funds) hold a large amount of short-term instruments (bank deposits including

cash, government paper, and corporate debt) that are easy to liquidate. For example,

mutual funds and pension funds hold portfolios with an average maturity of 3.97 and 4.36


4
  See, for example, Narayanan (1985), Sharfstein and Stein (1990), Shleifer and Vishny (1990), Bebchuk
and Stole (1993), Chevalier and Ellison (1999), Kapur and Timmermann (2005), Stein (2003, 2005),
Bolton et al. (2006), Calomiris (2008, 2011), Chen and Pennacchi (2009), and Pennachi and Rastad (2011).
5
  Although not the case in Chile, in some countries pension funds also have this type of structure.


                                                   5
years, respectively. This similarity between mutual and pension funds is especially

surprising considering that pension funds are supposed to save for the retirement of the

pensioners. In contrast, insurance companies are significantly more tilted toward the long

term, holding portfolios with an average maturity of 9.77 years.

        The short-termism of pension funds is not determined by a lack of instruments or

tactical behavior. In particular, of the outstanding government and corporate debt,

pension funds do not exhaust the supply of long-term instruments. Moreover, individual

biddings at government paper auctions suggest that pension funds bid less aggressively

for long-term instruments, both relative to other instruments and relative to insurance

companies. In addition, pension funds do not use their cash and other short-term

investments to take advantage of buying opportunities that arise during fire sales related

to crises.

        Estimates of returns of government bonds of different maturities suggest that,

given the risk-return tradeoff, investors with a short-run horizon have more incentives to

invest in short-term instruments relative to investors with a long-term horizon. While

long-term assets yield higher returns at a higher risk, the risk-return relation diminishes as

the investment horizon lengthens. Thus, the prevalence of short-term assets in pension

and mutual fund portfolios is consistent with them having relatively short-term

investment horizons.

        We provide evidence that the shorter investment horizon of mutual and pension

funds compared to insurance companies might result from agency factors that tilt the

managerial incentives. Namely, the fact that long-term assets are more volatile than short-

term ones poses a risk to open-end funds subject to short-run monitoring. In the case of




                                              6
mutual funds, the short-run monitoring is exercised by investors, who inject/redeem their

assets based on the funds’ short-run performance. In the case of pension funds, both

common regulatory practices that punish the funds that deviate from industry averages

and the owners of the asset-management companies exert a short-run monitoring.

Investors, too, could monitor managers in the short-run, even when their investments are

geared toward the long term. In contrast, insurance companies are not open-end asset

managers, receive assets that cannot be withdrawn in the short run, and have long-term

liabilities as investors acquire a defined-benefit plan when purchasing a policy. Thus,

insurance companies are not subject to the same kind of short-run monitoring. This type

of short-run monitoring seems to be behind the risk aversion of pension funds. When

pension funds do poorly they cut risk by investing more short term, perhaps as a way to

reduce the potential of having an even lower return. On the contrary, when mutual funds

do poorly they invest more long term, maybe as a way to try to compensate for their low

returns. This different behavior between Chilean pension and mutual funds is consistent

with the incentives they face, as we describe in the paper.

       The experience from the ideal benchmark case of Chile shows that the

development of large and sophisticated intermediaries with deep pockets does not

guarantee an increased demand for long-term assets. Relative to other emerging

economies, Chile has a developed capital market (de la Torre et al., 2011) and its

administrations have made a conscious effort from the supply and demand side of capital

to provide an adequate framework to extend debt maturities. In particular, Chile was the

first country to adopt in 1981 a mandatory, privately managed, defined-contribution (DC)

pension fund model by replacing the old public, defined-benefit (DB) pension system.




                                             7
Many developed and developing countries have followed suit and reformed their pension

regimes, establishing this type of pension fund system with rather similar regulatory

schemes.6 Thus, the characterization of the maturity structure of Chilean pension funds

and its comparison to that of other institutional investors offer some interesting lessons

on the role that these investors play on the development of a long-term debt market.

        The rest of the paper is organized as follows. Sections 2 and 3 briefly describe the

institutional investors we analyze and the main data used (the other data sets are

described throughout the paper). Section 4 characterizes the maturity structure of Chilean

institutional investors. Section 5 analyzes to what extent the supply side of instruments,

tactical behavior, risk, and managerial incentives might affect the maturity structure.

Section 6 concludes.



2. Chilean Institutional Investors

        As mentioned above, the Chilean institutional investors developed as part of a

series of macroeconomic and financial sector reforms that targeted both the demand and

supply side of capital. On the demand side, Chile has introduced reforms to foster capital

market development. Corporations and the government have then issued a wide range of

securities, including long-term local currency bonds. Moreover, Chile’s stable

macroeconomic performance since the early 1990s and its long history of issuing

inflation-linked instruments have also reduced the risk and the cost of long-term assets.



6
  For example, the UK moved toward a multi-pillar pension system in 1986. Sweden modified in 1994 the
pension system from a pay-as-you-go DB to a second-pillar system that includes a voluntary DC system. In
the US, proposals to reform the social security system were also recurrently considered. Following Chile’s
example, many developing countries adopted similar reforms, including Argentina, Bolivia, Colombia,
Costa Rica, the Dominican Republic, El Salvador, Hungary, Kazakhstan, Lithuania, Mexico, Peru,
Slovakia, Poland, and Uruguay.


                                                    8
        On the supply side, Chile has established relatively early a broad institutional

investor base. As a consequence, during the period under study these investors grew

considerably, received a steady inflow of funds, and became well established and large.

By 2005, mutual funds, pension funds, and insurance companies collectively had assets

under management equivalent to 84% of GDP. They have played an important role in

financial markets, investing in different types of asset classes, such as bonds, deposits,

equities, mortgages, and money market instrument, issued by both the private and the

public sectors, domestically and abroad. Given their size and importance as conduits of

savings, these institutional investors have offered different products and have been

subject to different regulations.

        Pension funds are the most important institutional investor in Chile, with assets

under management equivalent to 56% of GDP in 2005. Since 2002, pension fund

administrators (henceforth PFAs) offer five different funds (“A” to “E”), where fund “A”

offers the highest return-risk profile, and fund “E” the lowest.

        Because they are under a defined-contribution scheme, the investment portfolio of

pension funds is not subject to any regulation regarding a target asset-liability term

mismatch. The most important link between regulation and asset allocation is driven by

the so called “structural limits,” which basically restrict the proportion of foreign

investment and the ratio of equity and fixed income investment to total assets in each

type of fund. This implies that fund “A” is tilted to equity instruments, and fund “E” to

fixed income.7



7
  There are several other limits, such as counterpart exposure and derivatives investments, which we omit
from our analysis. For example, pension funds are allowed to use derivatives up to only 3% of their assets
(including any type of derivatives). These rules, in practice, imply a quite small use of interest rate


                                                    9
        Another important common regulatory restriction that pension funds face is that

they need to deliver a minimum rate of return. This regulation establishes that pension

funds are responsible for ensuring an average real rate of return over the previous 36

months that exceeds either (i) the average real return of all funds minus two or four

percentage points, depending on the riskiness of each fund, or (ii) the average real return

of all funds minus the absolute value of 50% of that average return. However, to

minimize the impact of this regulation on herding, the average real rate of return to

calculate the minimum return changed from 12 months to 36 months in October 1999,

giving PFAs more flexibility to deviate in the short term from industry comparators. If a

fund falls short in performance, the PFA must compensate for the difference.

        This kind of pension fund regulation is not Chile-specific and is typical of

systems that have DC pension programs, where the regulator wants to ensure the safety

of public savings. For example, in Latin America, Colombia, El Salvador, Peru, and

Uruguay (countries that have also reformed their pension fund systems) have similar

minimum return bands. In Europe, Poland, the Slovak Republic, and Switzerland also

have similar schemes. Other developed countries (Belgium and Germany) have

analogous guarantees in their voluntary DC programs.

        The insurance companies analyzed in our paper are life insurance companies,

which by 2005 managed assets equivalent to 18% of GDP. These assets come mainly

from funds accumulated by pensioners seeking an annuity income during their retirement

period of around 20 years. Given the long-term nature of their liabilities, insurance

companies have strong incentives to hedge their liabilities with long-maturity bonds to


derivatives to change their maturity profile. Furthermore, interest rate derivatives markets were not very
well developed in Chile during the period under study (Fernandez, 2006).


                                                   10
reduce their default risk and increase customers’ demand. Still, life insurance companies

are subject to different limits that intend to minimize the risk of their investments. One of

them (removed at the end of 2011) is related to the asset-liability mismatch, by which

cash flows are projected in ten tranches into the future and, based on the degree of

mismatch in each tranche, the life insurance company adjusts its capital requirement. In

addition, life insurance companies are subject to an asset adequacy test and other limits

referred to a maximum exposition to some asset class (such as, equity and real estate) and

the concentration to issuers and related investors.

       Mutual funds are the institutional investor less exposed to regulatory requirements

to structure their portfolios. In general, regulation is flexible and is mainly focused in

diversification and disclosure requirements. Once a mutual fund company decides to

offer a new fund, for example a “corporate fixed income fund,” the fund is obligated to

invest at least 90% of their assets in those instruments stated in the fund name. Therefore,

each mutual fund company determines the investment portfolio composition of its funds.



3. Main Data

       The main data used in this study consist of asset-level holdings of institutional

investors during the relevant period 2002-2008, when institutional investors grew and

consolidated in Chile and financial markets operated under relative normal

circumstances. The data come from different sources.

       The data on Chilean mutual funds and insurance companies come from the

Superintendency of Securities and Insurance (Superintendencia de Valores y Seguros,

SVS). The data on Chilean pension funds, the most comprehensive data, come from the




                                             11
Superintendency of Pensions (Superintendencia de Pensiones, SP). The other data used,

described throughout the paper, come from the Central Bank of Chile (Banco Central de

Chile) and other sources (Appendix Table 1).

        The data on Chilean mutual funds contain detailed portfolios of all existing

medium- and long-term funds at a monthly frequency during the period January 2002 to

December 2008. The database comprises 965,209 observations. It includes information

on the type of security, currency denomination, price, units held, and maturity date. In

addition to these medium- and long-term funds, there are several short-term mutual funds

providing money market services. We exclude those from the analysis to focus solely on

funds established to invest long term.8

        For pension funds, we use a panel of their portfolio investments in fixed-term

assets for each of the existing funds during the period 1996-2008 at monthly and daily

frequencies. We perform more detailed analysis for the period 2002-2008, when the

investment options expanded to more funds. We use panel data with the amount of

deposits (including cash as deposits with a one-day maturity), corporate bonds, and

government bonds held by fund per unit of time.9 There are a total of 6,659,681

observations on a monthly frequency, representing the portfolio holdings of the funds.

The data set contains information on the holdings of 76,498 different securities for 45


8
  Chilean mutual funds are classified according to the type and investment horizon of their assets. Fixed-
income funds include money management funds (with horizons of less than 90 days or less than 365 days)
and medium- and long-term funds. We only use the latter two, since the first ones would be tilted toward
the short term by construction. In 2008, approximately 60% of the existing funds were categorized as
medium- or long-term funds, 12% as money management funds (less than 365 days), and 28% as money
management funds (less than 90 days).
9
  Since September 2002, each pension fund administrator (PFA) offers by law five funds with different risk
profiles and investments in equity, subject to different portfolio regulations. The PFAs organize their
trading desks in different forms that vary over time. For example, some pension fund companies have
specialists for each asset class across fund types while others have dedicated managers for each fund,
selecting the portfolio in each asset category.


                                                   12
funds between September 2002 and June 2008. In addition to this monthly data set, we

use a daily data set with portfolios of the universe of funds and PFAs in operation, which

contains 201,288,833 observations for 62 funds between July 1996 and July 2008.10 The

daily data have the same fields included in the monthly database.

        The data on Chilean insurance companies comprise monthly portfolio holdings

from January 2002 to December 2008. The database contains 4,071,927 observations

corresponding to the fixed-term assets of 36 insurance companies. Information on

security type, maturity date, and currency, among others, are available in this data set.



4. Maturity Structure

        We describe the holdings of long-term assets by documenting in turn the maturity

structure of mutual funds, pension funds, and insurance companies. For the case of

mutual funds, Figure 1 plots the fraction of investments in fixed-term assets per year-to-

maturity, both within each maturity range and accumulated. We construct the figure by

determining at each point in time (each month) the term to maturity of each instrument in

a mutual fund portfolio, measuring the fraction of the value of all assets invested at

different terms to maturity, and then averaging these fractions across mutual funds and

time.

                           k
        Let di ,t and wi ,t denote the term to maturity of asset i at time t, and the share of

fixed-term assets invested in asset i at time t by fund k, respectively. The fraction of fund

k’s fixed-term assets with term to maturity D is



10
  The difference between the number of funds in the monthly and daily data sets is due to the extended
period the daily data set covers.


                                                 13
(1)                                    WD ,k ,t   wi ,t k I (di ,t  D) ,
                                                   i



where I denotes an indicator function that takes on the value one if the condition is met.

The average fraction of fund k ’s fixed-term assets invested at maturity D across time is

                                                            Tk
                                                       1
 (2)                                        WD ,k 
                                                       Tk
                                                            W
                                                            t 1
                                                                   D , k ,t   ,


where Tk is the number of periods in which mutual fund k is active. The overall average

fraction of fixed-term assets invested at maturity D across mutual funds and months

corresponds to

                                                1 N Tk
 (3)                                        WD   WD,k ,
                                                N k 1 T

where T denotes the number of months included in the entire sample period, and N is the

number of active mutual funds.11

        The fractions computed correspond to the empirical probability distribution

function (PDF) of the term to maturity of a Chilean peso invested by mutual funds in

fixed-term assets. The empirical cumulative distribution function (CDF) of the term to

maturity can easily be obtained by adding these fractions up to a given maturity. Finally,

in addition to the average CDF, Figure 1 also reports the 25th and 75th percentiles of the

CDF across mutual funds.

        Figure 1, Panel A shows that mutual funds hold a large fraction of their assets

short term. For example, they invest 38% of their portfolio up to one year, 59% up to

three years, and 73% up to five years. Moreover, they hold almost all of their assets in

11
   Although we focus on maturity, in unreported results we also analyze if there are large differences
between the maturity and duration structure of pension funds for which we have the necessary data to
perform the comparison. We find that the proportion of the portfolio held within each maturity/duration
range is not very different, except for short-term bonds with maturities of one to four years.


                                                   14
securities maturing within 15 years (95%). However, the distributions vary greatly across

mutual funds, as shown by the 25th and 75th inter-quartile range across funds, averaged

over time: the fraction of the fixed-term portfolio invested up to one year varies between

24% and 50%. Panel B shows that portfolio weights decline exponentially; the highest

density is observed at short maturities, after which probabilities systematically decline.

       Figure 2 shows the maturity structure of Chilean pension funds for the 2002-2008

period. Similar to mutual funds, PFAs are heavily invested in short-term assets. For

example, they invest 40% up to one year, 56% up to three years, and 68% up to five years

(Panel A). The distributions do not vary much by PFA as shown by the inter-quartile

range calculated across PFAs over time. The fraction of the fixed-term portfolio invested

up to one year varies only between 37% and 45% during the sample. Even smaller

degrees of dispersion are observed at other ranges of the CDFs. Panel B shows that the

portfolio weights decline exponentially, similarly to the case of mutual funds.

       Figure 3 shows the maturity structure of Chilean PFAs by fund type. Funds A (the

riskiest ones) have the lowest average maturity (2.72 years) and invest almost 62% of

their assets in instruments up to one year. In contrast, funds E (the safest ones) have the

highest average maturity (5.70 years) and invest almost 30% in instruments with maturity

of seven years or more. More generally, the maturity structure tends to increase as funds

become safer. The reason for this surprising finding might be related to the fact that each

affiliate (aged 55 or younger) can choose freely the type of fund to invest. Then, within

PFAs there might be competition between fund types to attract clients. To compensate for

the fact that they can only invest 5% of their portfolios in stocks, funds E might invest in

longer-maturity bonds to achieve better returns.




                                             15
         Figure 4 compares the maturity structure of Chilean mutual funds, pension funds,

and insurance companies. We focus first on the differences between mutual funds and

pension funds. The distributions of both types of institutional investors are very similar,

with small differences for some maturities (Panels A and B). However, the average

maturity of assets held by pension funds (4.36 years) is not statistically different from

that held by mutual funds (3.97 years) (Panels C and D). Mostly because of the difference

observed at particular maturities, a two-sample goodness-of-fit test for functional-data

(henceforth KS test) rejects the hypothesis that the observed maturity structures of

pension funds and mutual funds are generated by the same underlying distribution.12 In

unreported results we also compare the maturity structures at monthly frequency with a

coarser distribution (grouping the maturities for each month in different bins); we cannot

reject at conventional levels the hypothesis that the maturity structures of these two types

of investors are generated by the same distribution.

         The comparison between mutual and pension funds and insurance companies

shows that insurance providers are much more heavily invested in long-term instruments

than mutual and pension funds are (Figure 4). The differences are quite significant both


12
  This test was proposed by Cuesta-Albertos et al. (2006), and consists on applying a standard two-sample
Kolmogorov-Smirnov (KS) test to the random projections of each set of functional data; in our case the
samples of maturity structures of all pension funds and mutual funds, respectively. We start by forming two
groups of vectors of length M corresponding to the time-average maturity structures of all individual
                                         
pension and mutual funds WD , k  (1 / T ) WD , k ,t , discretized by month, with M corresponding to the
                                           t

longest maturity observed (in months). Each of these vectors is projected on a random direction           ,
obtaining two samples of random projections (one for each type of investor) of sizes n1 and n2 , the number
of pension funds and mutual funds respectively. The standard two-sample Kolmogorov-Smirnov test is
then applied to these samples. The process is repeated M times using different random directions, and the
resulting set of p-values is adjusted for false discovery rate under dependency as in Benjamini and Yekuteli
(2001). The p-value reported in the table corresponds to the minimum of the adjusted p-values, which
indicates the level of confidence with which at least one of the M hypotheses can be rejected. An
alternative statistic proposed by Cuesta-Albertos et al. (2007), based on the fraction of rejections among the
 M hypotheses, yields similar conclusions (not reported).


                                                     16
economically and statistically at different points in the distribution. And these differences

are reflected on the average maturity of Chilean insurance companies (9.77 years)

relative to those of mutual and pension funds (3.97 and 4.36 years, respectively).13

         In sum, the evidence suggests that Chilean mutual funds and pension funds are

short-term investors relative to insurance companies in Chile and mutual funds in the US.

Insurance companies in Chile are able to obtain a relatively long maturity structure even

when compared to US mutual funds. This contradicts the expectation that mutual funds

and pension funds are long-term investors, and raises the question of which factors might

be driving them to invest short term and insurance companies to invest long term.



5. What Drives the Maturity Structure?

         To analyze the potential factors that may contribute to the short-termism of

mutual funds and pension funds, we rely on different types of evidence. We focus on four

factors: (a) instrument availability, (b) rebalancing, (c) risk of investment instruments,

and (d) managerial incentives.

5.A. Instrument Availability

         From the previous evidence we know that insurance companies are significantly

more tilted toward the long term, which signals that long-term securities are available in

Chile. Yet, mutual and pension funds could still be constrained by the supply of long-


13
   In unreported results that use US fixed-income mutual funds as a benchmark comparison, we show that
Chilean mutual and pension funds are much more tilted toward the short term than US mutual funds, while
Chilean insurance companies are more long term. For example, for 2002-2005, Chilean pension funds hold
52% of their fixed-term instruments in assets with maturity of up to three years, while US multi-sector and
short-term mutual funds hold, respectively, 24% and 48% of their portfolio in assets with that maturity. The
differences persist throughout the distribution. All these differences result in a much longer average
maturity for US multi-sector and short-term mutual funds (9.55 and 7.76 years, respectively) than for
Chilean pension funds and mutual funds (4.61 and 3.88 years, respectively). These distributions are
statistically different at all conventional significance levels.


                                                    17
term instruments. To study the role of the supply side, we analyze unique data on bonds

held relative to bonds outstanding and bids at government bond auctions.

        Figure 5 shows the total amount of bonds issued by the government at different

maturities between 1998 and 2008 and the fraction of those issuances purchased by

pension funds. The figure contains separate panels per currency: nominal Chilean peso,

indexed Chilean peso (also known as UF, or inflation-linked), and US dollar. The figure

shows that in all cases pension funds purchase significantly less than the total amounts

issued. On average, pension funds purchase 3% of issuances in Chilean pesos, 40% of

government issuances in inflation-indexed pesos, and 15% of issuances in US dollars.

Also, within each denomination, the fraction of long-term issuances purchased by

pension funds is not much larger than that of short-term issuances. Even when looking at

inflation-indexed bonds that would allow pensioners to smooth their real lifetime

consumption and obtain lower real return volatility, the share of bonds with maturities

above ten years purchased by pension funds is only 41% (compared to 39% for indexed

bonds with less than ten years of maturity). This observation is relevant because

government bonds are considered relatively safe investments.14

        Regarding corporate debt, there is no information on the amount of issuances

purchased by PFAs. However, we have data on the amount of corporate debt held by

PFAs as a proportion of the outstanding corporate debt and their average maturities.

Table 1 shows that PFAs hold on average 40% of outstanding corporate debt, declining

from 58% in 1997 to 28% in 2004. PFA holdings are tilted toward instruments with

shorter maturities. While the average maturity of the outstanding debt is about 13 years,

14
  During 2002-2008, the gross total debt of the Chilean government was 8.4% of GDP. Furthermore,
during the whole period Chile’s sovereign debt was considered investment grade, with rating Baa1 in 2002
and A2 in 2008.


                                                  18
the average maturity of the debt held by PFAs is only five years.15 Again, this type of

information suggests that PFAs have not been constrained to expand their holdings of

long-term bonds.16

        Although the previous evidence helps us explore whether any type of institutional

investor is exhausting the supply of long-term instruments, borrower decisions to issue

securities likely depend on the demand for different maturities. To shed more light on the

underlying demand of different institutional investors for securities with different

maturities, we use unique data on auctions of government paper. The data set consists of

detailed information on biddings for government bonds (in pesos, inflation-indexed

pesos, and US dollars of maturity one year or longer) issued by the Central Bank of Chile

and the Treasury between 2002 and 2009. The data come from the central bank, which

organizes these auctions. The data set has information on biddings made by banks,

insurance companies, and pension funds. Banks are likely to bid both for themselves and

other institutions, notably mutual funds and small insurance companies that do not bid

directly. This means that we cannot separately identify the bidding behavior of mutual

funds. For this reason, and in light of the similarities in maturity structure between

pension and mutual funds, we focus our analysis on the bids of pension funds. Although

we do not explicitly analyze the bank bids, they are included in the sample as a control

group. In total, the data set contains 1,185 auctions and 20,937 bids during 2002-2009.


15
   While there is no information on the amount of corporate debt issuances purchased by PFAs, we use data
on the corporate debt holdings of PFAs as a proportion of the outstanding corporate debt (from Braun and
Briones, 2008) and information on the average maturities of PFA corporate debt holdings compared to the
average maturity of outstanding corporate debt (from the Chilean Superintendency of Pensions and the
Superintendency of Securities and Insurance).
16
   With respect to the banking system, the proportion of certificate deposits held by PFAs has been very
stable, oscillating between 25% and 30%. But banking sector information is less relevant to assess the
extent to which PFAs might be constrained by instrument availability because banks can accept any amount
of deposits.


                                                   19
       With the auction data, we estimate how much pension funds request at different

maturities and what price they offer for each quantity requested. We also compare the

behavior of pension funds and large insurance companies. Table 2, Panel A shows the

results, indicating when the differences between requests at different maturities (within

institutions) are statistically significant. Estimated quantities are reported as a share of the

total auction amount. Panel B reports the ratio of the share requested by insurance

companies and pension funds at each maturity, in the same auction. When an investor is

not bidding for an issuance we impute a zero for the quantity requested, but the

estimations for prices only include information for those investors that present a bid. For

this reason, the ratios of shares reported in Panel B do not include the cases when pension

funds do not bid for a security, biasing the results against finding larger shares bid by

insurance companies.

       Table 2, Panel A shows that pension funds request larger shares of the issuance

than insurance companies at most maturities, except for 30 year instruments. The shares

requested by insurance companies increase monotonically with time to maturity. When

comparing prices offered by both institutional investors, pension funds offer significantly

higher prices for five and ten year bonds, while no differences are observed between

prices offered for 20 and 30 year bonds. Moreover, a larger amount bid for by pension

funds is expected since pension funds are significantly larger investors than insurance

companies. Therefore, the smaller request of 30 year bonds reflects a lower preference by

pension funds for those long-term instruments, especially considering that similar prices

are offered for them across institutional investors. Furthermore, although pension funds

typically ask for larger shares of issuances, the ratio between the quantities demanded by




                                              20
insurance companies and pension funds hits a trough for bonds of ten year maturity

(Panel B). In fact, insurance companies request 60% of the amount requested by pension

funds of 20 year bonds and more than three times that of 30 year bonds.

       The results in Table 2 show that pension funds bid for short-term instruments

more aggressively than insurance companies; their relative bids weaken with the maturity

of the bonds issued and even reverse for 30 year bonds. Moreover, the behavior of

pension funds does not seem to be driven by low returns on long-term bonds. In fact, as

shown below in Section 5.C, the long-term bonds yield substantial returns when

compared to short-term ones.

       Table 3 further exploits the auction data by analyzing the shares requested by each

type of pension fund of inflation-indexed bonds (Panel A) and inflation-indexed, nominal

peso, and dollars bonds (Panel B). The results show that even those funds with higher

average maturity bid less aggressively for 30 year bonds than insurance companies.

Therefore the results of Table 2 are not driven by the aggregation of all different types of

funds and by the funds with lower average maturities.

       To conclude, the evidence suggests that the short-termism of pension funds is not

significantly constrained by the supply side of instruments. Pension funds seem to

demand less heavily bonds with longer maturities and their demand seems to play an

important role in their short-termism.

5.B. Rebalancing

       Mutual funds and pension funds might hold a large fraction of short-term assets

for tactical purposes to respond opportunistically to shocks, rebalancing their portfolios

and taking advantage of good buying opportunities. This is known as “cash-in-the-




                                            21
market” pricing, and refers to the idea that holding liquidity is costly because less liquid

assets have higher expected returns, but agents may hold liquidity because on occasion

they are able to make a profit by buying assets at fire-sale prices (Allen and Gale, 1994,

1998; Allen and Carletti, 2008).

        To shed light on the rebalancing effects, we display the behavior of short-term

assets during crisis times. We focus on pension funds because we have exclusive access

to high frequency (daily) portfolio data.17 Pension funds experience significantly less

outflows than mutual funds (as we show later), thus they should be especially able to use

their liquidity to take advantage of market opportunities in turbulent times instead of

meeting redemptions.

        Several papers have proposed that crisis periods in emerging economies are

frequently related to a lack of liquidity, when foreigners sell assets at fire-sale prices

(Krugman, 1998; Aguiar and Gopinath, 2005). The natural question here is whether the

domestic investors, who know the domestic market and have deep pockets, are the ones

on the other side of those selloffs. To analyze changes in the short-term portfolio during

crises, we study the evolution of short-term assets held by pension funds during the Asian

and Russian crises of 1997-1998. This period was characterized by a significant degree of

volatility that is especially useful for our purposes. Also, the 1997-1998 crises had a

substantially larger impact in Chile than the 2008-2009 global financial crisis.




17
   In unreported results, we analyze many regulatory changes related to pension funds. Since these
regulatory changes have typically been announced in advance, PFAs could accumulate liquidity prior to the
deregulation to take advantage of such changes. In other words, if PFAs hold liquidity to take advantage of
investment opportunities we should expect an increase in short-term holdings before the limits change, and
a reduction after their implementation. We find that around the regulatory events the portfolio share of
short-term assets does not change significantly. Namely, we find no evidence of liquidity hoarding before
the regulatory changes.


                                                    22
         Figure 6, Panel A shows the evolution of short-term assets during the major crisis

period in the sample that affected Chile, indicating the dates of some of the main events

in international financial markets. The pattern of short-term asset holdings shows an

increase from an average of 2% one week before the Asian crisis hit the Republic of

Korea in November 1997 (with the downgrade of Korean debt) to more than 3% two

weeks afterward, and remains high for the rest of this turbulent period. If anything, the

evolution of short-term assets is more consistent with a flight-to-liquidity strategy than

with the hoarding of liquidity to take advantage of fire-sale asset prices.

         In principle, this pattern could be explained by a sudden inflow of capital into

pension funds. Because it takes time for fund managers to find prudent long-term

investments, in the short run the fund’s average maturity might decline (Ferson and

Warther, 1996) if managers have more capital to invest. However, the net inflows PFAs

received during this period were around the two-year mean, within two standard

deviations of the mean.18 Therefore, the patterns in Figure 6, Panel A, do not seem to be

significantly affected by sudden inflows of cash.19 Furthermore, to explore if the reaction

of PFAs was different from that of mutual funds and insurance companies, we run

difference-in-differences regressions around the 1998 Russian crisis (Figure 6, Panel




18
    In unreported, more general results, we do find some evidence partly consistent with the effect
documented by Ferson and Warther (1996). That is, in periods when pension funds receive large inflows,
the allocation to short maturities increases more. Moreover, using the auction data we find that bids on
short-term bonds tend to increase proportionally more (when net inflows are positive or above different
means) than the bids on other bonds. However, this effect is small and managers do not change the type of
bonds they demand in a significant way.
19
   Because pension funds do not guarantee a certain rate of return, during a crisis all pension funds could
be yielding a negative return, as indeed happened in 2008 when all funds with equity exposure suffered
large losses. Therefore, they are unlikely to be perceived as a safe heaven and thus witness more inflows
during crises. In fact, the flows into pension funds tend to be very stable, including during crises, because
they are mandatory savings.


                                                    23
B).20 Panel regressions show that the reactions of pension funds during the crisis do not

differ in statistical terms from those of mutual funds and insurance companies.

           In sum, while the evidence analyzed here does not explain the average large

holdings of short-term assets by pension funds, it illustrates whether pension funds use

their large short-term positions to take advantage of buying opportunities. The evidence

that short-term positions do not seem to decrease during the type of events analyzed here

seems inconsistent with pension funds holding liquid assets to act opportunistically.

5.C. Risk of Investment Instruments

           Standard models of asset allocation indicate that the portfolio composition of an

investor depends on the risk-return combination of the different assets available for

investment (Bajeux-Besnainou, et al., 2001; Campbell et al., 2001; and Campbell and

Viceira, 2002). Thus, in principle, the short maturity structure of Chilean asset managers

could result from the risk profiles of the assets in which they invest. We explore next

some of the risks involved.

           A first potential explanation related to risk is that inflation risk could tilt

portfolios toward shorter maturities. Inflation movements are difficult to predict in the

long term, adding extra risk to the price of bonds with longer maturities. In other words,

the comparisons presented above could be misleading given that they aggregate all of the

fixed-term instruments held by Chilean mutual and pension funds, including those in

different currencies. To address this issue and shed light on how risk might be affecting

managerial decisions, we report the maturity structure of portfolios by currency,

separating the holdings in nominal pesos, “hard currencies” (US dollar, euro, British

pound, and yen), and indexed pesos (inflation-linked).
20
     We do not have data on mutual funds and insurance companies during the Asian crisis.


                                                     24
        Figure 7 shows the maturity structure of mutual funds and pension funds by

currency. In the case of mutual funds, the maturity structure of holdings in pesos is

similar to that in hard currencies (with holdings in pesos slightly longer), while the

maturity structure of holdings in inflation-linked pesos is significantly longer. In the case

of pension funds, the maturity structure differs significantly across currencies. Pension

funds are very short-term investors in pesos. For example, 55% (78%) of peso holdings

are held in instruments maturing in less than one (three) year(s). They are a little bit less

short term in hard-currency assets; 46% (91%) are in assets maturing in less than one

(three) year(s). On the contrary, they are more long term in indexed pesos instruments.

For example, about 30% (47%) are held in instruments with maturity lower than one

(three) year(s). The differences in the distributions are statistically significant, as shown

by the KS tests displayed in Panel E.

        The patterns illustrated in Figure 7 are consistent with pension and mutual funds

being more tilted toward the short term in assets with higher long-term risk. The price of

nominal peso instruments responds to inflation volatility, which tends to increase with the

maturity of the bond, perhaps explaining the short-term structure. Investors would be

more willing to go long in hard currencies than in Chilean pesos if holding hard

currencies allowed investors to hedge part of the inflation risk, which does not seem to be

strongly the case in Chile.21 Not being exposed to currency or inflation risk, indexed peso

bonds are relatively less risky than peso and hard-currency bonds, especially at longer

maturities, which could account for the willingness of Chilean investors to buy more

long-term indexed peso instruments. Therefore, for some types of instruments, asset

21
  While the correlation between monthly inflation and depreciations of the Chilean peso against the US
dollar between 1990 and 2008 is about 0.17, the correlations between annual and bi-annual inflation and
depreciations are 0.35 and 0.49, respectively.


                                                  25
managers might perceive a tradeoff between maturity, on the one hand, and currency and

inflation risks, on the other hand. When managers can reduce those risks, they seem more

willing to invest more long term. Still, the evidence shown here suggests that mutual and

pension funds hold a significant fraction of short-term assets even when risks are

reduced. For example, the average maturity for holdings of indexed peso bonds is 7.15

and 5.31 years for mutual and pension funds, respectively (compared to the average

maturity of insurance companies, of 9.77 years, when holding indexed and non-indexed

instruments).

         In addition to the risk of different investment instruments, there exist the risks of

investing at different maturities. Available evidence from other emerging economies

suggests that, if anything, investors in emerging economies should tilt their portfolios

toward the long term relative to investors in developed countries.22 Here, we complement

the existing evidence by compiling new data on prices of inflation-indexed government

bonds at different maturities, measured, alternatively, by indices of traded bonds at

different maturity buckets and indices derived from a model-based estimation of the yield

curve.23 We compute average returns, standard deviations, and Sharpe ratios (average

returns over standard deviations) for securities of different maturities over different

holding periods. These estimates are useful because, assuming zero covariance across


22
   Broner et al. (2013) compute Sharpe ratios of short- and long-term bonds in various emerging economies
(excluding Chile) and show that, on average, the difference in the Sharpe ratio of long- and short-term
bonds is higher in emerging countries than in developed countries. Moreover, those estimates have the
advantage that they are computed with much longer time series. In our case, we only have a five-year
sample, which might be short for this type of calculation. Still, the estimates in both papers lead to the same
conclusions.
23
   We focus on inflation-indexed securities because, as discussed above, they are the ones that better allow
investors to reduce risk and invest more long term. On a more pragmatic note, bonds issued by the central
bank at maturities beyond ten years are almost exclusively inflation indexed. The price information comes
from RiskAmerica, a private company that provides fair value pricing for the Chilean fixed income market.
These prices are widely used by institutional investors that mark their portfolios to market.


                                                     26
bonds of different maturities, portfolios should be proportional to the Sharpe ratios.

Although this is a strong assumption and more complex analysis is needed (Litterman

and Scheinkman, 1991), these Sharpe ratios give a first approximation to the topic and

allow us to compare the results to other papers that have used them.

       Figure 8 shows that, as expected, investing in long-term bonds yields higher

returns, albeit at a higher risk. For example, over a holding period of three months,

annualized returns for the five year bond index is approximately 4%, in contrast to 7% for

the 15 year bond index. Standard deviations also rise with longer maturities, being 4%

and 7% for five and 15 year bond indices respectively, also considering a holding period

of three months. In nearly all cases, we observe higher standard deviations when we

decrease the holding period, especially so for the longer-term maturities.

       The estimates also suggest that, given the risk-return tradeoff, investors with a

short-run horizon have more incentives to invest in short-term instruments relative to

investors with a long-term horizon. For example, Sharpe ratios for bond indices present a

flat structure along different maturities for short holding periods but tend to increase with

the maturities for longer holding periods. Similarly, Sharpe ratios obtained from the

model of the yield curve strongly decline with maturity for short holding periods (except

for maturities below three years) but are relatively flat for longer holding periods.

Regardless of the maturity, Sharpe ratios are larger for longer holding periods, but

especially so for longer maturities. This evidence suggests that, given the risk-return

profile of Chilean securities, the portfolios of investors with short horizons will be more

biased toward short-term securities than those of investors with long horizons. Moreover,




                                             27
the fact that they choose to hold short portfolios suggests that the risk they face by going

to longer horizons are larger than any extra risk-adjusted return they might obtain.24

        Aside from the price risk, one potential additional risk is that of liquidity. A

possible concern related to liquidity is that if prices for long-term bonds are not available,

mutual funds and pension funds that need to mark their portfolios to market each day to

calculate their net asset values (NAVs) may avoid debt for which market prices are not

readily available. Therefore, the need to calculate daily NAVs might lead to a preference

for shorter-maturity bonds whose market prices can be better estimated. However, in the

Chilean case there exist official price providers who value the different assets in the

market on a daily basis, including short- and long-term bonds. Therefore, PFAs and

mutual funds can use those prices to mark their portfolios to market. Moreover, the bonds

we analyze at different maturities are liquid enough to provide price signals for investors

that need to mark to market their portfolios.

        Another aspect of liquidity that can matter for the results is related to the fact that

bonds of different maturities might have different liquidity. In unreported results, we

compute two measures of liquidity, the turnover ratio and the “conventional liquidity

ratio” (Gabrielsen et. al, 2011) for Chilean nominal and indexed peso bonds using data

obtained directly from the Central Bank of Chile. The results for government bonds

suggest that short-term bonds are more liquid than long-term bonds, although the relation

is not monotonic. However, according to both of these estimations and market


24
   In unreported results, we simulate the returns and risks that pension funds would face in the case they
took longer portfolios. In each simulation we increase the average maturity of all funds by lowering the
shares of instruments with maturity lower than three years and increasing proportionally the shares of the
rest. In all cases we do not change the total investment of each fund. The results show that the average
returns and standard deviations increase with the average maturity of the portfolio. However, the standard
deviations grow faster than the returns. Given the short-term holding period that PFAs seem to have, these
results are consistent with those presented in Figure 8.


                                                   28
participants, long-term bonds are still fairly liquid. The results for corporate bonds

suggest that their liquidity does not decrease with the maturity. For example, the turnover

ratio is higher for bonds with initial maturities of 20 years than for short-term bonds. The

difference between these results seems to come from the fact that government bonds

include instruments issued by the central bank to conduct monetary policy, while

corporate bonds are just instruments to finance corporate investment. The finding that

long-term corporate bonds are more liquid than long-term government bonds might also

explain another fact, that PFAs hold longer-term corporate bonds than government bonds.

For 2002-2008, the average maturity of their corporate (government) bond holdings is

10.57 (5.47) years. Therefore, the liquidity of different bonds might explain to some

extent part of the results and the risk of holding bonds of different maturities.

       Taken as a whole, the evidence from Section 5.C suggests that the risk profile of

the available investment opportunities might affect the degree of short-termism of mutual

and pension funds. Institutional investors in Chile are sensitive to the risks involved in

investing in different instruments. However, as we analyze next, these risks affect

managers depending on the incentives they face.

5.D. Managerial Incentives: Market and Regulatory Monitoring

       In the context of financial intermediation and principal-agent problems, short-run

monitoring might affect manager incentives. In the case of open-end funds like pension

funds and mutual funds, managers are monitored in the short run by the underlying

investors (which can redeem their shares from the open-end funds), the regulator (which

imposes penalties in the case of pension funds when a fund deviates from the industry

average), and the asset-management companies (which tend to set compensation based on




                                             29
performance relative to the peers). This monitoring might lead to short-run investment

horizons and holdings of short-term instruments, because the higher volatility of long-

term assets poses additional risk of generating outflows. In other words, short-run

monitoring generates incentives for managers to be averse to investments that are

profitable at long horizons (like holding long-term bonds) but can have poor short-term

performance and let managers away from their competitors (Stein, 2005). If the risk of

long-term investment is large, it would be difficult to deviate from an equilibrium in

which all managers hold short-term assets. Historical reasons linked to high volatility and

the desire to have stability in the assets managed by these funds might have pushed the

equilibrium to the short term.

       One way to study the degree of market monitoring is to analyze the flows to the

funds and link them to performance at the fund level. The extent to which flows respond

to performance will also likely affect how managers respond to performance. We study

these two effects in turn.

       To study the relation between the flows to the funds and performance, we first

compute the outflow (negative inflow) that each fund faces each month. We calculate the

net inflows to a fund k at time t , I tk , as the change in the fund value Wt k during a month,

adjusted by the gross return of the portfolio in that month Rtk :

(4)                                    I tk  Wt k  Wt 
                                                        k
                                                          1 (1  Rt )
                                                                   k
                                                                      .




                                                30
We use this method to calculate net inflows to mutual funds. For pension funds, we

compute this measure by aggregating daily data on net inflows into each fund, directly

collected by the Chilean Superintendency of Pensions.25

         The results are displayed in Figure 9. Panel A shows the cumulative distribution

of net inflows I tk relative to fixed-income assets for Chilean mutual funds and PFAs. As

a benchmark, we also report those of US mutual funds. Negative (positive) values are

outflows (inflows). The figure shows that Chilean mutual funds face significant outflows.

For example, the historical probability of experiencing a net outflow of 3% of the

portfolio or more is 33%. To complement this evidence, Panel B shows the fraction of

fixed-term assets held in short-term assets (up to one and three months) and the

probability of outflows of that magnitude.26 Chilean mutual funds hold 9.3% of their

fixed-term assets in instruments with maturity of less than one month, and the probability

of an outflow of that magnitude occurring is almost 22%. US multi-sector bond funds are

subject to fewer outflows. For example, the historical probability of experiencing a net

outflow of 3% of the portfolio or more is 9% (instead of 33%). Therefore, the short-

termism of Chilean mutual funds might be partly explained by the relatively large

outflows they face.

         Chilean pension funds, on the contrary, are not exposed to significant outflows.

The distribution of net inflows of Chilean PFAs is significantly tilted to the right. So

redemption risk does not seem to be an important factor explaining pension funds’ short-

25
   Though not reported, we also compute the monthly inflows using the values and returns derived from our
monthly database and obtain qualitatively similar results.
26
   The values reported correspond to the probability that would be required to have a value at risk (VAR)
equal to the fraction of fixed-term assets held by funds at maturities of up to 30 and 90 days. For US funds,
we do not have information on the maturity structure at less than three years, so we use the extreme
assumption that within the zero to three year interval, the maturity structure of US funds is proportional to
that of Chilean mutual funds.


                                                     31
term holdings. For example, a net outflow of 1% of the portfolio has a historical

probability of 3% for PFAs and 38% for mutual funds. Though they face very different

outflows, the short-term positions of mutual funds and pension funds are not very

different, as shown in Figure 4.27 The estimations reported in Figure 9, Panel B also show

that pension funds seem to hold a large fraction of liquid assets for low-probability

events: they hold 4.4% of their fixed-term assets in instruments with a maturity of less

than one month, while the probability of an outflow of that magnitude is negligible. To

the extent that there is an opportunity cost of holding short-term instruments, pension

funds are paying a high price for their elevated self-insurance levels.

        Because mutual funds are subject to significant outflows, we analyze whether

these outflows are related to performance, as a sign of short-run monitoring that generates

incentives for managers (Lim et al., 2013). Table 4 shows the relation between outflows

and returns. Indeed, outflows are associated with short-term returns. A positive (negative)

return relative to the industry from a previous month is related to an inflow (outflow) into

the mutual fund. Because the results are robust to controlling for time (and fund) effects,

they are not capturing positive flows to all funds in good times or vice versa (although

these flows could be consistent with short-run monitoring). The short-term relation

vanishes when we use a longer-term horizon. This indicates that, while outflows respond

to short-run performance, they do not seem to be affected by the long-term returns

generated by a fund. Though not reported, the relation is never statistically significant for




27
   This is even more striking when one considers that mutual fund redemptions can be systemic aside from
idiosyncratic (investors may massively pull out of all mutual funds when market conditions worsen). The
systemic nature of mutual fund redemptions makes liquidations by mutual funds more costly as all funds
liquidate their positions at the same time.


                                                  32
pension funds.28 These findings are consistent with studies that analyze US data (Ippolito,

1992; Sirri and Tufano, 1998; Del Guercio and Tkac, 2002).

         Although the standard regulation applied to pension funds might complement the

market forces that monitor asset managers on a short-run (monthly) basis, it does not

indicate to any degree whether pension funds should invest long term or short term.

Instead, pension funds are required to yield returns within established margins. Thereby,

pension fund managers are penalized by regulations when they deviate from industry

standards, having to cover these losses with their own capital. These regulations might

also help explain why the companies that run the PFAs seem to monitor their managers

through tracking error models that constrain them to be close to the average pension fund

(Roll, 1992; Castañeda and Rudolph, 2009). While this regulatory discipline might

generate herding among pension funds, the evidence on its effects is mixed (Raddatz and

Schmukler, 2013) and it is not clear that herding should necessarily occur at the short-end

of the maturity spectrum.

         Next, we test how mutual and pension funds react when they underperform,

following a large literature based on the US that finds mixed evidence (Brown et al.,

1996; Busse, 2001; Goriaev et al., 2005; Chen and Pennacchi, 2009). The idea is that

even when mutual and pension funds might choose a low volatility portfolio on average

to try to minimize withdrawals, it could still be the case that when their returns are low,

they move to more risky allocations.



28
   In the case of pension funds, for regulatory reasons PFAs send monthly reports of their real returns to
future pensioners and must base their publicity on real returns. Thus, the reduction in real returns resulting
from inflation can potentially affect their ability to capture new affiliates or generate outflows. Although
there is not much evidence that the number of affiliates changes with returns, the ranking of PFAs by
returns (typically used in advertisements) seems to be positively correlated with the number of affiliates
across PFAs (Cerda, 2005).


                                                     33
       In order to test how the Chilean mutual funds behave when they underperform,

we analyze the reactions of the mutual fund managers whose funds have a return lower

than the market average (in the previous month or over the previous six months) or a

negative return. We do find evidence that mutual funds take more risk when their

performance lags behind. In all cases, we observe that these managers tend to increase the

average maturity of their portfolio (Table 5, Panel A).

       When performing the same analysis for the case of PFAs, we find different results

(Table 6, Panel A). When pension funds have a return lower than the market, they tend to

move their portfolio more toward the short term. When returns are negative, they do not

react. This suggests that only relative returns (not absolute ones) are important for PFA

managers. In other words, if managers face negative returns but so does the market, then

managers do not need to cut their risk exposure by moving to the short term. But when

they lag other market participants, they try to avoid a further decline by moving to the

short term. Furthermore, we find that the shortening of the maturity structure for pension

funds comes from a switch in the types of instruments they hold (Table 6, Panel B). They

increase their holdings of deposits and reduce their holdings of government and corporate

debt, which have a longer term to maturity. In unreported results using the actual

holdings by type of asset or the bids on government bonds, we do not observe that they

change the maturity structure within asset class.

       In alternative tests, we run a simplified version of the Brown et al. (1996) test. In

particular, we analyze the reactions of mutual fund managers whose funds have a return

lower than the market average in the first semester of each year, to see if they try to

compensate for this poor performance later in the year. Thus, we compute the ratio




                                            34
between the standard deviation of the returns in the second part of the year relative to the

first part of the year. Although we find that mutual funds that underperform the market in

the first semester do have on average higher ratios in the second half of the year, we do

not find evidence that these differences are statistically significant (Table 5, Panel B). For

pension funds we find, instead, that those funds that outperform the market in the first

semester tend to have higher ratios in the second half of the year than those funds with

lower returns in the first semester. These differences are statistically significant (Table 6,

Panel C).

       The evidence suggests that incentives work differently for mutual funds and

pension funds. For the former, when they are not performing well they seem to have

incentives to take more risk to try to catch up with the other funds. This evidence is

consistent with part of the results in the US literature (Brown et al., 1996). On the

contrary, pension funds are penalized if they do worse than the others and are not much

rewarded when they do well. Therefore, when their performance lags with respect to the

rest of the market (not in absolute terms), they cut their exposure to risk to avoid falling

below the minimum return band (most pension funds are always within this band). They

only take risk when it is safe to do so, when they are performing relatively well.

       The bidding behavior of PFAs for Chilean peso, indexed Chilean peso, and US

dollar denominated bonds seems to corroborate that PFAs do not want to deviate from

market standards (Table 7). If for any reason, in the past month a PFA ends up with more

exposure to US dollar instruments (measured as US dollars instruments relative to total

investments) than the market average, then the PFA bids more aggressively for Chilean




                                             35
peso and indexed peso instruments and less aggressively for US dollar bonds, thus

reducing its exposure and converging to the market equilibrium.

       Whereas the type of short-run monitoring analyzed here can play a role in open-

end funds, it is unlikely to affect insurance companies. The latter are not evaluated on a

short-term return basis by investors that can redeem their shares on demand and they are

not required to be close to the industry at each point in time. Instead, the maturity

structure of the insurance companies’ assets seems to be determined by that of their

liabilities. Insurance companies have long-term liabilities because they mostly provide

annuities to pensioners. Thus, the need to meet these liabilities gives them incentives to

hold long-term assets. In contrast, mutual funds and pension funds are pure asset

managers and have no liabilities beyond their fiduciary responsibility.

       In sum, the long-term nature of their liabilities shapes the incentives of the

insurance companies toward portfolios with longer maturities. In contrast, given the lack

of a liability structure, the incentives of Chilean pension and mutual funds to take

maturity risk are determined mainly by the constant monitoring exerted by the underlying

investors, their own companies, and the regulator. The discussion in this section provides

evidence that such monitoring behavior focuses on short-term performance and seems to

discourage investments in more volatile long-term instruments. Moreover, the fact that

mutual fund flows are related to performance suggests that some disciplining device

might be operating, and as consequence fund managers react by changing the degree to

which they invest short term and the risk they take. These effects are consistent with the

existence of a principal-agent problem. In the case of pension funds, though they are

subject to fewer outflows, the regulation reinforces the market mechanism by punishing




                                            36
those funds that underperform relative to a minimum return that depends on the market

average.

       The comparison of the maturity structure of insurance companies and pension

funds is particularly illuminating because in principle both should be long-term investors:

insurance companies provide mainly long-term annuities for retirement while pension

funds invest for the retirement of their affiliates. Indeed, upon retirement an individual

can choose between buying an annuity or keeping his/her assets in a pension fund and

gradually drawing the principal according to a program that considers expected longevity.

Despite the similarity in their implicit operational goals, pension funds have no explicit

liabilities and face regulation that punishes short-run deviations from their peers, which

gives them very different incentives to invest in long-term assets. Still, further work is

required to analyze the optimal portfolio structure of each type of investor by

incorporating equities, among other things.



6. Conclusions

       Many developing countries are in the quest of developing markets for long-term

financing. Chile has been at the forefront in these efforts. It has already relatively well

developed capital markets and has tried to extend debt maturities through a broad range

of macroeconomic and financial sector reforms. One notable feature has been the

establishment and growth of sophisticated institutional investors by, among other things,

reforming and introducing a new pension system in 1981, which has been emulated by

many countries. These investors are expected to invest long term, especially those

focused on providing funds for retirement.




                                              37
       Using the rich experience of Chile, this paper studies to what extent institutional

investors invest long term and the factors that might affect their decisions to hold assets

at different maturities. The paper finds that, despite all the favorable conditions, Chilean

asset managers (mutual and pension funds) are significantly tilted toward the short-term

end of the country’s maturity structure, with a large portion of their portfolio in assets

with maturities of less than one year. In contrast, insurance companies invest much more

long term. This substantial difference across investors within the same country helps us

understand what might be behind their short- and long-term investments.

       The evidence in this paper is inconsistent with two hypotheses as determinants of

the maturity structure. First, asset managers choose short-term instruments even when

assets for long-term investments are widely available and when other investors hold

them. That is, the supply side of instruments does not pose a mechanical constraint. On

the contrary, the investor side (the supply side of funds) seems essential to understand

debt maturity structures, which is consistent with the preferred habitat and investor

clientele literature (Guibaud et al., 2013). Second, evidence from pension funds suggests

that institutional investors do not hold short-term instruments for tactical reasons, to take

advantage of buying opportunities and purchase assets at fire sale prices.

       At least two factors seem to interact and play an important role in shaping

investor demand and, consequently, the maturity structure of institutional investors: the

risk profile of the available instruments and manager incentives linked to short-term

monitoring and the liability structure. Mutual and pension funds invest more long term in

less volatile indexed instruments. Moreover, mutual and pension funds hold a large

proportion of the less risky short-term instruments, even when they yield low returns.




                                             38
Managers forgo higher returns by not investing long term, especially as their investment

horizon expands.

       The incentive structure also seems to explain the short-term investment horizon of

mutual and pension funds in different ways. The evidence in this paper suggests that

incentives due to principal-agent problems might lead to investment in short-term

instruments. The two types of incentives that appear to be relevant are: short-run

monitoring (by investors or the regulator) and the liability structure of asset managers.

First, mutual funds are subject to substantial investor redemptions related to short-run

performance. In the case of pension funds, the short-run monitoring is exercised by

common regulatory practices that punish funds that deviate from industry averages and

by the owners of the asset-management companies. The short-term monitoring that

mutual and pension funds face seems to generate short-term investment horizons and,

given that the risk profile of the available instruments yields decreasing Sharpe ratios for

short-term holding periods, it becomes natural for them to invest more heavily in short-

term bonds. Second, mutual funds and pension funds do not have liabilities, and thus

have incentives to invest in short-term assets that are less risky and, as a consequence,

reduce the likelihood of deviating from their peers. In contrast, insurance companies have

long-term liabilities and, as a result, their maturity structure is significantly more long

term. In other words, given that asset managers and asset-liability managers face the

same investment opportunities, the evidence in this paper highlights the importance of

incentives in shifting the maturity structure.

       A key policy lesson from our findings is that, despite the benefits of long-term

debt, many economies like that of Chile might face an uphill effort in extending debt




                                                 39
maturities, even when many of the ex-ante conditions are in place. In particular,

extending debt maturities by just developing institutional investors such as mutual and

pension funds seems difficult to achieve and runs contrary to many of the initial

expectations behind the promotion of these market players. Merely establishing asset-

management institutions and assuming that managers will invest long term does not

appear to yield the expected outcome, especially because they would involve a similar

type of market and regulatory short-term monitoring as that in Chile.

        There seems to be an important tradeoff between monitoring managers according

to their short-term performance (which leads to short-term investments) and obtaining

higher returns by investing long term (at the cost of higher risks). This has important

social consequences given the large retirement savings managed by these institutional

investors. In fact, some discussions have started to emerge in Chile and internationally

about the pension system and how to reform it given the lower than expected replacement

rates.29 However, the socially optimal design to balance this tradeoff is not obvious

(Acemoglu et al., 2007) and requires further work. Policymakers in Chile have tried to

make the system more conducive for long-term investments, including regulatory

changes, but so far they have not been able to shift the equilibrium out of the short term.

Moreover, pensioners seem reluctant to see the value of their assets decline as a

consequence of risk taking. Additional work is needed to understand whether the

observed asset allocation of debt of different maturities among the various institutional

and retail investors is socially inefficient, to what extent long-term debt is socially


29
   According to some estimates, the amount in the average 65 year old pensioner account is 55,000 US
dollars. Given the expected remaining life of 15 years, this is equivalent to about 310 US dollars per month
or one third of the average salary in Chile. E.g., http://www.elmercurio.com/blogs/2014/01/14/18619/Un-
mejor-sistema-de-pensiones.aspx. For an international discussion, see Stewart (2014).


                                                    40
desirable, and how incentives could change to shift the investment horizon of institutional

investors.




                                            41
References

Acemoglu, D., M. Kremer, and A. Mian, 2007. Incentives in Markets, Firms, and
   Governments, Journal of Law, Economics, and Organization 24(2), 273-306.
Aguiar, M. and G. Gopinath, 2005. Fire-sale Foreign Direct Investment and Liquidity
   Crises, Review of Economics and Statistics 87(3), 439-452.
Alfaro, L. and F. Kanczuk, 2009. Debt Maturity: Is Long-term Debt Optimal? Review of
   International Economics 17(5), 890-905.
Allen, F. and E. Carletti, 2008. Mark-to-market Accounting and Liquidity Pricing,
   Journal of Accounting and Economics 45(2-3), 358-378.
Allen, F. and D. Gale, 1994. Limited Market Participation and Volatility of Asset Prices,
   American Economic Review 84(4), 933-955.
Allen, F. and D. Gale, 1998. Optimal Financial Crises, Journal of Finance 53(4), 1245-
   1284.
Bajeux-Besnainou I., J. Jordan, and R. Portait, 2001. An Asset Allocation Puzzle:
   Comment, American Economic Review 91(4), 1170-1179.
Bank for International Settlements (BIS), 2007. Financial Stability and Local Currency
   Bond Markets, CGFS Papers 28.
Bebchuk, L. and L. Stole, 1993. Do Short-term Objectives Lead to Under- or
   Overinvestment in Long-term Projects? Journal of Finance 48(2), 719-729.
Benjamini, Y. and D. Yekutieli, 2001. The Control of the False Discovery Rate in
   Multiple Testing under Dependency, Annals of Statistics 29(4), 1165-1188.
Bolton, P., J. Scheinkman, and W. Xiong, 2006. Executive Compensation and Short-
   termist Behavior in Speculative Markets, Review of Economic Studies 73(3), 577-610.
Borensztein, E., M. Chamon, O. Jeanne, P. Mauro, and J. Zettelmeyer, 2005. Sovereign
   Debt Structure for Crisis Prevention, IMF Occasional Paper 237.
Borensztein, E., K. Cowan, B. Eichengreen, and U. Panizza (Eds.), 2008. Bond Markets
   in Latin America: On the Verge of a Big Bang? MIT Press, Cambridge, MA.
Borensztein, E. and G. Gelos, 2003. A Panic-prone Pack? The Behavior of Emerging
   Market Mutual Funds, IMF Staff Papers 50(1), 43-63.
Braun, M. and I. Briones, 2008. Development of the Chilean Corporate Bond Market, in
   Borensztein, E., K. Cowan, B. Eichengreen, and U. Panizza (Eds.), Bond Markets in
   Latin America: On the Verge of a Big Bang? 151-184, MIT Press, Cambridge, MA.
Broner, F., G. Gelos, and C. Reinhart, 2006. When in Peril, Retrench: Testing the
   Portfolio Channel of Contagion, Journal of International Economics 69(1), 203-230.
Broner, F., G. Lorenzoni, and S. Schmukler, 2013. Why Do Emerging Economies
   Borrow Short Term? Journal of the European Economic Association, 11(1), 67-100.
Brown, K., V. Harlow, and L. Starks, 1996. Of Tournaments and Temptations: An
   Analysis of Managerial Incentives in the Mutual Fund Industry, Journal of Finance
   51 (1), 85-110.
Brunnermeier, M., 2009. Deciphering the Liquidity and Credit Crunch 2007-2008,
   Journal of Economic Perspectives 23(1), 77-100.
Busse, J., 2001. Another Look at Mutual Fund Tournaments, Journal of Financial and
   Quantitative Analysis 36, 53-73.




                                           42
Calomiris, C., 2008. The Subprime Turmoil, What’s Old, What’s New, and What’s Next,
    in Maintaining Stability in a Changing Financial System, Federal Reserve Bank of
    Kansas City Jackson Hole Symposium.
Calomiris, C., 2011. An Incentive-robust Programme for Financial Reform, The
    Manchester School, 39-72.
Campbell, J., M. Lettau, B. Malkiel, and Y. Xu, 2001. Have Individual Stocks Become
    More Volatile? An Empirical Exploration of Idiosyncratic Risk, Journal of Finance
    56(1), 1-43.
Campbell, J. and L.M. Viceira, 2002. Strategic Asset Allocation: Portfolio Choice for
    Long-Term Investors, Oxford University Press, Oxford.
Caprio, G. and A. Demirguc-Kunt, 1998. The Role of Long-term Finance: Theory and
    Evidence, World Bank Research Observer 13(2), 171-189.
Castañeda, P. and H. Rudolph, 2009, Portfolio Choice, Minimum Return Guarantees, and
    Competition in DC Pension Systems, SSRN Working Paper 1405411.
Cerda, R., 2005. Does Social Security Affect Retirement and Labor Supply? Evidence
    from Chile, Developing Economies 43(2), 235-264.
Chen, H. and G. Pennacchi, 2009. Does Prior Performance Affect a Mutual Fund’s
    Choice of Risk? Theory and Further Empirical Evidence, Journal of Financial and
    Quantitative Analysis 44(4), 745-775.
Chevalier, J. and G. Ellison, 1999. Career Concerns of Mutual Fund Managers, Quarterly
    Journal of Economics 114(2), 389-432.
Corbo, V. and K. Schmidt-Hebbel, 2003. Macroeconomic Effects of the Pension Reform
    in Chile, in Pension Reforms: Results and Challenges, 241-329, International
    Federation of Pension Fund Administrators, Santiago, Chile.
Cuesta-Albertos, J.A., R. Fraiman, A. Galves, J. Garcia, and M. Svarc, 2007. Identifying
    Rhythmic Classes of Languages Using Their Sonority: A Kolmogorov-Smirnov
    Approach, Journal of Applied Statistics 34(6), 749-761.
Cuesta-Albertos, J.A., R. Fraiman, and T. Ransford, 2006. Random Projections and
    Goodness-of-fit Tests in Infinite-dimensional Spaces, Bulletin of the Brazilian
    Mathematical Society 37(4), 1-25.
Davis, E.P., 1995. Pension Funds: Retirement Income Security and Capital Markets-An
    International Perspective, Oxford University Press.
Davis, E.P. and B. Steil, 2001. Institutional Investors, MIT Press, Cambridge, MA.
de la Torre, A., A. Ize, and S. Schmukler, 2011. Financial Development in Latin America
    and the Caribbean: The Road Ahead, World Bank.
Del Guercio, D. and P. Tkac, 2002. The Determinants of the Flows of Funds of Managed
    Portfolios: Mutual Funds vs. Pension Funds, Journal of Financial and Quantitative
    Analysis 37(4), 523-557.
Della Croce, R., F. Stewart, and J. Yermo, 2011. Promoting Longer-term Investment by
    Institutional Investors: Selected Issues and Policies, OECD Journal: Financial
    Market Trends 1, 1-20.
Diamond, D. and R. Rajan, 2001. Banks, Short-term Debt and Financial Crises: Theory,
    Policy Implications, and Applications, Carnegie-Rochester Conference Series on
    Public Policy 54(1), 37-71.




                                          43
Didier, T., R. Rigobon, and S. Schmukler, 2013. Unexploited Gains from International
    Diversification: Patterns of Portfolio Holdings around the World, Review of
    Economics and Statistics, forthcoming.
Eichengreen, B., 2009. The Dollar Dilemma: The World’s Top Currency Faces
    Competition, Foreign Affairs, September/October 88 (5), 53-68.
Eichengreen, B. and R. Hausmann, 1999. Exchange Rates and Financial Fragility,
    Proceedings, Federal Reserve Bank of Kansas City, 329-368.
Fernandez, V, 2006. Emerging Derivatives Markets: The Case of Chile, Emerging
    Markets Finance and Trade 42 (2), 63-92.
Ferson, W. and V. Warther, 1996. Evaluating Fund Performance in a Dynamic Market,
    Financial Analysts Journal 52(6), 20-28.
Gabrielsen, A., M. Marzo, and P. Zagaglia, 2011. Measuring Market Liquidity: An
    Introductory Survey, MPRA Paper 35829, University Library of Munich.
Gelos, G. and S.J. Wei, 2005. Transparency and International Portfolio Holdings. Journal
    of Finance 60(6), 2,987-3,020.
Grinblatt, M. and M. Keloharju, 2000. The Investment Behavior and Performance of
    Various Investor Types: A Study of Finland’s Unique Data Set, Journal of Financial
    Economics 55(1), 43-67.
Goriaev, A., T. Nijman, and B. Werker, 2005. Yet Another Look at Mutual Fund
    Tournaments, Journal of Empirical Finance 12, 127-137.
Guibaud, S., Y. Nosbusch, and Vayanos, D., 2013. Bond Market Clienteles, the Yield
    Curve, and the Optimal Maturity Structure of Government Debt, Review of Financial
    Studies 26(8), 1914-1961.
Hau, H. and H. Rey, 2008. Home Bias at the Fund Level, American Economic Review
    98(2), 333-38.
Impavido, G., E. Lasagabaster, and M. Garcia-Huitron, 2010. Competition and Asset
    Allocation Challenges for Mandatory DC Pensions: New Policy Directions,
    Brookings Institution and World Bank.
Impavido, G., A.R. Musalem, and T. Tressel, 2003. The Impact of Contractual Savings
    Institutions on Securities Markets, World Bank Policy Research Paper 2948.
Ippolito, R.A., 1992. Consumer Reaction to Measures of Poor Quality: Evidence from the
    Mutual Fund Industry, Journal of Law and Economics 35(1), 45-70.
Jotikasthira, C., C. Lundblad, and T., Ramadorai, 2012. Asset Fire Sales and Purchases
    and the International Transmission of Financial Shocks, Journal of Finance 67(6),
    2015-2050.
Jeanne, O., 2009. Debt Maturity and the International Financial Architecture, American
    Economic Review 99(5), 2135-48.
Kaminsky, G., R. Lyons, and S. Schmukler, 2004. Managers, Investors, and Crises:
    Mutual Fund Strategies in Emerging Markets, Journal of International Economics
    64(1), 113-134.
Kapur, S. and A. Timmermann, 2005. Relative Performance Evaluation Contracts and
    Asset Market Equilibrium, Economic Journal 115(506), 1,077-1,102.
Kim, W. and S.J. Wei, 2002. Offshore Investment Funds: Monsters in Emerging
    Markets? Journal of Development Economics 68(1), 205-224.
Krugman, P., 1998. Fire-sale FDI, http://web.mit.edu/krugman/www/FIRESALE.htm.




                                          44
Lim, J., B. Sensoy, and M. Weisbach, 2013. Indirect Incentives of Hedge Fund
    Managers, NBER Working Paper 18903.
Litterman, R. and J. Scheinkman, 1991. Common Factors Affecting Bond Returns,
    Journal of Fixed Income 1(1), 54-61.
Narayanan, M.P., 1985. Managerial Incentives for Short-term Results, Journal of
    Finance 40(5), 1469-1484.
Organization for Economic Cooperation and Development (OECD), 2013a. The Role of
    Banks, Equity Markets, and Institutional Investors in Long-term Financing for
    Growth and Development. Report for G20 Leaders.
Organization for Economic Cooperation and Development (OECD), 2013b. Annual
    Survey of Large Pension Funds and Public Pension Reserve Funds. Report on
    Pension Funds’ Long-term Investments
Pennachi, G. and M. Rastad, 2011. Portfolio Allocation for Public Pension Funds.
    Journal of Pension Economics and Finance 10(2), 221-245.
Raddatz, C., 2010. When the Rivers Run Dry: Liquidity and the Use of Wholesale Funds
    in the Transmission of the US Subprime Crisis, World Bank Policy Research
    Working Paper 5203.
Raddatz, C. and S. Schmukler, 2012. On the International Transmission of Shocks:
    Micro-evidence from Mutual Fund Portfolios, Journal of International Economics,
    88(2), 357-374.
Raddatz, C. and S. Schmukler, 2013. Deconstructing Herding: Evidence from Pension
    Fund Investment Behavior, Journal of Financial Services Research, 43(1), 99-126.
Rajan, R. 1992. Insiders and Outsiders: The Choice between Informed and Arm’s-length
    Debt, Journal of Finance 47(4), 1367-1400.
Rajan, R. 2005. Has Financial Development Made the World Riskier? Proceedings,
    Federal Reserve Bank of Kansas City, 313-369.
Rey, P. and J. Stiglitz, 1993. Short-term Contracts as a Monitoring Device, NBER
    Working Paper 4514.
Rodrik, D. and A. Velasco, 2000. Short-term Capital Flows, in Pleskovic, B. and J.
    Stiglitz (Eds.), Annual World Bank Conference on Development Economics 1999,
    59-90, World Bank.
Roll, R., 1992. A Mean/Variance Analysis of Tracking Error, Journal of Portfolio
    Management 18(4), 13-22.
Sharfstein, D. and J. Stein, 1990. Herd Behavior and Investment, American Economic
    Review 80(3), 465-479.
Shleifer, A. and R. Vishny, 1990. Equilibrium Short Horizons of Investors and Firms,
    American Economic Review 80(2), 148-153.
Sirri, E. and P. Tufano, 1998. Costly Search and Mutual Fund Flows, Journal of Finance
    53(5), 1589-1622.
Stein, J., 2003. Agency, Information, and Corporate Investment, in: Constantinides, G.,
    M. Harris, and R. Stulz (Eds.), Handbook of the Economics of Finance, 111-163,
    Elsevier, Amsterdam, North-Holland.
Stein, J., 2005. Why Are Most Funds Open-end? Competition and the Limits of
    Arbitrage, Quarterly Journal of Economics 120(1), 247-272.
Stewart, F., 2014. Proving Incentives for Long-term Investment by Pension Funds: The
    Use of Outcome-based Benchmarks, World Bank Policy Research Paper 6885.



                                          45
The Economist, 2013. Pension Funds Can and Should Invest More in Infrastructure,
    October 26.
The Economist, 2014. How to Get More of the World’s Savings to Pay for New Roads,
    Airports, and Electricity, March 22.
Tirole, J., 2003. Inefficient Foreign Borrowing: A Dual- and Common-agency
    Perspective, American Economic Review 93(5), 1,678-1,702.




                                       46
                                                                                                Figure 1
                                                                               Maturity Structure of Chilean Mutual Funds
This figure presents the maturity structure of Chilean domestic bond mutual funds, that is, the proportion of the portfolio held at different terms to maturity. Shares are
calculated as a fraction of the overall portfolio. Only medium- and long-term bond mutual funds are taken into account. The maturity structure is calculated per mutual fund
and averaged across funds at each moment in time using monthly bins, and then averaged over time. The sample period is Sep. 2002-Jun. 2008. Panel A shows the average
accumulated portfolio weight in each bin as well as the 25th and 75th percentiles across mutual funds. Panel B shows the average total portfolio weight within each monthly
bin, along with the fitted value of the fractional polynomial regression of total portfolio weights on the term to maturity in months. Panel C shows the accumulated weights in
a table format.

                                                                                                           A. Accumulated Weights
                                                                                                                       years to maturity
                                                        100%
                                                                                     25th Percentile
                                                                 90%                 Average

                                                                 80%                 75th Percentile

                                                                 70%
                                   Portfolio Share




                                                                 60%

                                                                 50%

                                                                 40%

                                                                 30%

                                                                 20%

                                                                 10%

                                                                 0%
                                                                       0        1          2           3      4       5          6        7      8          9          10
                                                                                                                                                     Years to Maturity

                                                                                                  B. Weights within Each Maturity Range
                                                                 10%
                                                                                                                                                           Fitted
                                                                  9%                                                       years to maturity               Average

                                                                  8%

                                                                  7%
                                               Portfolio Share




                                                                  6%

                                                                  5%

                                                                  4%

                                                                  3%

                                                                  2%

                                                                  1%

                                                                  0%
                                                                           0     1          2          3       4       5         6         7     8           9         10
                                                                                                                                                     Years to Maturity



     C. Accumulated Weights
                                                                       <1year (y)              <3y            <5y              <7y             <10y                 <15y    <20y   <30y
     Chilean Domestic Mutual Funds                                       38%                   59%            73%              80%             88%                  95%     99%    100%
                                                                                       Figure 2
                                                                          Maturity Structure of Chilean PFAs
This figure presents the maturity structure of Chilean pension fund administrators (PFAs), that is, the proportion of the portfolio held at different terms to maturity. Shares are
calculated as a fraction of the fixed-term portfolio. The maturity structure is calculated per PFA (over all fund types) and averaged across PFAs at each moment in time using
monthly bins, and then averaged over time. The sample period is Sep. 2002-Jun. 2008. Panel A shows the accumulated portfolio weight in each bin, as well as the 25th and
75th percentiles across PFAs. Panel B shows the total portfolio weight within each bin, along with the fitted value of the fractional polynomial regression of total portfolio
weights on the term to maturity in months. Panel C shows the accumulated weights in a table format.

                                                                                             A. Accumulated Weights
                                                      100%
                                                                           25th Percentile
                                                      90%                  Average
                                                                           75th Percentile
                                                      80%

                                                      70%
                                    Portfolio Share




                                                      60%

                                                      50%

                                                      40%

                                                      30%

                                                      20%

                                                      10%

                                                       0%
                                                             0        1       2          3      4      5         6         7     8           9        10
                                                                                                                                      Years to Maturity


                                                                                     B. Weights within Each Maturity Range
                                                       6%
                                                                                                                                            Fitted
                                                                                                           years to maturity
                                                                                                                                            Average
                                                       5%


                                                       4%
                                    Portfolio Share




                                                       3%


                                                       2%


                                                       1%


                                                       0%
                                                              0       1       2          3      4      5         6         7      8          9        10
                                                                                                                                      Years to Maturity


     C. Accumulated Weights
                                                             <1year (y)        <3y             <5y             <7y             <10y              <15y      <20y   <30y
     Chilean PFAs                                              40%             56%             68%             77%             86%               93%       100%   100%
                                                                                     Figure 3
                                                                 Maturity Structure of Chilean PFAs by Fund Type
This figure presents the maturity structure of Chilean pension fund administrators (PFAs) by fund type. Shares are calculated as a fraction of the fixed-term portfolio. The
maturity structure is calculated per fund and averaged across funds of the same type at each moment in time using monthly bins, and then averaged over time. The sample
period is Sep. 2002-Jun. 2008. Panel A shows the accumulated portfolio weight in each bin. Panel B shows the total portfolio weight within each bin. Panel C shows the
accumulated weights and the average maturity in a table format.

                                                                                            A. Accumulated Weights
                                                        100%

                                                        90%

                                                        80%

                                                        70%
                                      Portfolio Share




                                                        60%

                                                        50%
                                                                                                                                  Funds A

                                                        40%                                                                       Funds B

                                                        30%                                                                       Funds C

                                                        20%                                                                       Funds D

                                                                                                                                  Funds E
                                                        10%

                                                         0%
                                                                 0        1   2         3      4      5      6       7      8         9        10
                                                                                                                                Years to Maturity


                                                                                    B. Weights within Each Maturity Range
                                                        7%


                                                        6%


                                                        5%
                                   Portfolio Share




                                                        4%                                                                        Funds A

                                                                                                                                  Funds B
                                                        3%
                                                                                                                                  Funds C

                                                        2%                                                                        Funds D

                                                                                                                                  Funds E
                                                        1%


                                                        0%
                                                             0        1       2         3     4      5       6       7      8         9        10
                                                                                                                                Years to Maturity


     C. Average Maturity and Accumulated Weights
                                                                                                          Accumulated Weights
                          Avg. Maturity
                                                               <1year (y)         <3y         <5y          <7y          <10y                <15y    <20y    <30y
     Funds A                   2.72                              62%              77%         83%          87%          93%                 95%     100%    100%
     Funds B                   3.67                              48%              64%         74%          82%          90%                 94%     100%    100%
     Funds C                   4.62                              38%              55%         66%          75%          85%                 92%     100%    100%
     Funds D                   4.51                              35%              52%         66%          77%          87%                 93%     100%    100%
     Funds E                   5.70                              22%              39%         56%          70%          83%                 91%     100%    100%
                                                                         Figure 4
                                 Maturity Structure of Chilean Insurance Companies Compared to Mutual Funds and PFAs
This figure compares the maturity structure of Chilean insurance companies to that of Chilean domestic mutual funds and PFAs. Only medium- and long-term bond mutual funds are taken into account. The
maturity structure of Chilean mutual funds and PFAs (insurance companies) is calculated per mutual fund and PFA (company) and averaged across mutual funds and PFAs (companies) at each moment in time
using monthly bins, and then averaged over time. PFA shares are calculated as a fraction of the fixed-term portfolio, whereas shares of insurance companies and mutual funds are calculated as a fraction of the
overall portfolio. The sample period is Sep. 2002-Jun. 2008. Panel A shows the accumulated portfolio weights of the maturity structure of Chilean insurance companies, domestic mutual funds, and PFAs, and
Panel B shows the same information within each monthly bin. Panel C shows the average maturity and accumulated weights in a table format. Panel D shows p-values for the two-sided t-tests of equality of
average maturities, accumulated weights, and the Kolmogorov-Smirnov (KS) test of equality of the whole maturity structure. The KS test for functional data is based on the methodology proposed by Cuesta-
Albertos et al. (2006) that relies on random projections of the samples of maturity structures. The p-value reported for this test is adjusted for false discovery rate as suggested by Benjamini and Yekutieli (2001)
and corresponds to the minimum p-value obtained after repeating the test as many times as the number of maturity bins used to construct the figure, using a different random projection vector in each repetition. *,
**, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

                                                                                                           A. Accumulated Weights
                                                             100%

                                                               90%

                                                               80%

                                                               70%
                                          Portfolio Share




                                                               60%

                                                               50%

                                                               40%

                                                               30%
                                                                                                                                            Chilean PFAs
                                                               20%
                                                                                                                                            Chilean Domestic Mutual Funds

                                                               10%
                                                                                                                                            Chilean Insurance Companies

                                                                0%
                                                                       0    1             2    3             4        5        6        7             8           9           10
                                                                                                                                                             Years to Maturity



                                                                                              B. Weights within Each Maturity Range
                                                              10%

                                                               9%

                                                               8%

                                                               7%
                                          Portfolio Share




                                                               6%

                                                               5%

                                                               4%                                                                       Chilean PFAs

                                                               3%                                                                       Chilean Domestic Mutual Funds


                                                               2%                                                                       Chilean Insurance Companies


                                                               1%

                                                               0%
                                                                       0    1             2   3              4        5        6        7             8            9          10
                                                                                                                                                             Years to Maturity

  C. Average Maturity and Accumulated Weights
                                          (i)                                      (ii)            (iii)              (iv)            (v)           (vi)                      (vii)     (viii)          (ix)
                                                                                                                                    Accumulated Weights
                                                            Avg. Maturity
                                                                                <1year (y)         <3y               <5y             <7y           <10y                      <15y       <20y           <30y
  (1) Chilean Domestic Mutual Funds                             3.97              39%              60%               73%             81%           89%                       95%         99%           100%
  (2) Chilean PFAs                                              4.36              40%              56%               68%             77%           86%                       93%        100%           100%
  (3) Chilean Insurance Companies                               9.77              26%              34%               42%             47%           55%                       68%         86%           100%

  D. Hypothesis Testing
                                                                 (i)               (ii)            (iii)              (iv)            (v)                  (vi)               (vii)     (viii)          (ix)
                                                                                                                            Accumulated Weights
                                                            Avg. Maturity
                                                                                <1year (y)      <3y                  <5y             <7y                 <10y                 <15y       >20y           KS
  (1) = (2)                                                     0.24              0.30          0.12                0.02**         0.01**               0.01**               0.02**     0.04**       <0.01***
  (1) = (3)                                                    <0.01***           0.02**       <0.01***            <0.01***       <0.01***             <0.01***             <0.01***   <0.01***      <0.01***
  (2) = (3)                                                    <0.01***           0.44         <0.01***            <0.01***       <0.01***             <0.01***             <0.01***   <0.01***      <0.01***
                                                                                                    Figure 5
                                                                                   Government Bonds Purchased by Chilean PFAs
This figure presents the total amount of government bonds issued by currency denomination and the total amount and the proportion purchased by PFAs. The panels are
shown by currency and represent total issuances and purchases. The sample period is 1998-2008. Panel A shows the results for bonds denominated in nominal Chilean
pesos, Panel B for bonds denominated in indexed (inflation-linked) Chilean pesos, and Panel C for bonds denominated in US dollars.


                                                                                           A. Issuance Denominated in Nominal Chilean Pesos
                                                                        $45,000

                                                                        $40,000

                                                                        $35,000
                            Millions of Chilean Pesos




                                                                        $30,000

                                                                        $25,000

                                                                        $20,000

                                                                        $15,000

                                                                        $10,000
                                                                                     3%     7%        7%                    14%
                                                                         $5,000

                                                                             $0
                                                                                    <1y    2y         5y          8y        10y      20y            30y
                                                                                                                                           Years to Maturity



                                                                                           B. Issuance Denominated in Indexed Chilean Pesos
                                                                        $140,000

                                                                        $120,000
                                   Thousands of Indexed Chilean Pesos




                                                                        $100,000

                                                                         $80,000

                                                                         $60,000

                                                                         $40,000
                                                                                           31%
                                                                         $20,000                      33%         52%       33%      47%            7%

                                                                             $0
                                                                                     <1y    2y         5y         8y        10y       20y           30y
                                                                                                                                           Years to Maturity


                                                                                                 C. Issuance Denominated in US Dollars
                                                                          $4,500

                                                                          $4,000

                                                                          $3,500
                                       Millions of US Dollars




                                                                          $3,000

                                                                          $2,500

                                                                          $2,000

                                                                          $1,500
                                                                                     10%   25%
                                                                          $1,000

                                                                            $500                       15%
                                                                              $0
                                                                                     <1y    2y         5y          8y       10y          20y         30y
                                                                                                                                            Years to Maturity

                                                                                           Total Amount Purchased by PFAs         Total Issuance
                                                                                                         Figure 6
                                                                                     Evolution of PFA Short-term Assets around Events
This figure shows how the share of short-term assets in the portfolio of PFAs varies during the Asian and Russian crises of 1997-1998. Panel A presents the average share of domestic short-
term fixed-income assets (those with a term to maturity of up to 30 days) held by Chilean PFAs. PFA shares are calculated as a fraction of the fixed-term portfolio, not the overall portfolio.
Some of the major events occurring during this period are displayed in vertical lines. Panel B shows the results for the difference-in-differences regression between Chilean mutual funds,
PFAs, and insurance companies for the 1998 Russian crisis. The variable PFA (Mutual Fund) Dummy is equal to one if the investor is a PFA (mutual fund). The variable Crisis Dummy is
equal to one if the observation is in the crisis period (Aug. 1998-Oct. 1998). The Post-crisis Dummy is equal to one if the observation is in the post crisis period (Nov. 1998-Jan.1999). In
all other cases, the dummy variables are equal to zero. The sample period is May 1998-Jan. 1999. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level,
respectively.

                                                                                          A. Average Share of PFA's Short-term Fixed-Income Assets
                                                   6%
                                                                   Aug. 14                                                             Jul. 13 IMF &
                                                                   Indonesia                                                                                                 Sep. 23
                                                                                                                                       World Bank
             Short-term Assets - Portfolio Share




                                                   5% Jul. 2       free-floats                                                                                               LTCM
                                                                                                                                       rescue package
                                                        Thailand   the rupiah                                                                                                bailout
                                                        devalues
                                                                                                                                       for Russia
                                                   4%   the baht

                                                   3%


                                                   2%                                                                                                           Aug. 13
                                                                       Aug. 11                Nov. 28 Moody's                                                   Russian
                                                                      IMF rescue              lowers South                                                       stock
                                                   1%                 package                 Korea's credit                                                    market
                                                                      for Thailand            rating                                                            collapses
                                                   0%




                                                                                                   B. Difference-in-Differences Regressions

                                                                                                                               Dependent Variable: Average Maturity
                                                                                                                                                                  Mutual Funds and
                                                                                                            PFAs and Mutual           PFAs and Insurance
                                                                             1998m5 -1999m1                                                                          Insurance
                                                                                                                Funds                    Companies
                                                                                                                                                                    Companies
                                                                                                            Coef.       Std. Error     Coef.       Std. Error     Coef.        Std. Error

                                                               PFA Dummy                                   5.432 ***         0.504    -1.425            1.717
                                                               Mutual Fund Dummy                                                                                 -6.954 ***        1.397
                                                               Crisis Dummy                                0.062             0.432     0.131            1.387    0.131             1.319
                                                               Post-crisis Dummy                           -0.707 *          0.423    -0.172            1.311    -0.172            1.246
                                                               PFA Dummy*Crisis Dummy                      -0.199            0.733    -0.383            2.499
                                                               Mutual Fund Dummy*Crisis Dummy                                                                    0.133             1.942
                                                               PFA Dummy*Post-crisis Dummy                 0.672             0.748     0.153            2.510

                                                               Mutual Fund Dummy*Post-crisis Dummy                                                               -0.508            1.872

                                                               Constant                                    3.344 ***         0.311    10.169 ***        0.991   10.169 ***         0.942
                                                               No. of Obs.                                            289                      112                          147

                                                               R-Squared                                              0.55                     0.20                         0.38
                                                                      Figure 7
                                         Maturity Structure of Chilean Mutual Funds and PFAs by Currency
This figure presents the maturity structure of Chilean domestic bond mutual funds and PFAs by currency: nominal Chilean pesos, indexed (inflation-linked) Chilean pesos, and "hard
currencies" (US dollars, yens, euros, and British pounds). The maturity structure of Chilean mutual funds (PFAs) is calculated per mutual fund (PFA), and averaged across mutual funds
(PFAs) at each moment in time using monthly bins. Weights are calculated over the entire portfolio and then normalized within each currency category. The sample period is Sep. 2002 - Jun.
2008. Panel A shows the maturity structure of Chilean domestic mutual funds and Panel B shows that of Chilean PFAs. Panel C shows the portfolio composition by currency. Panel D shows
the average maturity by currency. Panel E shows p-values for the two-sided t-tests of equality of average maturities and the Kolmogorov-Smirnov (KS) test of equality of the whole maturity
structure. The KS test for functional data is based on the methodology proposed by Cuesta-Albertos et al. (2006) that relies on random projections of the samples of maturity structures. The p-
value reported for this test is adjusted for false discovery rate as suggested by Benjamini and Yekutieli (2001) and corresponds to the minimum p-value obtained after repeating the test as
many times as the number of maturity bins used to construct the figure, using a different random projection vector in each repetition. *, **, and *** represent statistical significance at the 10%,
5%, and 1% level, respectively.


                                                                                                                A. Chilean Domestic Mutual Funds byyears to maturity
                                                                                                                                                    Currency

                                                                                      100%

                                                                                        90%
                                            Portfolio Share within Each Currency




                                                                                        80%

                                                                                        70%

                                                                                        60%

                                                                                        50%

                                                                                        40%

                                                                                        30%

                                                                                        20%                                                                         Hard Currencies

                                                                                                                                                                    Pesos
                                                                                        10%
                                                                                                                                                                    Indexed Pesos
                                                                                         0%
                                                                                              0      1          2       3         4       5       6          7      8           9         10
                                                                                                                                                                        Years to Maturity
                                                                                                                            B. Chilean PFAs by Currency

                                                                                       100%

                                                                                        90%
                                              Portfolio Share within Each Currency




                                                                                        80%

                                                                                        70%

                                                                                        60%

                                                                                        50%

                                                                                        40%

                                                                                        30%

                                                                                        20%                                                                             Hard Currencies

                                                                                                                                                                        Pesos
                                                                                        10%
                                                                                                                                                                        Indexed Pesos
                                                                                         0%
                                                                                              0      1          2       3         4       5       6          7      8           9         10
                                                                                                                                                                        Years to Maturity

                        C. Overall Portfolio Weights by Currency
                                                                                                                                                            (1)               (2)           (3)
                                                                                                                                                           Pesos        Indexed Pesos Hard Currencies
                        Chilean Domestic Mutual Funds                                                                                                      80.9%             5.9%         13.3%
                        Chilean PFAs                                                                                                                       21.5%            75.2%          3.3%

                        D. Average Maturity
                                                                                                                                                             (1)             (2)            (3)
                                                                                                                                                            Pesos       Indexed Pesos Hard Currencies
                        Chilean Domestic Mutual Funds                                                                                                       3.88            7.15           3.14
                        Chilean PFAs                                                                                                                        1.85            5.31           1.60

                        E. Hypothesis Testing
                                                                                         (i)            (ii)                                                                    (iii)           (iv)
                                                                                       Chilean Mutual Funds                                                                        Chilean PFAs
                                                                                       t-test           KS                                                                    t-test            KS
                        (1) = (2)                                                    <0.01***        <0.01***                                         (1) = (2)             <0.01***         <0.01***
                        (1) = (3)                                                    <0.01***        <0.01***                                         (1) = (3)             <0.01***         <0.01***
                        (2) = (3)                                                    <0.01***        <0.01***                                         (2) = (3)             <0.01***         <0.01***
                                                                                                                    Figure 8
                                                                                            Bond Returns at Different Maturities and Holding Periods
This figure presents the average annualized returns, standard deviations, and Sharpe ratios (average returns/standard deviations) of Chilean bonds of different maturities for various holding periods (3 months, 1 year, 2 years, and 3 years). Panel A shows statistics
for indices of government inflation-indexed bonds. Panel B shows statistics using prices from model-based estimations of the yield curve. Returns for bonds of different maturities are daily, calculated using a rolling window for the different holding periods. The
sample period is Jan. 2002-Dec. 2007.

                                                        A. Indices of Chilean Government Inflation-Indexed Bonds                                                                          B. Indices Based on the Estimated Yield Curve

                                                                            A1. Average Return                                                                                                            B1. Average Return

                                    0.14                                                                                                                          0.14
                                               3m                12m                                                                                                          3m           12m

                                    0.12       24m               36m                                                                                              0.12        24m          36m


                                     0.1                                                                                                                            0.1




                                                                                                                                             Annualized Return
               Annualized Return




                                    0.08                                                                                                                          0.08


                                    0.06                                                                                                                          0.06


                                    0.04                                                                                                                          0.04


                                    0.02                                                                                                                          0.02


                                      0                                                                                                                               0
                                           1        2       3          5      6       9      10         12   15       18       20                                         1    2      3          5         6       9      10     12    15         18       20
                                                                                                                   Years to Maturity                                                                                                           Years to Maturity




                                                                       A2. Average Standard Deviation                                                                                                B2. Average Standard Deviation

                                    0.18                                                                                                                          0.18
                                                3m                12m
                                                                                                                                                                              3m            12m
                                    0.16                                                                                                                          0.16
                                                24m               36m                                                                                                         24m           36m
                                    0.14                                                                                                                          0.14

                                    0.12                                                                                                                          0.12
               Standard Deviation




                                                                                                                                             Standard Deviation




                                     0.1                                                                                                                            0.1

                                    0.08                                                                                                                          0.08

                                    0.06                                                                                                                          0.06

                                    0.04                                                                                                                          0.04

                                    0.02                                                                                                                          0.02

                                      0                                                                                                                               0
                                           1        2       3          5      6       9      10         12   15      18        20                                         1    2      3          5         6       9      10      12      15      18        20
                                                                                                                   Years to Maturity                                                                                                           Years to Maturity




                                                A3. Ratio between Average Return and Average Standard Deviation                                                                B3. Ratio between Average Return and Average Standard Deviation

                                    2.5                                                                                                                             2.5
                                               3m               12m                                                                                                           3m          12m


                                               24m              36m                                                                                                           24m         36m
                                     2                                                                                                                               2



                                    1.5                                                                                                                             1.5
                Sharpe Ratio




                                                                                                                                                     Sharpe Ratio




                                     1                                                                                                                               1



                                    0.5                                                                                                                             0.5



                                     0                                                                                                                               0
                                           1    2           3          5      6       9      10         12   15       18        20                                        1    2      3          5         6       9      10     12    15         18       20
                                                                                                                    Years to Maturity                                                                                                          Years to Maturity
                                                          Figure 9
                        Net Inflows to Chilean Mutual Funds and PFAs Compared to US Mutual Funds
This figure presents the cumulative distribution of net monthly inflows of funds to Chilean domestic bond mutual funds, Chilean PFAs, and US bond mutual funds as a fraction
of their fixed-term assets. Net inflows to Chilean and US mutual funds are computed for each mutual fund as the difference between the contemporaneous and lagged value of
a mutual fund's assets and the returns accrued from the assets in the previous month's portfolio, and are divided by the contemporaneous value of a mutual fund's fixed-term
assets. Net inflows to PFAs are calculated by aggregating daily data, directly collected by the Chilean Superintendency of Pensions. The sample period is Sep. 2002-Dec. 2005.
Panel A shows the empirical cumulative probability distributions of these normalized inflows across mutual funds (PFAs) and months, under the assumption that normalized
inflows are independent and identically distributed across mutual funds (PFAs) and time. The distribution of US and Chilean mutual fund inflows are shown only partially
because they have been limited to fit the scale of the distribution of PFA inflows. Panel B reports the fraction of the fixed-term portfolio invested by the average mutual fund
(PFA) up to one and three months (reported in the first and third columns) and the probabilities of observing an outflow larger than that magnitude (reported in the second and
fourth columns). These probabilities are obtained from the empirical distributions shown in Panel A. Estimations for the US for Panel B are based on the assumption that
within the zero to three year interval, the maturity structure of US funds is the same as that of Chilean mutual funds.


                                                                                     A. Cumulative Distribution of Net Inflows
                                                            100%
                                                                          Chilean PFAs
                                                            90%
                                                                          Chilean Domestic Mutual Funds
                                                            80%           US Multi-Sector Mutual Funds
                                   Cumulative Probability




                                                            70%

                                                            60%

                                                            50%

                                                            40%

                                                            30%

                                                            20%

                                                            10%

                                                             0%
                                                                   -5%   -4%     -3%       -2%       -1%        0%       1%      2%       3%      4%        5%
                                                                                         Net Inflows as a Fraction of Fixed-Term Assets



                                                             B. Percentage of Assets Held Short Term and Probability of Outflows of that Magnitude

                                                                                                                 Probability                                 Probability
                                                                                                 % of            Outflows >                      % of        Outflows >
                                                                                               Short-term           % of                       Short-term       % of
                                                                                                 Assets          Short-term                      Assets      Short-term
                                                                                                                   Assets                                      Assets
                                                                                                         Up to 1 month                             Up to 3 months
                            Chilean Domestic Mutual Funds                                          9.3%            21.6%                         17.9%           13.4%
                            Chilean PFAs                                                           4.4%             0.0%                         11.2%            0.0%
                            US Multisector Bond Funds                                              3.7%             6.6%                          7.1%            2.8%
                                                                          Table 1
                                                         PFA Holdings of Outstanding Corporate Debt

This table shows the corporate bond holdings of PFAs compared to the total outstanding corporate debt. Panel A presents the fraction of outstanding corporate debt that PFAs purchase. Panel
B presents the average maturity of PFA corporate bond holdings compared to the average maturity of the total outstanding corporate debt. The data on outstanding corporate debt per year
come from Braun and Briones (2008). The yearly amount purchased by PFAs is the average across monthly data, obtained from the Superintendency of Pensions. Panel B presents this
information as of December 31 of each year during the period 2002-2005, obtained from the Superintendency of Pensions and the Superintendency of Securities and Insurance of Chile.


                                                                A. Fraction of Outstanding Corporate Debt Held by PFAs


                                                                                                                           Purchased by PFAs
                                                             Outstanding Corporate Debt    Purchased by PFAs (Millions
                                            Year                                                                       (Percentage of Outstanding
                                                              (Millions of US Dollars)           of US Dollars)
                                                                                                                            Corporate Debt)

                                            1997                        $2,047                         $1,195                        58%
                                            1998                        $1,699                         $941                          55%
                                            1999                        $2,156                         $1,214                        56%
                                            2000                        $3,974                         $1,388                        35%
                                            2001                        $6,076                         $1,723                        28%
                                            2002                        $8,293                         $2,331                        28%
                                            2003                        $9,790                         $2,901                        30%
                                            2004                       $12,931                         $3,650                        28%



                                        B. Average Maturity (in Years) of PFA Corporate Bond Holdings vs. Total Outstanding Corporate Debt

                                                                      Dec. 2002                      Dec. 2003                     Dec. 2004                    Dec. 2005
          PFA Holdings of Corporate Debt                                 4.9                            5.0                           5.8                           6.1
          Outstanding Corporate Debt                                     12.2                           12.7                          14                           14.7
                                                                            Table 2
                                               Bids by PFAs and Insurance Companies in Government Bond Auctions

Panel A shows the shares pension funds and insurance companies bid for in auctions of Chilean government bonds of different maturities. Panel B shows the ratio between the shares requested by insurance
companies and pension funds. P-values for the hypothesis tests of equal requests (measured as the ratio of insurance companies to pension funds) across the different maturities are shown on the right side of the
panel. The data for this table include all government auctions from 2002 to 2009 of bonds denominated in pesos, inflation-indexed pesos, and US dollars. Regressions are run separately for inflation-indexed
pesos and for all currencies, controlling for currency. Standard errors are clustered by auction and type of institutional investor. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level,
respectively.

                                                                                      A. Shares Requested and Prices Offered


                                                             (i)                                (ii)                                       (iii)                                       (iv)
                                                            Dependent Variable: Shares Requested                                                     Dependent Variable: Prices Offered
                                Time to                                           Indexed Pesos, Pesos, and US                                                         Indexed Pesos, Pesos, and US
                                                     Indexed Pesos                                                                   Indexed Pesos
                                Maturity                                         Dollars, Controlling for Currency                                                    Dollars, Controlling for Currency
                                (Years)      Coef.                 Std. Error     Coef.                Std. Error                Coef.                Std. Error      Coef.               Std. Error
     Pension Funds                  1                                             0.029   ***            0.010                                                          106     ***           0.521
                                    2       0.063     ***            0.010        0.090   ***            0.005                    101.10 ***               0.287       107.30 ***             0.598
                                    5       0.118     ***            0.006        0.108   ***            0.005                    107.20 ***               0.309       107.20 ***             0.320
                                   10       0.129     ***            0.006        0.125   ***            0.005                     58.00    ***            0.696       105.70 ***             0.631
                                   20       0.163     ***            0.010        0.163   ***            0.010                     96.00    ***            0.899        96.00 ***             0.898
                                   30       0.075     ***            0.011        0.075   ***            0.011                     89.69    ***            1.292        89.69 ***             1.291
     Insurance Companies            1
                                    2       0.007     ***            0.005        0.045   ***            0.004                    101.20 ***               0.790       104.80 ***             3.037
                                    5       0.012     ***            0.003        0.035   ***            0.003                    100.60 ***               0.447       101.70 ***             0.581
                                   10       0.012     ***            0.003        0.035   ***            0.004                     98.64    ***            0.737        99.50 ***             0.651
                                   20       0.076     ***            0.010        0.076   ***            0.010                     95.55    ***            0.687        95.55 ***             0.686
                                   30       0.126     ***            0.014        0.126   ***            0.014                     88.86    ***            0.924        88.86 ***             0.923
        No. of Obs.                                      3,700                              7,498                                          1,196                                      1,812
     When comparing within institutional investor across maturities, the differences between shares requested are all statistically significant (two-sided t-test of equality at 10% significance level), except
     in some cases. Differences are not significant when testing:
          - 2y = 30y and 5y = 10y (indexed peso bonds) and 2y = 30y (all currencies) for shares requested by pension funds.
          - 2y = 5y, 2y = 10y, and 5y = 10y (indexed peso bonds) and 5y = 10y (all currencies) for shares requested by insurance companies.
     Differences between prices are all statistically significant (within institutional investor across maturities), with the following exceptions:
          - 5y = 10y (indexed peso bonds) and 1y=10y and 2y = 5y (all currencies) for prices offered by pension funds.
          - 2y= 5y (indexed peso bonds and all currencies) for prices offered by pension funds.


                                                                    B. Ratio between Shares Requested by Insurance Companies and Pension Funds


                                                             (i)                                (ii)                                        (i)                                               (ii)
                                                Dependent Variable: Ratio between Shares Requested                                  P-values for Hypothesis Tests of Equality between Maturities
                                                                                  Indexed Pesos, Pesos, and US                                                            Indexed Pesos, Pesos, and US
          Time to Maturity                           Indexed Pesos                                                                   Indexed Pesos
                                                                                 Dollars, Controlling for Currency                                                       Dollars, Controlling for Currency
              (Years)
                                             Coef.                 Std. Error     Coef.                Std. Error         2y        5y             10 y      20 y       1y        2y           5y      10 y    20 y
                  1                                                               0.105   ***            0.082
                  2                         0.168     ***            0.145        0.053   ***            0.076                                                          0.212
                  5                         0.218     ***            0.115        0.184   ***            0.098           0.789                                          0.149   0.088
                 10                         0.119     ***            0.044        0.167   ***            0.044           0.746     0.424                                0.449   0.144         0.858
                 20                         0.609     ***            0.113        0.609   ***            0.112           0.017     0.016           0.000                0.000   0.000         0.005    0.000
                 30                         3.473     ***            1.701        3.473   ***            1.701           0.054     0.057           0.049     0.094      0.048   0.045         0.054    0.052 0.093
             No. of Obs.                                    418                                 666
                                                                              Table 3
                                                       Bids of PFAs in Government Bond Auctions by Fund Type
This table shows the shares that the different types of pension funds bid for in auctions of Chilean government bonds of different maturities and the ratio between the shares requested by
insurance companies and pension funds. The data for this table include government auctions from 2003 to 2009 of bonds denominated in pesos, inflation-indexed pesos, and US dollars.
Regressions are run separately for inflation-indexed pesos (Panel A) and for all currencies, controlling for currency (Panel B). Standard errors are clustered by auction. *, **, and *** represent
statistical significance at the 10%, 5%, and 1% level, respectively.

                                                                                            A. Indexed Pesos


                                                                                        Dependent Variable: Shares Requested
             Time to                 Funds A                          Funds B                          Funds C                         Funds D                          Funds E
             Maturity                          Std.                             Std.                             Std.                            Std.                             Std.
             (Years)         Coef.                            Coef.                            Coef.                           Coef.                            Coef.
                                               Error                            Error                            Error                           Error                            Error
                 1
                 2          0.038 **           0.020          0.067 ***         0.022          0.120 ***         0.027         0.131 ***         0.027          0.071 ***         0.020
                 5          0.119 ***          0.017          0.148 ***         0.018          0.202 ***         0.020         0.163 ***         0.022          0.132 ***         0.020
                 10         0.105 ***          0.011          0.142 ***         0.016          0.202 ***         0.017         0.171 ***         0.016          0.141 ***         0.016
                 20         0.150 ***          0.021          0.171 ***         0.020          0.213 ***         0.021         0.197 ***         0.018          0.160 ***         0.020
                 30         0.072 ***          0.022          0.093 ***         0.022          0.119 ***         0.026         0.111 ***         0.026          0.077 ***         0.028
            No. of Obs.               1,638                            1,727                            1,717                           1,722                            1,726



                                                         Dependent Variable: Ratio between Shares Requested by Insurance Companies and Pension Funds
             Time to                 Funds A                          Funds B                          Funds C                         Funds D                          Funds E
             Maturity                          Std.                             Std.                             Std.                            Std.                             Std.
             (Years)         Coef.                            Coef.                            Coef.                           Coef.                            Coef.
                                               Error                            Error                            Error                           Error                            Error
                 1
                 2                                                                             0.031 ***         0.097         0.028 ***         0.092          0.038 ***         0.108
                 5          0.067 ***          0.265          0.104 ***         0.351          0.183 ***         1.026         0.204 ***         1.109          0.229 ***         1.171
                 10         0.073 ***          0.192          0.076 ***         0.175          0.057 ***         0.157         0.061 ***         0.161          0.114 ***         0.356
                 20         0.652 ***          0.973          0.568 ***         1.001          0.431 ***         0.705         0.490 ***         0.775          0.565 ***         0.899
                 30         2.004 ***          1.234          2.288 ***         3.416          1.348 ***         0.741         1.642 ***         0.932          3.205 ***         3.769
            No. of Obs.                209                              236                            274                             258                              220



                                                                B. Indexed Pesos, Pesos, and US Dollars, Controlling for Currency


                                                                                        Dependent Variable: Shares Requested
             Time to                 Funds A                          Funds B                          Funds C                         Funds D                          Funds E
             Maturity                          Std.                             Std.                             Std.                            Std.                             Std.
             (Years)         Coef.                            Coef.                            Coef.                           Coef.                            Coef.
                                               Error                            Error                            Error                           Error                            Error
                 1          0.094 ***          0.017          0.120 ***         0.015          0.120 ***         0.015         0.116 ***         0.014          0.097 ***         0.016
                 2          0.038 **           0.020          0.067 ***         0.022          0.120 ***         0.027         0.131 ***         0.027          0.071 ***         0.020
                 5          0.111 ***          0.015          0.140 ***         0.016          0.213 ***         0.024         0.183 ***         0.025          0.153 ***         0.024
                 10         0.107 ***          0.012          0.139 ***         0.015          0.191 ***         0.016         0.154 ***         0.019          0.123 ***         0.019
                 20         0.150 ***          0.021          0.171 ***         0.020          0.213 ***         0.021         0.197 ***         0.018          0.160 ***         0.020
                 30         0.072 ***          0.022          0.093 ***         0.022          0.119 ***         0.026         0.111 ***         0.026          0.077 ***         0.028
            No. of Obs.               1,954                            2,047                            2,037                           2,041                            2,045



                                                         Dependent Variable: Ratio between Shares Requested by Insurance Companies and Pension Funds
             Time to                 Funds A                          Funds B                          Funds C                         Funds D                          Funds E
             Maturity                          Std.                             Std.                             Std.                            Std.                             Std.
             (Years)         Coef.                            Coef.                            Coef.                           Coef.                            Coef.
                                               Error                            Error                            Error                           Error                            Error
                 1
                 2                                                                             0.031 ***         0.097         0.028 ***         0.092          0.038 ***         0.108
                 5          0.071 ***          0.261          0.110 ***         0.338          0.265 ***         1.330         0.205 ***         1.035          0.212 ***         1.069
                 10         0.118 ***          0.268          0.109 ***         0.237          0.083 ***         0.215         0.098 ***         0.247          0.137 ***         0.374
                 20         0.652 ***          0.973          0.568 ***         1.001          0.431 ***         0.705         0.490 ***         0.775          0.565 ***         0.899
                 30         2.004 ***          1.234          2.288 ***         3.416          1.348 ***         0.741         1.642 ***         0.932          3.205 ***         3.769
            No. of Obs.                250                              278                            324                             289                              264
                                                                                  Table 4
                                                                    Mutual Fund Inflows and Past Returns

This table presents regressions of Chilean domestic bond mutual funds’ monthly inflows (as a fraction of the assets at the beginning of the month) on funds' past returns. The different regressions use
alternative independent variables, namely, lagged monthly, quarterly, semi-annual, and annual excess returns and returns. All independent variables are lagged one period. Excess returns are
computed as the difference between each fund's returns over the average return across funds for the corresponding time span. Panel A shows regressions estimated using all funds (unbalanced
panel). Panel B shows regressions only considering funds that exist throughout the whole sample period (balanced panel). Observations for which the monthly inflow is larger than one are excluded.
The data cover the period Sep. 2002-Dec. 2005. Standard errors are clustered by fund. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.



                                                                                        A. Unbalanced Panel


                                                                        Dependent Variable: Inflows Relative to Total Assets
         Independent Variables (Lagged)           Coef.               Std. Error      Time Dummies        Fund Dummies         R-Squared             No. of Obs.             No. of Funds
         Monthly Excess Return                    0.261 ***              0.055               No                 No               0.010                  1,675                      63

         Monthly Return                           0.257 ***              0.056              Yes                 No               0.173                  1,675                      63

         Monthly Return                           0.218 ***              0.061              Yes                 Yes              0.223                  1,675                      63

         Quarterly Excess Return                  0.123                  0.095               No                 No               0.001                  1,465                      63

         Quarterly Return                         0.124                  0.119              Yes                 No               0.179                  1,465                      63

         Quarterly Return                        -0.095                  0.220              Yes                 Yes              0.232                  1,465                      63

         Semi-Annual Excess Return                0.035                  0.064               No                 No               0.000                  1,201                      58

         Semi-Annual Return                       0.033                  0.074              Yes                 No               0.160                  1,201                      58

         Semi-Annual Return                      -0.238                  0.200              Yes                 Yes              0.214                  1,201                      58

         Annual Excess Return                     0.180                  0.150               No                 No               0.001                   864                       49

         Annual Return                            0.181                  0.165              Yes                 No               0.189                   864                       49

         Annual Return                           -0.138                  0.418              Yes                 Yes              0.237                   864                       49




                                                                                          B. Balanced Panel


                                                                        Dependent Variable: Inflows Relative to Total Assets
         Independent Variables (Lagged)           Coef.               Std. Error      Time Dummies        Fund Dummies         R-Squared             No. of Obs.             No. of Funds
         Monthly Excess Return                    0.244 ***              0.051               No                 No               0.011                  1,178                      32

         Monthly Return                           0.209 ***              0.056              Yes                 No               0.177                  1,178                      32

         Monthly Return                           0.200 ***              0.054              Yes                 Yes              0.202                  1,178                      32

         Quarterly Excess Return                  0.218                  0.159               No                 No               0.003                  1,058                      32

         Quarterly Return                         0.150                  0.216              Yes                 No               0.182                  1,058                      32

         Quarterly Return                         0.113                  0.256              Yes                 Yes              0.204                  1,058                      32

         Semi-Annual Excess Return                0.210                  0.145               No                 No               0.002                   910                       32

         Semi-Annual Return                       0.225                  0.155              Yes                 No               0.170                   910                       32

         Semi-Annual Return                       0.128                  0.234              Yes                 Yes              0.195                   910                       32

         Annual Excess Return                     0.211                  0.139               No                 No               0.001                   700                       32

         Annual Return                            0.263 **               0.118              Yes                 No               0.189                   700                       32

         Annual Return                            0.055                  0.358              Yes                 Yes              0.221                   700                       32
                                                                                          Table 5
                                                                        Effect of Mutual Fund Past Returns on Risk
This table shows the relation between past returns and the risks that mutual funds managers take. Panel A shows the changes in the average maturity of those funds that have a return lower than the market average or
a negative return in the previous month. The maturity is expressed in months. Panel B shows the relation between the standard deviation of returns in the second semester vs. the first semester of each year and fund
performance, following the Brown, Harlow, and Starks (1996) methodology. The variable Loser Dummy is equal to one if in the first semester of the year the fund has a total return lower than the market average.
We refer to these funds as "losers" funds (otherwise they are "winners"). The Interim Performance variable is the accumulated returns of the second semester of each year. The Cumulative 1-year Performance
variable is the accumulated returns over the last year. In both panels, the returns of the funds are computed using the "Indirect Method." The sample period is Feb. 1998-Sep. 2013. *, **, and *** represent statistical
significance at the 10%, 5%, and 1% level, respectively.

                                                                                     Panel A. Average Maturity of the Portfolio


                                                                                                                           Dependent Variable: Average Maturity

                                                                                                          (i)                                 (ii)                               (iii)
                                                                                                                  Std.                                Std.                                Std.
                       Independent Variables                                                    Coef.                               Coef.                              Coef.
                                                                                                                 Error                               Error                               Error
                       Constant                                                              53.222 ***          2.846            53.707 ***         2.554           53.144 ***          2.810

                       Return(t-1)<Market Return(t-1) Dummy                                   4.595     ***      0.514

                       6-month Mean Return<6-month Market Mean Dummy                                                              4.534     ***      0.540

                       Return(t-1)<0 Dummy                                                                                                                           6.238     ***       0.536

                       No. of Obs.                                                                      9,181                               8,265                               9,181




                                                                                  Panel B. Ratio of Standard Deviation of Returns

                                                                                                                                    Dependent Variable:
                                                                                                        Std. Dev. of Returns (Second Semester) / Std. Dev. of Returns (First Semester)
                                                                                                          (i)                                 (ii)                               (iii)
                                                                                                                  Std.                               Std.                                Std.
                       Independent Variables                                                    Coef.                               Coef.                              Coef.
                                                                                                                 Error                               Error                               Error
                       Constant                                                               1.268     ***      0.074            1.302     ***      0.756           1.287     ***       0.085

                       Loser Dummy                                                            0.021              0.108            -0.006             0.109           0.001               0.116

                       Interim Performance                                                                                        -0.029 **          0.014

                       Cumulative 1-year Performance                                                                                                                 -0.005              0.011

                       No. of Obs.                                                                       774                                 774                                 774
                                                                                        Table 6
                                                                           Effect of PFA Past Returns on Risk
This table shows the relation between past returns and the risks that pension funds managers take. Panel A shows the changes in the average maturity of those funds that have a return lower than the market
average (for the same fund types) or a negative return in the previous month. The maturity is expressed in months. Panel B shows the weight composition for each asset class (government and corporate bonds
and deposits) and the relation between these weights and past returns. Panel C shows the relation between the standard deviation of returns in the second semester vs. the first semester of each year and fund
performance, following the Brown, Harlow, and Starks (1996) methodology. The variable Loser Dummy is equal to one if in the first semester of the year the fund has a total return lower than the market
average (for the same fund type). We refer to these funds as "losers" funds (otherwise they are "winners"). The Interim Performance variable is the accumulated returns of the second semester of each year.
The Cumulative 1-year Performance variable is the accumulated returns over the last year. The returns of the funds are computed using the "Indirect Method." The sample period is Sep. 2002-Dec. 2005 for
Panel A and B and Jan. 1997-Dec. 2005 for Panel C. In all panels we control for fund type. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.


                                                                                Panel A. Average Maturity of the Portfolio

                                                                                                                     Dependent Variable: Average Maturity

                                                                                                      (i)                               (ii)                              (iii)
                                                                                                             Std.                               Std.                                Std.
                      Independent Variables                                               Coef.                               Coef.                             Coef.
                                                                                                            Error                              Error                               Error
                      Constant                                                          52.789 ***          0.346          52.378 ***          0.326         52.340 ***            0.283
                      Return(t-1)<Market Return(t-1) Dummy                              -1.005 **           0.501
                      6-month Mean Return<6-month Market Mean Dummy                                                         -1.436 ***         0.527
                      Return(t-1)<0 Dummy                                                                                                                     -0.184               0.552
                      No. of Obs.                                                                  1,290                              1,170                              1,290



                                                                                 Panel B. Weights within Each Asset Class

                                                                                                               Dependent Variable: Weight of Government Bonds
                                                                                                      (i)                               (ii)                              (iii)
                                                                                                             Std.                               Std.                                Std.
                      Independent Variables                                               Coef.                               Coef.                             Coef.
                                                                                                            Error                              Error                               Error
                      Constant                                                          0.434     ***       0.003           0.432     ***      0.003          0.430     ***        0.003
                      Return(t-1)<Market Return(t-1) Dummy                              -0.004              0.004
                      6-month Mean Return<6-month Market Mean Dummy                                                         -0.014 ***         0.005
                      Return(t-1)<0 Dummy                                                                                                                     0.006                0.005
                      No. of Obs.                                                                  1,290                              1,170                              1,290

                                                                                                                Dependent Variable: Weight of Corporate Bonds

                                                                                                      (i)                               (ii)                              (iii)
                                                                                                             Std.                               Std.                                Std.
                      Independent Variables                                               Coef.                               Coef.                             Coef.
                                                                                                            Error                              Error                               Error
                      Constant                                                          0.165     ***       0.002           0.163     ***      0.002          0.164     ***        0.001
                      Return(t-1)<Market Return(t-1) Dummy                              -0.005 **           0.002
                      6-month Mean Return<6-month Market Mean Dummy                                                         -0.006 **          0.002
                      Return(t-1)<0 Dummy                                                                                                                     -0.003               0.003
                      No. of Obs.                                                                  1,290                              1,170                              1,290

                                                                                                                    Dependent Variable: Weight of Deposits
                                                                                                      (i)                               (ii)                              (iii)
                                                                                                             Std.                               Std.                                Std.
                      Independent Variables                                               Coef.                               Coef.                             Coef.
                                                                                                            Error                              Error                               Error
                      Constant                                                          0.401     ***       0.004           0.405     ***      0.003          0.406     ***        0.003
                      Return(t-1)<Market Return(t-1) Dummy                              0.009     *         0.005
                      6-month Mean Return<6-month Market Mean Dummy                                                         0.019     ***      0.006
                      Return(t-1)<0 Dummy                                                                                                                     -0.003               0.006
                      No. of Obs.                                                                  1,290                              1,170                              1,290



                                                                              Panel C. Ratio of Standard Deviation of Returns

                                                                                                                              Dependent Variable:
                                                                                                  Std. Dev. of Returns (Second Semester) / Std. Dev. of Returns (First Semester)
                                                                                                      (i)                               (ii)                              (iii)
                                                                                                             Std.                               Std.                                Std.
                      Independent Variables                                               Coef.                               Coef.                             Coef.
                                                                                                            Error                              Error                               Error
                      Constant                                                          1.871     ***       0.184           1.819     ***      0.189          1.705     ***        0.213
                      Loser Dummy                                                       -0.238 **           0.105           -0.234 **          0.105          -0.210 **            0.106
                      Interim Performance                                                                                   0.020              0.017
                      Cumulative 1-year Performance                                                                                                           0.020                0.013
                      No. of Obs.                                                                     128                              128                                128
                                                                          Table 7
                                                   Bids by Pension Funds in Government Bond Auctions:
                                                              The Effect of Foreign Exposure

This table shows the shares and prices pension funds bid for in auctions of Chilean government bonds of different currencies. The data for this table include all government auctions from Sep.
2002 to Jun. 2008 of bonds denominated in pesos, inflation-indexed pesos, and US dollars. The variables for the different currencies show the shares requested and prices offered for each type
of currency. The variable Exposure Dummy is equal to one if the investment of the fund in US dollars denominated instruments (as a % of the total investment) of the last month is greater than
the market average of the same variable during the same month. Standard errors are clustered by auction and pension fund. *, **, and *** represent statistical significance at the 10%, 5%, and
1% level, respectively.

                                                             Indexed Pesos, Pesos, and US Dollars, Controlling for Currency

                                                                                                         Dependent Variable:
                                                                                            Shares Requested             Prices Offered
                                               Independent Variables                     Coef.        Std. Error     Coef.        Std. Error
                                               Indexed Pesos                             0.107 ***       0.006       105.5 ***       0.446
                                               Pesos                                     0.028 ***       0.004       105.8 ***       0.533
                                               US Dollars                                0.085 ***       0.011       102.1 ***       0.143
                                               Indexed Pesos*Exposure Dummy              0.054 ***       0.010       0.227           0.619
                                               Pesos*Exposure Dummy                      0.014 **        0.006      -0.556           0.782
                                               US Dollars*Exposure Dummy                 0.010           0.014      -0.574 ***       0.213
                                               No. of Obs.                                       4,533                       1,093
                                                                                Appendix Table 1
                                                                             Description of Main Data
This table presents information on the main data used in this paper by type of institutional investor. It includes the sample period, data frequency, number of observations, number of funds, and
data source. Number of funds refers to the number of mutual funds, the number of insurance companies, or number of pension funds in each case.




                                                                                                           No. of Funds /
              Institutional Investor                   Sample Period         Frequency     No. of Obs.                                            Data Source
                                                                                                            Companies

              Chilean Domestic Mutual Funds         Jan. 2002 - Dec. 2008      Monthly          965,209         86          Superintendency of Securities and Insurance of Chile
              Chilean Insurance Companies           Jan. 2002 - Dec. 2008      Monthly         4,071,927        36          Superintendency of Securities and Insurance of Chile
              Chilean PFAs                          Jan. 2002 - Jun. 2008      Monthly         6,659,681        45          Superintendency of Pensions of Chile
              Chilean PFAs                           Jul. 1996 - Jul. 2008      Daily       201,288,833         62          Superintendency of Pensions of Chile
              US Mutual Funds                            2003 - 2005          Annually             3,816        167         Morningstar