WVS IWiq2
POLICY RESEARCH WORKING PAPER 1792
Tradeoffs from Hedging The benefits In risk-reduction
from hedgvig Ecuadorian old
Oil Price Risk in Ecuador and the opportunity costs of
hedging
Sudhakar Satyanarayan
Eduardo Somensatto
FgLE 7P
The World Bank
Latin America and the Caribbean
Country Departnent III
Country Operations Division
June 1997
LICY RESEARCH WORKING PAPER 1792
Summary findings
The oil sector is critical to Ecuador's economy, Satayanarayan and Somensatto investigate methods to
contributing about 17 percent to the country's GDP. reduce risk for the country's oil exports through hedging
Elcuador began exporting crude oil in 1972 and over the in futures markets. They find that hedging Ecuadorian oil
past two and a half decades oil has become the country's has significant potential for risk reduction.
rnost important sector. It is controlled by the government After simulating ex-ante cross hedges for 1991-96,
through the public enterprise, PETROECUADOR, which they find that in each case ex-ante hedging effectively
serves as the holding company for all state-owned reduces risk. They calculate the tradeoffs between return
petroleum operations. and risk from hedging and find that for a risk-minimizing
Movements in oil prices are of major concern to the short hedger, a 1-percent reduction in risk would cost a
government, and forecasts of oil prices are built into the reduction in return of 0.65 percent.
government budget. Ecuador's macroeconomic
performance depends on the oil sector's performance;
shocks to the sector have economywide repercussions.
This paper - a product of the Country Operations Division, Country Department III, Latin America and the Caribbean
-- is part of a larger effort in the department to provide policy advice to member countries. Copies of the paper are available
free from the World Bank, 1818 H Street NW, W/ashington, DC 20433. Please contact Eduardo Somensatto, room H5-
103, telephone 202-473-0128, fax 202-477-8518, Internet address esomensatto@worldbank.org. June 1997. (18 pages)
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 view of the W'orld Bank, its Executive Directors, or the
countries they represent. b
Produced by the Policy Research Dissemination Center
TRADE-OFFS FROM HEDGING OIL PRICE RISK IN ECUADOR
Sudhakar Satyanarayan
Dept. of Finance, Rockhurst College
1100 Rockhurst Road
Kansas City, MO 64110
Tel: (816) 501-4562
and
Eduardo Somensatto
Europe and Central Asia Region, Southeastern Europe Department
World Bank, Washington, D. C. 20433
Tel: (202) 473-0128
I. INTRODUCTION
The oil sector is a critical sector of the Ecuadorian economy contributing about 17 % to the
country's GDP. Ecuador began exporting crude oil in 1972 and over the last two and a half
decades the oil sector has emerged as the country's most important sector. The oil sector is
controlled by the government through the public sector enterprise, PETROECUADOR, which
serves as the holding company for all state-owned petroleum operations. Movements in oil prices
are of major concern to the government and forecasts of oil prices are built into the government
budget. The performance of the oil sector critiically affects Ecuador's macroeconomic
performance and shocks to this sector has economy wide repercussions.
The volatility of the world oil market since the OPEC oil price shocks of the 1970's has
resulted in oil export dependent countries like Ecuador facing a considerable degree of
macroeconomic risk. Between 1973-81 when Ecuador first emerged as an oil exporter, real GDP
grew at an annual average rate of 6.1 %. During much of the 1980's, oil prices declined
substantially. Over the 1985-86 period, Ecuador lost U$900 million equivalent to about 8 % of
GDP. Declining oil revenues resulted in large public sector deficits and a worsening balance of
payments situation. The effect of the oil price decline was that between 1981-91, GDP growth
averaged about 2.1%, less than half the growth rate before the oil boom'. Ecuador's dependence
on oil is such that even minor oil price declines have had a substantial cumulative adverse impact
on Ecuador's macroeconomic performance.
Developing countries like Ecuador have sought to achieve export revenue stabilization
through International Commodity Agreements with importing nations. These agreements have
1 The source of these figures are Ecuador: Policy Options for
the Rest of the 1990's, Report No.11161-EC, Country Operations
Dept. IV, World Bank.
however not been successful. Other methods such as stabilization funds, contingent financing and
export diversification have also not been successful in stabilizing export revenues.
An alternative approach to stabilizing export revenues is to use market-based risk
management tools such as futures hedging. Though futures hedging cannot insulate exporters
from a long term secular decline in commodity prices, they are effective in managing short term
price risk. Using futures markets for hedging is a notion that is just now beginning to gain
acceptability among developing countries. The New York Mercantile Exchange (NYMEX)
estimates that developing countries are increasingly holding a higher percentage of the total open
interest in crude oil futures. Since the Gulf war countries like Mexico, Brazil and Chile are
regular users of the oil derivatives markets (see Claessens and Varangis (1995)).
The objective of this paper is to assess the risk management prospects for hedging
Ecuadorian oil. We develop a portfolio model of hedging and use it to evaluate the costs and
beneiits of different hedging strategies. Our paper shows that there are effective risk reducing
strategies available to Ecuadorian policy makers that would have reduced the variance of
Ecuadorian oil revenues over time. While these strategies may necessitate foregoing unexpected
gains, they would have prevented unanticipated short term losses. We provide estimates of the
costs and benefits of different hedging strategies that may aid in policy formulation.
2
H. PRICE VOLATILITY, STATIONARITY, AN1D BASIS RISK
The bulk of international trade in crude oil involves light to medium crude (API gravity
of 300 to 40°). Ecuadorian oil (Oriente) is classified as light crude. Ecuador's monthly oil export
prices over the Jan.88-Dec.96 period is shown in Figure 1. The average monthly export price
(per barrel) for Ecuadorian crude over this period was $15.92 with a standard deviation of $3.45
and an associated coefficient of variation of 21.67%. This is a high degree of volatility.
Before turning to the issue of hedging effectiveness, the time series properties of export
(spot) prices and futures prices need to be investigated. The spot and futures prices of most
commodities are generated by stochastic processes that are nonstationary (i.e. these prices are
random walks). The practical implication of nonstationarity is that past prices cannot be used in
predicting future prices. Moreover, transitory and permanent shocks cannot be distinguished
from one another. From an econometric viewpoint, nonstationarity is problematic since estimated
parameters are unstable and a regression on nonstationary variables leads to spurious results (see
Granger and Newbold (1974)). Thus, a nonstationary series must be transformed into a
stationary series before any inferences can be drawn from it. The simple logic for requiring
stationarity is that models inferred from stationary series are also stationary or stable. In general,
a nonstationary series can be transformed into a stationary series by differencing. Table la
reports the results of a Dickey-Fuller (D-F) test for nonstationarity on the levels and first
differences of both spot and futures prices. The D--F test results confirm that both spot and
futures prices are nonstationary in levels but stationary in first differences. Thus, regressions
must be constructed in terms of the stationary, first differenced variables.
3
FIGURE 1
EXPORT PRICES & FUTURES PRICES
40-
35-
30
-0 25-
20-
15-
10-
Jan88 Jan89 Jan90 Jan91 Jan92 Jan93 Jan94 Jan95 Jan96 Dec96
Year
-|- Export Price I Futures Price
Table la
Tests of Stationarity (Jan. 1988-Dec. 1996)
Variable Dickey-Fuller D-F (statistic) Augmented D -F
(1) Spot Price (Levels) -2.69 -2.08 (6 lags)
(2) Futures Price (Levels) -2.32 -1.97 (6 lags)
(3) Spot Price (Differences) -6.57** -5.06**(6 lags)
(4) Futures Price (Differences) -6.51** .-5.25**(6 lags)
Note: The critical value of the D-F statistic for 100 observations is -3.45. ** indicates significance at the
95% level.
Table lb
Test of Basis Risk (Jan. 1988-Dec. 1996)
Regression a I f I R2 I D-W
ASt = a + flFt -.02 .96**) .81 2.37
(-.28) (21.41)
Notes:
1. ASt= St - St-,, AFt=Ft - Ft-I
2. D-W: Durbin-Watson Statistic
3. ** indicates significance at the 95% level. T-statistics are in parentheses.
4. The stationarity test involves the following regression:
AXt = a + flXt-I + :P ?i AXt-i + et
i=0
If Si = 0, Vi this is referred to as the Dickey Fuller (D-F) test. If S5i X 0, Vi this is
called the Augmented Dickey Fuller (ADF) test. The optimal lag length, p, is chosen
using Akaike's Iformation Criterion (AIC); p is the value that minimize's AIC.
If 8 is significant, the null hypothesis of nonstationarity is rejected. A significant D-F test
statistic thus rejects the null, implying stationarity. Note that the spot and futures prices in
levels are non-stationary but the spot and futures prices in first differences are stationary.
5
The world's largest oil futures market is the NYMEX2. The NYMEX crude oil futures
contract which was introduced in March 1983 is based on pipeline delivery of 1000 barrels of
West Texas Intermediate (WTI) crude in Cushing, Oklahoma. The quality of Ecuadorian Oriente
is similar but not identical to WTI crude. If the quality of the spot (cash) commodity is identical
to the quality of the commodity specified in the futures contract, the usual recommendation is
to hedge all of the spot commodity since the spot and futures price in this case tend to be highly
correlated. This type of hedge is called a "naive" hedge. But since Ecuadorian Oriente differs
from WTI crude, the effectiveness of "cross-hedging" Ecuadorian crude using the WTI futures
contract needs to be determined.
Since Ecuadorian crude differs from the WTI crude specified in the futures contract there
will be some divergence between the time series behavior of Ecuadorian spot prices and WTI
futures prices. This divergence is called "basis" risk. In general, the greater the correlation
between spot and futures prices, the more effective the hedge. Since R-square (R2) is essentially
a measure of correlation, hedging effectiveness is measured by R2, and basis risk by 1-R2.
Table lb reports the results of a regression of spot price changes on (nearby) futures price
changes. The R2 of .81 and basis risk of .19 indicates that Ecuadorian crude can be hedged using
the WTI futures contract.3
2 Other exchanges that trade crude oil and petroleum futures
are the IPE (International Petroleum Exchange), SIMEX (Singapore
International Monetary Exchange) and ROEFEX (Rotterdam Exchange).
Liquidity is however highest in the NYMEX. Besides liquidity
considerations, Latin American countries prefer hedging on the
NYMEX because of time zone and trading hour considerations.
3 Note that the regression is constructed in terms of
stationary or differenced variables. Hedging effectiveness is
sometimes measured as the R2 of a regression of price levels. This
would be incorrect in our case given that we have determined spot
6
Ell. RISK AVERSION AND RETURN-RISK TRADE-OFFS
To illustrate the benefits of hedging, a simple framework is presented here depicting the
hedging decision as a portfolio selection problem in which the hedger selects the optimal
proportions of unhedged (spot) and hedged (futures) output4. The portfolio can then be
represented as:
ERp = Q. E(St+l - St) + Qh E(Ft+l - F) ................. (1)
where:
ERp = Expected return on the hedged portfolio
Q= Unhedged (spot) output or output available for export
E(S,+, - S) = Expected change in the Ecuadorian export price from time t to t+ 1
Hedged output
E(Ft+ I- Ft) = Expected change in the futures price from time t to t+ 1
At time period t, St and Ft are known but S,+, and F,+, are unknown; St+, and F,+, are thus
random variables.'
and futures price levels to be nonstationary.
4 The model here is similar to that in Satyanarayan, Thigpen
and Varangis (1993).
'We have not incorporated costs into the model. These costs
include brokerage fees and the opportunity cost of holding a margin
account - i.e., the difference between the interest bearing notes
of the margin account and investing somewhere else. However, these
costs are considered very small.
7
The issue to be determined is if the country is better off not hedging as compared to some
hedging. Here we will consider only the use of a "short-hedge" to insure against price declines.
(A short hedge is one in which the hedger sells futures contracts). In a short hedge, a long
position in the spot market (Q > 0) is offset by a short position in the futures market (Q <
0). Let h = (Q / Q,). If the value of Q, is set equal to 1, h can be interpreted as the hedge ratio
- the percentage of the spot or cash position that is hedged in the futures market. Thus for a
short hedger,
ERp = E(St+, - St) - h E(F,+1 - F) ................... (2)
If the portfolio is completely hedged, that is, each unit in the spot market is hedged with a unit
of futures, then h = 1 ( i.e. naive hedge). If h = 0, then there is no hedging and the expected
return on the portfolio is simply equal to the return on the spot market.
The Variance (Varp) or risk of the portfolio is given by:
Varp = Var(S) + h2 Var(F) - 2 h cov(S,F) ................ (3)
where:
Var(S), Var(F) = variance of spot and futures price changes
cov(S,F) = covariance between spot and futures price changes
8
The expected utility (EU) function of the Ecuadorian hedger is a function of the expected
return (ERp) and variance of the portfolio (Varp). Thus,
EU = E(Rp) - X Varp .................. (4)
where X is a risk aversion parameter. Higher (lower) values of A imply higher (lower) levels of
risk aversion. The model above is a mean-variance model (see Markowitz (1959)) and implicitly
assumes that the hedger has a quadratic utility function or that returns are normally distributed.6
The optimization problem is to select the hedge ratio which will maximize EU. Thus,
8EU/8h = - E(F,+1-Ft) - 2Xh Var(F) + 2X cov(S,F) = 0
Solving for the optimal (utility-maximizing) hedge ratio, h**, from the above gives:
hi+ = [cov(S,F) / Var(F)] + [(Ft-E(Ft+1)) / 2X Var(F) . (5)
Let h* = [cov(S,F) / Var(F)]. The above may then be rewritten as:
h** = h* + ( [Ft-E(Ft+,)] / [2X Var(F)] ). (6)
6 Quadratic utility functions raise several theoretical
problems (see Arrow, 1971) but work by Levy and Markowitz (1979)
and Kroll, Levy, and Markowitz (1984) suggest that the assumption
of quadratic utility is a reasonable empirical approximation.
9
With infinite risk aversion X-1o and the second term disappears. Therefore, for a risk minimizer
the first term in the equation above, h*, is the only relevant one. The variable h* is called the
hedging component and is equivalent to the risk-minimizing hedge ratio. Note that h* is the
slope coefficient of an OLS regression of spot pice changes (dependent variable) on futures price
changes (independent variable). With infinite risk aversion, the optimal or utility maximizing
hedge ratio is the same as the risk minimizing hedge ratio (i.e. h** = h*).
The second term in (6) is called the speculative component and implies that the greater the
level of risk aversion, the smaller the speculative component. The speculative component is
however positively related to the "bias" (Ft-E[Ft+1]) between the current and the expected futures
price. The speculative component essentially captures the effect of short hedging on expccted
returns.7 If the expected futures price is less than the current futures price, the hedger benefits
from selling ahead more of his output.
Table 2 reports ex-ante (before the resolution of uncertainty) and ex-post (after the
resolution of uncertainty) risk minimizing hedge ratios and contrasts the performance of four
portfolios - unhedged, naive, ex-ante hedged and ex-post hedged for the years 1991-96. We
assume that hedges are placed at the beginning of each year by buying the one year crude oil
futures contract on the NYMEX and continued until December, a month before the contract
7Equation 6 also implies that if the current futures price is
an unbiased estimate of the expected futures price (i.e. Ft =
E[F,+11), the speculative component in h** disappears and h** = h*.
Thus in an unbiased futures market, the risk-minimizing hedge ratio
is equal to the optimal hedge ratio. Also, with infinite risk
aversion the optimal hedge ratio is independent of this bias. See
McKinnon (1967) and Rolfo (1980).
10
TABLE 2
Performance of Hedged and Unhedged Portfolios (1991-1996)
Period I Portfolio Hedge Ratio Portfolio Portfolio Risk
Return Variance Reduction
. (US$/barrel)
1991 Hedge
Jan 88 - Dec 90 Unhedged h =0 -.7758 4.10 -
Naive h =1 -.5558 1.52 63%
Ex-Ante Hedged h= 1.03 -.5492 1.48 64%
Jan 91 - Dec 91 Ex-Post Hedged h= 1.64 -.4150 1.05 74%
1992 Hedge
Jan 89 - Dec 91 Unhedged h=O .1192 .7483 -
Naive h=1 .1075 .1850 75%
Ex-Ante Hedged h=1.05 .1069 .1701 77%
Jan 92 - Dec 92 Ex-Post Hedged h= 1.62 .1003 .0888 88%
1993 Hedge
Jan 90 - Dec 92 Unhedged h=0 -.4508 .7912 -
Naive h=1 -.2300 .1938 76%
Ex-Ante Hedged h=1.04 -.2212 .1820 77%
Jan 93 - Dec 93 Ex-Post Hedged h=1.53 -.1130 .1123 86%
1994 Hedge
Jan 91 - Dec 93 Unhedged h=0 .2783 .5504 -
Naive h=1 .2275 .4143 25%
Ex-Ante Hedged h= 1.05 .2250 .4166 24%
Jan 94 - Dec 94 Ex-Post Hedged h=.89 .2331 .4123 I 25%
1995 Hedge |
Jan 92 - Dec 94 Unhedged h=0 .0850 .5796 -
Naive h=1 -.0742 .3392 41%
Ex-Ante Hedged h=.88 -.0551 .3189 45%
Jan 95 - Dec 95 Ex-Post Hedged h=.76 -.0360 .3120 46%
1996 Hedge
Jan 93 - Dec 95 Unhedged h=0| .4650 1.8134 -
Naive h=1 -.0658 2.0624 -14%
| Ex-Ante Hedged h=.76 .0616 1.6748 8%
Jan 96 - Dec 96 Ex-Post Hedged I h=.43 | .2367 1.48 18%
11
expires.8 The ex-ante risk minimizing hedge ratios in Table 2 are estimated using information
available only up to the period in which the hedge was placed. Thus, the 1991 hedge is
estimated using information available only upto Dec. 19909. The ex-post hedge on the other
hand is estimated using the actual spot and futures prices that prevailed over the hedge period.
The ex-post portfolio is therefore a benchmark to compare the performance of the other hedges
since the ex-post hedge is based on complete information and thus yields the maximum amount
of risk reduction.
The results in Table 2 show that in every one of the hedges the variance or risk of the
unhedged position exceeded the risk of the ex-ante hedged position. The risk reduction benefits
of the ex-ante hedges"0 range from a reduction in risk of 77% for the 1992 and 1993 hedges
to 8% for the 1996 hedge. Thus, there are clearly substantial risk reduction benefits from
hedging Ecuadorian oil. Notice also that the naive portfolio is less risky than the unhedged
portfolio in all hedges except the 1996 hedge. For the 1996 hedge, a naive strategy would have
actually resulted in increasing rather than decreasing portfolio variance. This simply underscores
the fact that naive hedges are not appropriate for hedging Ecuadorian oil since the level of basis
8 There is no reason as to why the timing and duration of the
hedges cannot be different from that assumed in our paper. We chose
the one year contract over a shorter contract, in order to provide
simulation results over a longer period.
9 In estimating the ex-ante hedge ratios, we use information
up to three years prior to the period in which the hedge is placed.
This is to ensure that only relatively recent information is used
in iconstructing the ex-ante hedge ratios.
10The percentage reduction in risk (1- [Var(Hedged)/Var
(Unhedged)]) is identical to the coefficient of determination, R2,
in a regression of spot price changes (dependent variable) on
futures price changes (independent variable) . See Ederington (1979)
for a detailed derivation of this result.
12
risk is high.
An aspect of hedging that does not receive much attention is the fact that hedging carries
an opportunity cost in terms of foregone returns. Whether the hedger considers these costs
reasonable or not depends upon the hedger's degree of risk aversion. We turn now to a
discussion of these costs and the effect of risk aversion on the hedging decision.
We estimated ex-post optimal hedge ratios at different levels of risk aversion using the
1994 futures contract as an example. Table 3 reports optimal hedge ratios at different levels of
risk aversion and associated return and risk levels. For values of X between 100 and infinity, the
optimal hedge ratio is essentially constant implying that for these values of risk aversion the
speculative component is insignificant"1. Thus, it seems that the optimal hedging strategy is not
significantly different for reasonable levels of risk aversion. At values of X equal to or lesser
than .10, the results imply that Ecuador should buy rather than sell futures (i.e. negative values
of h** imply a long position in futures). This is not surprising in view of the relation that existed
between Ft and E(F,+1) over the life of the 1994 contract. Over the hedge period, the mean value
of (Ft+1-Ft) was equal to .0508 (U$/barrel). Given that the expected futures price, on average,
exceeds the current futures prices over the life of this contract, the recommendation is to go net
long in futures at lower levels of risk aversion to profit from this price bias.
We calculated portfolio returns and variances for hedge (h) ratios between 0 and 1.
These results are reported in Table 4 and graphed in Figure 2. Figure 2 is a mean-standard
deviation portfolio opportunity frontier and depicts the return and risk trade-offs from hedging
11 This result is similar to Rolfo's (1980) result on optimal
hedging for cocoa producing countries and Ouattara, Schroeder, and
Sorenson's (1992) work on coffee hedging for C6te d'Ivoire.
13
Table 3
Optimal Hedge Ratios, Portfolio Return and Risk at Different Levels of Risk Aversion
Risk Aversion Optimal Hedge Portfolio Portfolio
Parameter Ratio Return Standard
A___________h** (US$/barrel) Deviation
oo .89 .23 .64
10,000 .889 .23 .64
1,000 .889 .23 .64
100 .888 .23 .64
10 .875 .23 .64
1 .743 .24 .65
.10 -.574 .31 .89
.01 -13.75 .98 6.14
001 -145.54 7.68 61.01
.0001 -1463.43 74.67 610.07
Table 4
Risk-Return Trade-Offs
Optimal Implied
Hedge Risk % %
Ratio Aversion Porfolio Portfolio Reduction Reduction
h** Parameter Return Variance in Return in Risk Cost
0 .1645 .2783 .5504 - -
.10 .1853 .2733 .5212 1.8 5.3 .34
.20 .2122 .2682 .4954 3.7 10.0 .37
.30 .2482 .2631 .4731 5.5 14.0 .39
.40 .2988 .2580 .4543 7.3 17.5 .42
.50 .3755 .2529 .4390 9.1 20.3 .45
.60 .5049 .2478 .4271 11.0 22.4 .49
.70 .7707 .2428 .4187 12.8 23.9 .53
.80 1.6270 .2377 .4138 14.6 24.8 .59
.89* x* .2331* .4122* 16.3 25.1* .65*
.90 -14.64 .2326 .4123 16.4 25.1 .66
1.00 -1.33 .2275 .4143 18.3 24.7 .74
Note: * indicates values associated with the minimum-variance portfolio.
14
FIGURE 2
RETURN-RISK TRADE-OFFS FROM HEDGING
0.28-
0.27-
o 0.251 0
0 00
0.26-
a)
o 0.825
0
0
EL
0.7
0.24-
0.23-
1
0.22- X l . l l l
0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72 0,73 0.74 0.75
Portfolio Standard Deviation
Note: The numbers on the portfolio opportunity frontier refer to hedge ratios.
M stands for the minimum risk portfolio
Ecuadorian oil. The highest return and the highest risk (standard deviation) are associated with
the unhedged portfolio (h=O). The minimum risk portfolio corresponds to Point M with an
associated return of .2331 (U$/barrel) and a standard deviation of .642 (variance of .4122). In
between the hedge ratios of 0 and .89, lie successive portfolios corresponding to lower risk but
also lower return. Note that portfolios on the negatively sloped portion of the opportunity set can
be eliminated. These portfolios are inefficient because for the same risk, portfolios on the
positively sloped portion yield a higher return.
Figure 2 illustrates the basic policy dilemma faced by the hedger. The fundamental issue
is if it is worth foregoing the unhedged rate of return and insuring against possible oil price
declines by accepting a lower rate of return.. The decision to hedge is influenced by the level of
risk aversion. Other important considerations in the hedging decision is the cost of the structural
adju.stments (fiscal and budgetary adjustments) often undertaken in the face of unexpected price
declines.
We also calculated the explicit costs of hedging Ecuadorian oil. Hedging is effective if the
decrease in risk is sufficient to compensate the hedger for the decrease in return. We compared
the return and variance of the unhedged and hedged positions to calculate a cost elasticity
measure as follows:
Cost of Hedging = (Percentage Reduction in Return) / (Percentage Reduction in Variance);
where:
% Reduction in Return = 1 - [(Return of Hedged) / (Return of Unhedged)]
% Reduction in Risk = 1- [Variance (Hedged) / Variance (Unhedged)]
16
These cost elasticities are shown in the last column oiF Table 4 and range between .34 to .74,
with larger values implying higher costs of risk reduction. The cost associated with the
minimum-variance portfolio is .65 which implies that a 1% reduction in risk will result in a
.65 % reduction in return"2. Whether this is a reasonable cost of risk reduction or not depends
upon the hedgers's degree of risk aversion.
IV. CONCLUDING REMARKS
This paper investigates methods to reduce risk for Ecuadorian oil exports through hedging
in futures markets. We find that hedging Ecuadorian oil has significant risk reduction potential.
We simulated ex-ante cross hedges for 1991-96 and found that in each case, ex-ante hedging was
effective in reducing price risk. We calculated the return and risk trade-offs from hedging
Ecuadorian oil and found that for a risk minimizing short hedger, a 1 % reduction in risk would
have cost a reduction in return of .65 %.
We conclude that there are risk reduction benefits from hedging Ecuadorian oil. We have
provided some estimates of the opportunity costs of hedging that may aid in the hedging
decision.
12 The portfolio opportunity frontier (and thus return-risk
trade-offs) will change depending on the levels, variances and
covariances of spot and futures price changes and would be
different in another period. The resuLts here are indicative of the
nature of the trade-offs prevailing in this market.
17
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Granger, C.W.J. and Newbold P., "Spurious Regressions in Econometrics", Journal of
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Kroll Y., Levy H. and Markowitz H., "Mean-Variance Versus Direct Utility Maximization",
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Levy H. and Markowitz H., "Approximating Expected Utility by a Function of Mean and
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18
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Developing Countries
WPS1774 The Demand Tor Base Money Valeriano F. Garcia June 1997 J. Forgues
arnd the Sustainability of Public 39774
Debt
WPS1775 Can High-Inflatior Developing Martin Ravallion June 1997 P. Sader
Countries Escape Absolute Poverty? 33902
WPS1776 From Prices to incomes: Agricultural John Bales June 1997 P. Kokila
Subsidization 'Without Protection? Jacob Meerman 33716
WPS1777 Aid, Policies, and Grmvvh Craig Burnside June 1997 K. Labrie
David Dollar 31001
WPS1778 How Government Policies Affect Szozepan Figiel June 1997 J. Jacobson
the Relationship between Polish Torn Scott 33710
and World Wheat Prices Panos Varangis
WPS1779 Water AllocaUion Mschanisms: Ariel Dinar June 1997 M. Rigaud
Principles and Examples . iark V. Rosegrant 30344
Ruth Meinzen-Dick
WPS17S0 High-Level Rent-Seeking and Jacqueline Coolidge June 1997 N. Busjeet
Corruption in African Regimes: Susan Rose-Ackerman 33997
Theory and Cases
WPS1781 Technology Accumuiation and Fie Carlo Padoan June 1997 J. Ngaine
Diffusion: Is There a , Regionial 37947
Dimension?
WPS1782 Regional Integration and the Prices L. Alan Winters June 1997 J. Ngaine
of Imports: An Empirical W,?fo n Whan.g 37947
Investigation
WPS1783 Trade POajcy Options ?Or the Gienn W. Hsrrisorn June 1997 J. Ngaine
Chilean Government: A Quanttative Thomas F. Rutiheford 37947
Evaluation David G. Tarr
WPS1784 Analyzing the Sustainability of Fiscal John T. Cuddirgton June 1997 S. King-Watson
Deficits in Developing Ccountries 31047
WPS1785 The Causes of Governme,t eno !d the Cmon mmornander June 1997 t Witte
Consequences for Growth and Hamid R. Davoodi 85637
Well-Being Une J. Lee
WPS1786 The Ecnononics oF Customis Unions Constantine Alichetopoulos June 1997 M. PateFra
in the Commonwealth of David Tarr 39515
Independent States
Policy Research Working Paper Series
Contact
Title Author Date for paper
'NPS1787 Trading Arrangements and Diego Puga June 1997 J. Ngaine
industrial Development Anthony J. Venables 37947
WPS1 788 An Economic Analysis of Woodfuel Kenneth M. Chomitz June 1997 A Maranon
Management in the Sahel: The Case Charles Griffiths 39074
of Chad
'NPS1789 Competition Law in Bulgaria After Bernard Hoekman June 1997 J. Ngaine
Central Planning Simeon Djankov 37947
WPS1790 Interpreting the Coefficient of Barry R. Chiswick June 1997 P. Singh
Schooling in the Human Capital 85631
Earnings Function
WNPS1791 Toward Better Regulation of Private Hemant Shah June 1997 N. Johi
Pension Funds 38613