An important characteristic of the 1970s and 1980s has been the large volatility of primary commodity spot prices. For instance, from 1971 to 1974 prices of food commodities (in SDRs) rose by over 100 percent, and then fell by 25 percent from 1974 to 1977. More recently, during 1983–86 prices of metals and minerals fell by 23 percent, then rose by 54 percent from 1986—88. This instability in commodity prices has affected the export earnings of a large number of developing countries dependent on the export of a handful of commodities, or even a single commodity. To the extent that many developing countries are net importers of these commodities, their import bills have also fluctuated considerably. The fluctuations have had a serious impact on their income and consumption, leading them to seek ways of reducing the fluctuations, or at least reducing their impact. At the macroeconomic level the impact on economic management can be reduced, for instance, by the authorities’ use of additional official funding as provided by the International Monetary Fund’s (IMF) compensatory and contingency financing facility (CCFF).1 At the more disaggregated level the risks being faced by individual agents or groups of agents can be reduced by using available market instruments. It is in the latter context that hedging via the futures markets can play an important role, which in turn may also have important stabilizing effects in the aggregate.
Commodity futures are, of course, hardly new. The operations of several of the futures markets go back nearly a century. However, the recent sharp expansion in the size of these operations, together with advances in communications, means that futures markets could make a substantial contribution to improving developing countries’ welfare.
A critical issue for any developing country contemplating the use of futures markets is the cost of using these markets. The costs are essentially of two kinds. The first one arises from the returns that may be demanded by other investors for assuming the risk of future spot price volatility—that is, the risk premium. The second cost arises from any market failure. If the market is not using publicly available information efficiently, futures prices become biased predictors of future spot prices, entailing additional costs in using the markets.2
An evaluation of these two types of costs revolves around the issue of market efficiency. According to the efficient-market hypothesis, the expected excess rate of return to speculation in the futures market for commodities should be zero. Since excess returns to futures speculation can be decomposed into two components—the risk-premium component and the forecasting-error component—a test of the efficiency hypothesis can provide an indication of the costs due to one or both of these components.
Despite extensive empirical research on futures markets, there is little agreement on the extent to which these markets can be characterized as approximately efficient. The reasons for the lack of consensus include empirical evidence based on nonuniform commodity samples, time periods, and econometric techniques. In any case, very few studies have examined the data for the 1980s, which has been a highly volatile period. The exercises this paper undertakes focus on the futures prices for seven commodity markets over the period 1976–88. A number of different econometric tests are used to evaluate the degree of efficiency of these markets and the ability of futures prices to forecast accurately future spot prices.
The rest of the paper is arranged as follows. Section I contains a discussion of the efficient-market hypothesis and of the existing main empirical studies on the validity of this hypothesis in commodity markets. Section II presents some simple descriptive statistics of excess returns in futures markets. Here, the paper focuses primarily on the unconditional prediction errors of the futures prices. The regression tests for conditional unbiasedness are undertaken in Section III. Section IV notes the main implications of the empirical results and suggests directions for future research.
Bodie, Z., and V. Rosansky, “Risk and Return in Commodity Futures,” Financial Analysts Journal, Vol. 36 (May-June 1980), pp. 17 –39.
Danthine, Jean-Pierre, “Information, Futures Prices, and Stabilizing Speculation,” Journal of Economic Theory, Vol. 17 (March 1978), pp. 79 –98.
Dusak, K., “Futures Trading and Investors Returns: An Investigation of Commodity Market Risk Premiums,” Journal of Political Economy, Vol. 81 (November-December 1973), pp. 1387 –1406.
Fama, Eugene F., “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, Vol. 25 (May 1970), pp. 383 –417.
Frankel, Jeffrey, and Kenneth Froot, “Using Survey Data to Test Standard Propositions Regarding Exchange Rate Expectations,” American Economic Review, Vol. 77 (March 1987), pp. 133 –53.
Hansen, Lars, “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, Vol. 50 (July 1982), pp. 1029 –54.
Hansen, Lars, and R. Hodrick, “Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis,” Journal of Political Economy, Vol. 88 (February 1980), pp. 829 –53.
Hazuka, Thomas, “Consumption Betas and Backwardation in Commodities Markets,” Journal of Finance, Vol. 39 (July 1984), pp. 647 –55.
Hodrick, Robert J., The Empirical Evidence on the Efficiency of Forward and Futures Foreign Exchange Markets (New York: Harwood Academic Publishers, 1987).
Jagannathan, Ravi, “An Investigation of Commodity Futures Prices Using the Consumption-Based Intertemporal Capital Asset Pricing Model,” The Journal of Finance, Vol. 40 (March 1985), pp. 175 –91.
Kaminsky, Graciela, “The Peso Problem and the Behavior of the Exchange Rate: The Dollar-Pound Exchange Rate, 1976–1987” (unpublished; San Diego: University of California at San Diego, December 1988).
Kaminsky, Graciela, and Manmohan S. Kumar, “Efficiency in Commodity Futures Markets,” IMF Working Paper 89/106 (Washington: International Monetary Fund, December 1989).
Lucas, R.E., Jr., , “Interest Rates and Currency Prices in a Two-Country World,” Journal of Monetary Economics, Vol. 10 (April 1980), pp. 335 –60.
Newey, Whitney K., and Kenneth D. West, “Simple, Positive Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Economeirica, Vol. 85 (October 1987).
Pownall, Roger, and Brian Stuart, “The IMF’s Compensatory and Contingency Financing Facility,” Finance and Development, International Monetary Fund and World Bank, Vol. 25 (December 1988), pp. 9 –11.
Ross, Stephen A., under “Finance,” in A New Palgrave: A Dictionary of Economics, Vol. 2, ed. by John Eatwell, and others (London: Macmillan Press, 1987), pp. 322 –55.
Samuelson, Paul A., “Proof That Properly Anticipated Prices Move Randomly,” Industrial Management Review, Vol. 6 (April 1965), pp. 41 –49.
Graciela Kaminsky, an Assistant Professor of Economics at the University of California at San Diego, was a Visiting Scholar in the Research Department when this paper was written.
Manmohan S. Kumar, an Economist in the Research Department, is a graduate of the London School of Economics and Political Science. He received his Ph.D. from Cambridge University, where he also taught before joining the Fund.
The authors are grateful to Bijan Aghevli, Guillermo Calvo, and Peter Wick-ham for valuable advice and comments. Comments from Michael Dooley. Roger Pownall, Assaf Razin, Blair Rourke. and participants in a Research Department seminar are also gratefully acknowledged.
The CCFF was established in August 1988 replacing the former compensatory financing facility (CFF). The new facility preserves the basic features of compensatory financing and in addition provides contingency financing from the IMF to help members maintain the momentum of IMF-supported adjustment programs. For an account of the operations of the facility, see Pownall and Stuart (1988).
Another cost of operating in the futures market is the transaction cost (which includes brokers’ and other commission fees, the cost of maintaining margins, and others). This cost is, however, likely to be much smaller than the two discussed above and. in any case, does not raise any important conceptual issues.
See Ross (1987). The use of efficiency in the informational sense is different from the notion of Pareto efficiency, whereby an economy is efficient if it is not possible to produce more of any one good or service without lowering the output of some others.
As we discuss below, this is a necessary but not a sufficient condition for efficiency.
Although the information set at t + n is different from that at t, if markets are efficient there is no presumption, on average, that ft+n would exceed, or be less than ft.
Even more strongly, as will be discussed presently, when RPt = 0 (because investors are risk neutral, or because the sign of risk premium changes over time with its average being zero), μr+n ≠ 0 docs not necessarily imply that investors are irrational.
In a more recent study, Bodie and Rosansky (1980) found that if the Dusak sample is extended to a longer period (1950–76), the unconditional excess returns are significantly positive.
Hazuka (1984) examined one-month-to-maturity returns of futures contracts for agricultural commodities including corn, oats, sugar, and wheat, and metals such as copper and silver.
The reason for calling this a test of “unconditional” unbiasedness is that it is not dependent on any specific information set based on which expectations are to be taken. For instance, in subsequent analysis, the information set consists only of past prices of the same commodity or information on macro variables. In this case all publicly available information is included.
It can be shown using the Lucas (1980) model that this will be the stochastic process followed by spot prices if money supply follows a random-walk process with a changing drift δi, and output is constant.
This issue can also be analyzed in the standard Bayesian approach. Agents acquire new information in each period and revise prior beliefs continuously. The distribution of the information set, therefore, becomes tighter over time. This approach is consistent with the fact that markets appear to be more efficient over the short-term horizon than over the longer term.
There is a third “strong” form of efficiency, which asserts that all information that is known to any investor, including privately held information, is reflected in market prices. Thus, no abnormal excess returns are possible.
Note that in these tests the notion of efficient use of the available information imposes stronger restrictions than the one discussed in the previous section in which investors had incomplete information about the stochastic process followed by the variable in question.
It should be noted that the rejection of the efficiency hypothesis for forecasts greater than three months was corroborated by an analysis of the out-of-sample predictive power of futures excess returns vis-à-vis the model in Table 5. For instance, the efficiency gain by using the model in Table 5 for soybeans for a nine-month forecast horizon for the period 1986–88 was 32 percent; for copper, for the same forecast horizon and time period, it was nearly 30 percent. The detailed results of these tests are provided in Kaminsky and Kumar (1989).