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Forecasting volatility in commodity markets

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Author Info

  • Kroner, Kenneth F.
  • Kneafsey, Devin P.
  • Claessens, Stijn
  • DEC

Abstract

Commodity prices have historically been among the most volatile of international prices. Measured volatility (the standard deviation of price changes) has not been below 15 percent and at times has been more than 50 percent. Often the volatility of commodity prices has exceeded that of exchange rates and interest rates. The large price variations are caused by disturbances in demand and supply. Stockholding leads to some price smoothing, but when stocks are low, prices can jump sharply. As a result, commodity price series are not stationary and in some periods they jump abruptly to high levels or fall precipitously to low levels relative to their long-run average. Thus it is difficult to determine long-term price trends and the underlying distribution of prices. The volatility of commodity prices makes price forecasting difficult. Indeed, realized prices often deviate greatly from forecasted prices, which has led to the practice of giving forecasts probability ranges. But assigning probability ranges requires forecasting future price volatility, which, given uncertainties about true price distribution, is difficult. One potentially useful source of information for forecasting volatility is the volatility forecasts imbedded in the prices of options written on commodities traded in exchanges. Options give the holder the right to buy (call) or sell (put) a certain commodity at a certain date at a fixed (exercise) price. Options prices depend on several variables, one of which is the expected volatility up to the maturity date. Given a specific theoretical model, the market prices of options can be used to derive the market's expectations about price volatility and the price distribution. The authors systematically analyze different methods'abilities to forecast commodity price volatility (for several commodities). They collected the daily prices of commodity options and other variables for seven commodities (cocoa, corn, cotton, gold, silver, sugar, and wheat). They extracted the volatility forecasts implicit in options prices using several techniques. They compared several volatility forecasting methods, divided into three categories: (1) forecasts using only expectations derived form options prices; (2) forecasts using only time-series modeling; (3) forecasts that combine market expectations and time-series modeling (a new method devised for this purpose). They find that the volatility forecasts produced by method 3 outperform the first two as well as the naive forecast based on historical volatility. This result holds both in and out of sample for almost all commodities considered.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 1226.

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Date of creation: 30 Nov 1993
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Handle: RePEc:wbk:wbrwps:1226

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Keywords: Markets and Market Access; Access to Markets; Economic Theory&Research; Economic Forecasting; Science Education;

References

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  1. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  2. Shang-Jin Wei & Jeffrey A. Frankel, 1991. "Are Option-Implied Forecasts of Exchange Rate Volatility Excessively Variable?," NBER Working Papers 3910, National Bureau of Economic Research, Inc.
  3. Lamoureux, Christopher G & Lastrapes, William D, 1993. "Forecasting Stock-Return Variance: Toward an Understanding of Stochastic Implied Volatilities," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 293-326.
  4. Taylor, Stephen J., 1987. "Forecasting the volatility of currency exchange rates," International Journal of Forecasting, Elsevier, vol. 3(1), pages 159-170.
  5. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
  6. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. " Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-20, June.
  7. Latane, Henry A & Rendleman, Richard J, Jr, 1976. "Standard Deviations of Stock Price Ratios Implied in Option Prices," Journal of Finance, American Finance Association, vol. 31(2), pages 369-81, May.
  8. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
  9. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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Cited by:
  1. Christian Walter & Jose Lopez, 1997. "Is implied correlation worth calculating? Evidence from foreign exchange options and historical data," Research Paper 9730, Federal Reserve Bank of New York.
  2. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
  3. Neely, Christopher J., 2009. "Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 188-205, February.
  4. Jose M. Campa & P.H. Kevin Chang & James F. Refalo, 1999. "An Options-Based Analysis of Emerging Market Exchange Rate Expectations: Brazil's Real Plan, 1994-1997," NBER Working Papers 6929, National Bureau of Economic Research, Inc.
  5. Shao, Renyuan & Roe, Brian E., 2001. "Underpinnings for Prospective, Net Revenue Forecasting in Hog Finishing: Characterizing the Joint Distribution of Corn, Soybean Meal and Lean Hogs Time Series," 2001 Annual meeting, August 5-8, Chicago, IL 20664, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  6. Claessens, Stijn & Qian, Ying, 1995. "Bootstrapping options: An application to recapture clauses," Economics Letters, Elsevier, vol. 47(3-4), pages 377-384, March.
  7. Jose M. Campa & P. H. Kevin Chang, 1997. "The Forecasting Ability of Correlations Implied in Foreign Exchange Options," NBER Working Papers 5974, National Bureau of Economic Research, Inc.
  8. Jose M. Campa & P.H. Kevin Chang & Robert L. Reider, 1997. "Implied Exchange Rate Distributions: Evidence from OTC Option Markets," NBER Working Papers 6179, National Bureau of Economic Research, Inc.
  9. Fong, Wai Mun & See, Kim Hock, 2002. "A Markov switching model of the conditional volatility of crude oil futures prices," Energy Economics, Elsevier, vol. 24(1), pages 71-95, January.
  10. Geyser, Mariette & Cutts, Michela, 2007. "SAFEX maize price volatility scrutinised," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 46(3), September.
  11. Christian Dunis & Jason Laws & Stephane Chauvin, 2003. "FX volatility forecasts and the informational content of market data for volatility," The European Journal of Finance, Taylor & Francis Journals, vol. 9(3), pages 242-272.

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