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Forecasting Changes in Copper Futures Volatility with GARCH Models Using an Iterated Algorithm

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  • Smith, Kenneth L
  • Bracker, Kevin

Abstract

There is a gap in the literature regarding the out-of-sample forecasting ability of GARCH-type models applied to derivatives. A practitioner-oriented method (iterated cumulative sum of squares) is applied to detecting breakpoints in the variance of two copper futures series. Short-, intermediate-, and long-term out-of-sample forecasts of copper future series are compared to forecasts from a benchmark random walk model for each series. Not only do the GARCH-type models dominate the random walk model, but the relative improvement is fairly consistent across series, forecast horizon, and GARCH-type model. The evidence makes clear that, with few exceptions, the forecast improvement of the GARCH-type models over the RW model lies somewhere between 20-30 percent. It is particularly true that for the long-term close to close forecasts, there is great coherence among the forecasts. These all fall within a fairly narrow range. Copyright 2003 by Kluwer Academic Publishers

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  • Smith, Kenneth L & Bracker, Kevin, 2003. "Forecasting Changes in Copper Futures Volatility with GARCH Models Using an Iterated Algorithm," Review of Quantitative Finance and Accounting, Springer, vol. 20(3), pages 245-265, May.
    • Handle: RePEc:kap:rqfnac:v:20:y:2003:i:3:p:245-65
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    Cited by:

    1. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    2. Zian Wang & Xinyi Lu, 2024. "COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning," Papers 2409.08356, arXiv.org.
    3. Chan, Wing Hong & Young, Denise, 2009. "A New Look at Copper Markets: A Regime-Switching Jump Model," Working Papers 2009-13, University of Alberta, Department of Economics.
    4. Shawkat M. Hammoudeh & Yuan Yuan & Michael McAleer, 2009. "Exchange Rate and Industrial Commodity Volatility Transmissions and Hedging Strategies," CARF F-Series CARF-F-172, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    5. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
    6. Shawkat M.Hammoudeh & Yuan Yuan & Michael McAleer, 2010. "Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies," Working Papers in Economics 10/33, University of Canterbury, Department of Economics and Finance.
    7. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    8. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    9. Phillip A. Cartwright & Natalija Riabko, 2019. "Do spot food commodity and oil prices predict futures prices?," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 153-194, July.
    10. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    11. Guo, Jin, 2018. "Co-movement of international copper prices, China's economic activity, and stock returns: Structural breaks and volatility dynamics," Global Finance Journal, Elsevier, vol. 36(C), pages 62-77.
    12. Joe Brocato & Kenneth Smith, 2012. "Sudden equity price declines and the flight-to-safety phenomenon: additional evidence using daily data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(3), pages 712-727, July.
    13. Farooq Malik, 2015. "Revisiting the relationship between risk and return," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 25-40, January.
    14. Zian Wang & Xinshu Li, 2024. "On the macroeconomic fundamentals of long-term volatilities and dynamic correlations in COMEX copper futures," Papers 2409.08355, arXiv.org.

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