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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
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    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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    Bibliographic Info

    Article provided by Springer in its journal Review of Quantitative Finance and Accounting.

    Volume (Year): 20 (2003)
    Issue (Month): 3 (May)
    Pages: 245-65

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    Handle: RePEc:kap:rqfnac:v:20:y:2003:i:3:p:245-65

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    Web page: http://springerlink.metapress.com/link.asp?id=102990

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    Cited by:
    1. Hammoudeh, S.M. & Yuan, Y. & McAleer, M.J., 2010. "Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies," Econometric Institute Research Papers EI 2010-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Shawkat M. Hammoudeh & Yuan Yuan & Michael McAleer, 2009. "Exchange Rate and Industrial Commodity Volatility Transmissions and Hedging Strategies," CIRJE F-Series CIRJE-F-668, CIRJE, Faculty of Economics, University of Tokyo.

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