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Chapter 9 A Source of Long Memory in Volatility

In: Forecasting in the Presence of Structural Breaks and Model Uncertainty

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  • Namwon Hyung
  • Ser-Huang Poon
  • Clive W.J. Granger

Abstract

This paper compares the out-of-sample forecasting performance of three long-memory volatility models (i.e., fractionally integrated (FI), break and regime switching) against three short-memory models (i.e., GARCH, GJR and volatility component). Using S&P 500 returns, we find that structural break models produced the best out-of-sample forecasts, if future volatility breaks are known. Without knowing the future breaks, GJR models produced the best short-horizon forecasts and FI models dominated for volatility forecasts of 10 days and beyond. The results suggest that S&P 500 volatility is non-stationary at least in some time periods. Controlling for extreme events (e.g., the 1987 crash) significantly improved forecasting performance.

Suggested Citation

  • Namwon Hyung & Ser-Huang Poon & Clive W.J. Granger, 2008. "Chapter 9 A Source of Long Memory in Volatility," Frontiers of Economics and Globalization, in: Forecasting in the Presence of Structural Breaks and Model Uncertainty, pages 329-380, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:fegzzz:s1574-8715(07)00209-6
    DOI: 10.1016/S1574-8715(07)00209-6
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