A source of long memory in volatility

Namwon Hyung, Ser Huang Poon, Clive W.J. Granger

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

17 Scopus citations


This paper compares the out-of-sample forecasting performance of three longmemory 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.

Original languageEnglish
Title of host publicationForecasting in the Presence of Structural Breaks and Model Uncertainty
PublisherEmerald Group Publishing Ltd.
Number of pages52
ISBN (Print)9780444529428
StatePublished - 2008

Publication series

NameFrontiers of Economics and Globalization
ISSN (Print)1574-8715


  • Fractional integration
  • Long memory
  • Regime switching
  • Structural breaks
  • Volatility components
  • Volatility forecasting


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