@inbook{8b9cbe0b2fd74cf9afbdf0c18100d998,
title = "A source of long memory in volatility",
abstract = "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.",
keywords = "Fractional integration, Long memory, Regime switching, Structural breaks, Volatility components, Volatility forecasting",
author = "Namwon Hyung and Poon, {Ser Huang} and Granger, {Clive W.J.}",
year = "2008",
doi = "10.1016/S1574-8715(07)00209-6",
language = "English",
isbn = "9780444529428",
series = "Frontiers of Economics and Globalization",
publisher = "Emerald Group Publishing Ltd.",
pages = "329--380",
booktitle = "Forecasting in the Presence of Structural Breaks and Model Uncertainty",
address = "United Kingdom",
}