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.
Hyung, N., Poon, S.-H. and Granger, C.W.J. (2008), "Chapter 9 A Source of Long Memory in Volatility", Rapach, D.E. and Wohar, M.E. (Ed.) Forecasting in the Presence of Structural Breaks and Model Uncertainty (Frontiers of Economics and Globalization, Vol. 3), Emerald Group Publishing Limited, Bingley, pp. 329-380. https://doi.org/10.1016/S1574-8715(07)00209-6
Emerald Group Publishing Limited
Copyright © 2008, Emerald Group Publishing Limited