Chapter 9 A Source of Long Memory in Volatility

Namwon Hyung, Ser-Huang Poon and Clive Granger

A chapter in Forecasting in the Presence of Structural Breaks and Model Uncertainty, 2008, pp 329-380 from Emerald Group Publishing Limited

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.

Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.101 ... 1574-8715(07)00209-6
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:fegzzz:s1574-8715(07)00209-6

DOI: 10.1016/S1574-8715(07)00209-6

Access Statistics for this chapter

More chapters in Frontiers of Economics and Globalization from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().