An evaluation of volatility forecasting techniques

Timothy J. Brailsford*, Robert W. Faff

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

199 Citations (Scopus)

Abstract

The existing literature contains conflicting evidence regarding the relative quality of stock market volatility forecasts. Evidence can be found supporting the superiority of relatively complex models (including ARCH class models), while there is also evidence supporting the superiority of more simple alternatives. These inconsistencies are of particular concern because of the use of, and reliance on, volatility forecasts in key economic decision-making and analysis, and in asset/option pricing. This paper employs daily Australian data to examine this issue. The results suggest that the ARCH class of models and a simple regression model provide superior forecasts of volatility. However, the various model rankings are shown to be sensitive to the error statistic used to assess the accuracy of the forecasts. Nevertheless, a clear message is that volatility forecasting is a notoriously difficult task.

Original languageEnglish
Pages (from-to)419-438
Number of pages20
JournalJournal of Banking and Finance
Volume20
Issue number3
DOIs
Publication statusPublished - Apr 1996
Externally publishedYes

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