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Statistical Models for Financial Volatility

Robert F. Engle
Financial Analysts Journal
Vol. 49, No. 1 (Jan. - Feb., 1993), pp. 72-78
Published by: CFA Institute
Stable URL: http://www.jstor.org/stable/4479615
Page Count: 7
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Statistical Models for Financial Volatility
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Abstract

This paper develops tools for measuring and forecasting volatility when it varies over time. A variety of popular models for conditional variances, including ARCH, GARCH and EGARCH, are presented and compared. The results suggest that volatility is forecastable. Such predictions may affect portfolio decisions. Volatility may consequently be priced, leading to time-varying risk premiums.

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