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Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts

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Author Info
Akgiray, Vedat
Abstract

This article presents new evidence about the time-series behavior of stock prices. Daily return series exhibit significant levels of second-order dependence, and they cannot be modeled as linear white-noise processes. A reasonable return-generating process is empirically shown to be a first-order autoregressive process with conditionally heteroskedastic innovations. In particular, generalized autoregressive conditional heteroskedastic GARCH (1, 1) processes fit to data very satisfactorily. Various out-of-sample forecasts of monthly return variances are generated and compared statistically. Forecasts based on the GARCH model are found to be superior. Copyright 1989 by the University of Chicago.

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Publisher Info
Article provided by University of Chicago Press in its journal Journal of Business.

Volume (Year): 62 (1989)
Issue (Month): 1 (January)
Pages: 55-80
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Handle: RePEc:ucp:jnlbus:v:62:y:1989:i:1:p:55-80

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