Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts
AbstractThis 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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Bibliographic InfoArticle provided by University of Chicago Press in its journal Journal of Business.
Volume (Year): 62 (1989)
Issue (Month): 1 (January)
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Web page: http://www.journals.uchicago.edu/JB/
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