Title: Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts
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.
Publication Year: 1989
Publication Date: 1989-01-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 867
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot