Additive outliers, GARCH and forecasting volatility

Additive outliers, GARCH and forecasting volatility

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Article ID: iaor19993242
Country: Netherlands
Volume: 15
Issue: 1
Start Page Number: 1
End Page Number: 9
Publication Date: Jan 1999
Journal: International Journal of Forecasting
Authors: ,
Abstract:

The Generalized Autoregressive Conditional Heteroscedasticity [GARCH] model is often used for forecasting stock market volatility. It is frequently found, however, that estimated residuals from GARCH models have excess kurtosis, even when one allows for conditional t-distributed errors. In this paper we examine if this feature can be due to neglected additive outliers [AOs], where we focus on the out-of-sample forecasting properties of GARCH models for AO-corrected returns. We find that models for AO-corrected data yield substantial improvement over GARCH and GARCH-t models for the original returns, and that this improvement holds for various samples, two forecast evaluation criteria and four stock markets.

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