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High Breakdown Point Conditional Dispersion Estimation with Application to S & P 500 Daily Returns Volatility
Shinichi Sakata
Halbert White
Abstract
We show that quasi-maximum likelihood (QML) estimators for conditional dispersion models can be severely affected by a small number of outliers such as market crashes and rallies, and we propose new estimation strategies (the two-stage Hampel estimators and two-stage S-estimators) resistant to the effects of outliers and study the properties of these estimators. We apply our methods to estimate models of the conditional volatility of the daily returns of the S&P 500 Cash Index series. In contrast to QML estimators, our proposed method resists outliers, revealing an informative new picture of volatility dynamics during "typical" daily market activity.
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