High Breakdown Point Conditional Dispersion Estimation with Application to S & P 500 Daily Returns Volatility
发现条件离散模型的拟极大似然估计易受市场崩盘等异常值影响，提出两种稳健估计方法，并应用于标普500日收益率波动建模，揭示了正常市场活动下的波动动态。
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.