摘要：Outlier is the observation that is not consistent with the rest of observations. It exists not only in stock prices but also in the economic variables. In multifactor asset pricing model, the ordinary least square method (OLS) is commonly used to estimate coefficients. The existence of outliers can lead to inadequate results under the OLS framework. Huber’s robust method (HRM) can be used to avoid the bad impacts of outliers and the abnormal problems. Appling both methods to Shanghai stock market, the outlier observations are analyzed to examine its influence on the results and parameters estimation. The result of this study found that HRM outperforms OLS.
其他摘要：Outlier is the observation that is not consistent with the rest of observations. It exists not only in stock prices but also in the economic variables. In multifactor asset pricing model, the ordinary least square method (OLS) is commonly used to estimate coefficients. The existence of outliers can lead to inadequate results under the OLS framework. Huber’s robust method (HRM) can be used to avoid the bad impacts of outliers and the abnormal problems. Appling both methods to Shanghai stock market, the outlier observations are analyzed to examine its influence on the results and parameters estimation. The result of this study found that HRM outperforms OLS.
