Robust Measurement of Beta Risk
介绍多种稳健统计方法用于估计贝塔风险，实证表明相比普通最小二乘法，稳健方法在股票收益分布非正态时能提高估计效率。
Many empirical studies find that the distribution of stock returns departs from normality. In such cases, it is desirable to employ a statistical estimation procedure that may be more efficient than ordinary least squares. This paper describes various robust methods, which have attracted increasing attention in the statistical literature, in the context of estimating beta risk. The empirical analysis documents the potential efficiency gains from using robust methods as an alternative to ordinary least squares, based on both simulated and actual returns data.