An Empirical Bayes Estimate of Market Risk
提出一种经验贝叶斯方法，在纠正市场模型参数估计中的系统性误差后，利用证券特征参数的先验分布和回归结果，更有效地估计单个证券的alpha、beta和sigma平方。
Starting with a market model of security returns, we describe how the parameters of a distribution for security characteristics can be estimated in a manner correcting for a subtle but significant source of error. When this error is removed, strong negative correlations between “alpha” and “beta” and between “alpha” and “sigma squared,” and a strong positive correlation between “beta” and “sigma squared” are observed. With this feature in the prior distribution, and with the results of a regression for a particular security, we develop an empirical Bayes estimate of the security's three parameters (alpha, beta and sigma squared) which makes use of more information than other estimates described in the literature.