Beta Matrix and Common Factors in Stock Returns
研究了多因子模型中贝塔矩阵秩的估计方法，发现一种受限贝叶斯信息准则估计器对大量资产数据可靠，并用于分析美国股票收益，表明许多模型的贝塔矩阵不满秩，导致风险溢价无法识别。
We consider the estimation methods for the rank of a beta matrix corresponding to a multifactor model and study which method would be appropriate for data with a large number of assets. Our simulation results indicate that a restricted version of Cragg and Donald’s (1997) Bayesian information criterion estimator is quite reliable for such data. We use this estimator to analyze some selected asset pricing models with U.S. stock returns. Our results indicate that the beta matrix from many models fails to have full column rank, suggesting that risk premiums in these models are underidentified.