Bayesian Analysis of Stochastic Betas
提出一个均值回归随机过程来估计市场贝塔，模拟和实证表明其比GARCH等模型更精确，能改善对冲效果并解释规模、账面市值比等异象。
Abstract We propose a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates than GARCH betas, betas conditioned on aggregate or firm-level variables, and rolling regression betas, even when the true betas are generated based on these competing specifications. Our model significantly improves out-of-sample hedging effectiveness. In asset pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models and helps resolve asset pricing anomalies such as the size, book-to-market, and idiosyncratic volatility effects in the cross section of stock returns.