Bayesian Analysis of Linear Factor Models with Latent Factors, Multivariate Stochastic Volatility, and APT Pricing Restrictions
分析了一类新的线性因子模型，其中因子是潜在的，超额收益的协方差矩阵遵循多元随机波动过程。利用贝叶斯MCMC方法，评估了套利定价理论的横截面限制，比较了不同的随机波动率设定，并检验了因子数量。研究发现，对于10个规模十分位组合，三个潜在因子加上多元随机波动率最能解释超额收益，且数据强烈支持受APT限制的模型。
Abstract We analyze a new class of linear factor models in which the factors are latent and the covariance matrix of excess returns follows a multivariate stochastic volatility process. We evaluate cross-sectional restrictions suggested by the arbitrage pricing theory (APT), compare competing stochastic volatility specifications for the covariance matrix, and test for the number of factors. We also examine whether return predictability can be attributed to time-varying factor risk premia. Analysis of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) methods. We find that three latent factors with multivariate stochastic volatility best explain excess returns for a sample of 10 size decile portfolios. The data strongly favor models constrained by APT pricing restrictions over otherwise identical unconstrained models.