Factor Stochastic Volatility in Mean Models: A GMM Approach
提出一个半参数框架，用潜在随机波动因子捕捉资产收益的联合条件方差矩阵的共同性，并通过条件矩约束实现参数识别和GMM估计，适用于研究风险溢价和波动率动态。
This paper provides a semiparametric framework for modeling multivariate conditional heteroskedasticity. We put forward latent stochastic volatility (SV) factors as capturing the commonality in the joint conditional variance matrix of asset returns. This approach is in line with common features as studied by Engle and Kozicki (1993), and it allows us to focus on identication of factors and factor loadings through first- and second-order conditional moments only. We assume that the time-varying part of risk premiums is based on constant prices of factor risks, and we consider a factor SV in mean model. Additional specification of both expectations and volatility of future volatility of factors provides conditional moment restrictions, through which the parameters of the model are all identied. These conditional moment restrictions pave the way for instrumental variables estimation and GMM inference.