Factor multivariate stochastic volatility models of high dimension
基于因子分解解决高维波动率估计的维数灾难，提出因子多元随机波动率模型，给出两阶段估计法并推导渐近性质，用模拟和资产组合应用验证预测效果。
Building upon factor decomposition to overcome the curse of dimensionality inherent in multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework. We propose a two-stage estimation procedure for the fMSV model: in the first stage, estimators of the factor model are obtained, and in the second stage, the MSV component is estimated using the estimated common factor variables. We derive the asymptotic properties of the estimators, taking into account the estimation of the factor variables. The prediction performances are illustrated by finite-sample simulation experiments and applications to portfolio allocation.