Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model
开发了一个高维因子随机波动率模型，同时捕捉杠杆效应、收益非对称性和厚尾特征，并提出了高效的MCMC估计算法。实证表明收益非对称性是系统性现象，该模型在VaR评估中优于其他因子模型。
We develop a factor stochastic volatility model that incorporates leverage effects, return asymmetry, and heavy tails across all systematic and idiosyncratic model components. Our model leads to a flexible high-dimensional dependence structure that allows for time-varying correlations, tail dependence, and volatility response to both systematic and idiosyncratic return shocks. We develop an efficient Markov chain Monte Carlo algorithm for posterior estimation based on the particle Gibbs, ancestor sampling, particle efficient importance sampling methods, and interweaving strategy. To obtain parsimonious specifications in practice, we build computationally efficient model selection directly into our estimation algorithm. We validate the performance of our proposed estimation method via simulation studies with different model specifications. An empirical study for a sample of U.S. stocks shows that return asymmetry is a systematic phenomenon and our model outperforms other factor models for value-at-risk evaluation.