Sparse Bayesian time-varying covariance estimation in many dimensions
提出一种稀疏贝叶斯方法，通过潜在时变随机因子估计高维时间序列的动态协方差，并用全局-局部收缩先验剔除冗余因子。模拟和标普500日收益率数据表明，该方法在相关性估计、最小方差组合和预测精度上优于常见基准。
We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global-local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500 members. Our approach yields precise correlation estimates, strong implied minimum variance portfolio performance and superior forecasting accuracy in terms of log predictive scores when compared to typical benchmarks.