Fitting Vast Dimensional Time-Varying Covariance Models
提出一种快速估计高维时变协方差模型的新方法，解决了现有方法在处理数百甚至数千资产时存在的未诊断的 incidental 参数问题，支持横截面维度大于时间序列维度的情况，并可用于假设检验和样本外对冲表现比较。
Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared.