论文标题
使用全球和国家因素模型的大波动矩阵分析
Large Volatility Matrix Analysis Using Global and National Factor Models
论文作者
论文摘要
基于潜在因子模型，已经开发了几个大波动率矩阵推理程序。他们经常认为有一些常见因素可以解释波动率动态。但是，一些研究表明存在局部因素。特别是，在分析全球股票市场时，我们经常观察到国家特定的因素可以解释其自己国家的波动动态。为此，我们提出了基于多层次因子模型的双主要正交补体阈值（双重诗）方法，还建立了其渐近性特性。此外，我们证明了当存在局部因素结构时使用常规主正交组件阈值（诗人）的缺点。我们还使用双重诗人来描述维数的祝福，以供局部协方差矩阵估计。最后，我们在使用来自20个金融市场的国际股票的样本外投资组合分配研究中调查了双重估计器的性能。
Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few of common factors, which can account for volatility dynamics. However, several studies have demonstrated the presence of local factors. In particular, when analyzing the global stock market, we often observe that nation-specific factors explain their own country's volatility dynamics. To account for this, we propose the Double Principal Orthogonal complEment Thresholding (Double-POET) method, based on multi-level factor models, and also establish its asymptotic properties. Furthermore, we demonstrate the drawback of using the regular principal orthogonal component thresholding (POET) when the local factor structure exists. We also describe the blessing of dimensionality using Double-POET for local covariance matrix estimation. Finally, we investigate the performance of the Double-POET estimator in an out-of-sample portfolio allocation study using international stocks from 20 financial markets.
