Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures
提出一种基于高维面板数据的新聚类方法，用于对大量金融时间序列进行分组，考虑了可观测和不可观测因子，并应用于6000多只国际股票，量化了美国次贷危机的全球溢出效应。
This article introduces a new procedure for clustering a large number of financial time series based on high-dimensional panel data with grouped factor structures. The proposed method attempts to capture the level of similarity of each of the time series based on sensitivity to observable factors as well as to the unobservable factor structure. The proposed method allows for correlations between observable and unobservable factors and also allows for cross-sectional and serial dependence and heteroscedasticities in the error structure, which are common in financial markets. In addition, theoretical properties are established for the procedure. We apply the method to analyze the returns for over 6000 international stocks from over 100 financial markets. The empirical analysis quantifies the extent to which the U.S. subprime crisis spilled over to the global financial markets. Furthermore, we find that nominal classifications based on either listed market, industry, country or region are insufficient to characterize the heterogeneity of the global financial markets. Supplementary materials for this article are available online.