Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures

Abstract

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.

Keywords:

• Clustering
• Factor structure
• Heterogeneous panel
• Lasso
• Serial and cross-sectional error correlations

Acknowledgment

The authors thank the Co-editor, the associate editor and two anonymous reviewers for constructive and helpful comments that improved the quality of the article considerably. The authors also thank comments from participants at the 28th Australasian Finance and Banking Conference.

Funding

Ando’s research is supported by Research Grant from Melbourne Business School, and Bai’s research is supported by the National Science Foundation (SES1357198)

Notes

¹ The firms affected include AIG, Bear Stearns, BNP Paribas, Fannie Mae, Freddie Mac, Lehman Brothers, and Merrill Lynch.