Abstract
This paper 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 risk factors as well as to the unobservable factor structure, which is group specific. The proposed method allows for correlations between observable and unobservable factors and also allows for cross-sectional and serial dependence and heteroskedasticities 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 6,000 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 complexity of the global financial markets.
Highlights
The U.S subprime crisis of 2007, which was triggered by the collapse of the U.S housing market, subsequently spilled over to the entire U.S and the European financial markets, resulting in bankruptcies, forced mergers, and bailouts for many large firms.2 These financial shocks further spread to the global financial markets, and led to massive declines in worldwide asset values.identifying the sources of the co-movement of international stock returns is one of the most important issues in finance
Cross-sectional and time-series variations in stock returns? (3) Do the co-movements within a market, industry, country, or region constitute the only sources of cross-sectional and time series variations in the stock markets? (4) What are the different characteristics of the markets that can be observed during the recent financial crisis? To address these questions, we introduce a new procedure for clustering a large number of financial time series based on high-dimensional panel data with grouped factor structures
This paper proposed a novel and a general approach that simultaneously implements the following features: (1) detecting a set of relevant observable factors, (2) extracting unobservable common and group-specific factors, (3) automatically determining the number of groups, and (4) clustering a huge number of assets
Summary
The U.S subprime crisis of 2007, which was triggered by the collapse of the U.S housing market, subsequently spilled over to the entire U.S and the European financial markets, resulting in bankruptcies, forced mergers, and bailouts for many large firms. These financial shocks further spread to the global financial markets, and led to massive declines in worldwide asset values. We introduce a new procedure for clustering a large number of financial time series based on high-dimensional panel data with grouped factor structures. Our empirical analysis indicates that the country-specific factor is one of the sources of co-movement in the cross-sectional and time-series variations of stock returns. This result is consistent with that of Fama and French (2012), who reported that global models fare poorly, while local versions of their three- and four-factor models for each of four regions – North America, Europe, Japan, and Asia Pacific – capture local average returns rather well.
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