期刊
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
卷 34, 期 4, 页码 489-503出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/07350015.2015.1052458
关键词
Big data; Concentration inequality; GICS; High-frequency factor model; Location-based thresholding; Low rank plus sparse; Positive-definite; Precision matrix; SDPR sector ETF's
资金
- NSF [DMS-1206464, DMS-1406266]
- FMC Faculty Scholar Fund at the University of Chicago Booth School of Business
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1206464] Funding Source: National Science Foundation
We document a striking block-diagonal pattern in the factor model residual covariances of the S&P 500 Equity Index constituents, after sorting the assets by their assigned Global Industry Classification Standard (GICS) codes. Cognizant of this structure, we propose combining a location-based thresholding approach based on sector inclusion with the Fama-French and SDPR sector Exchange Traded Funds (ETF's). We investigate the performance of our estimators in an out-of-sample portfolio allocation study. We find that our simple and positive-definite covariance matrix estimator yields strong empirical results under a variety of factor models and thresholding schemes. Conversely, we find that the Fama-French factor model is only suitable for covariance estimation when used in conjunction with our proposed thresholding technique. Theoretically, we provide justification for the empirical results by jointly analyzing the in-fill and diverging dimension asymptotics.
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