Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data

Abstract

Jianqing Fana, Alex Furgerb & Dacheng Xiuca Department of Operations Research & Financial Engineering and BendheimCenter for FinancePrinceton University, Princeton, NJ 08544, and School of Data Science, Fudan University, Shanghai, Chinab Department of Operations Research & Financial Engineering Princeton University, 26 Prospect Avenue, Princeton, NJ 08544c Booth School of Business University of Chicago, 5807 S. Woodlawn Avenue, Chicago, IL 60637Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/jbes.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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 153 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.