Improving estimation of portfolio risk using new statistical factors

Xialu Liu, John Guerard, Rong Chen and Ruey Tsay ()
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Xialu Liu: San Diego State University
Rong Chen: Rutgers University
Ruey Tsay: University of Chicago

Annals of Operations Research, 2025, vol. 346, issue 1, No 16, 245-261

Abstract: Abstract Searching for new effective risk factors on stock returns is an important research topic in asset pricing. Factor modeling is an active research topic in statistics and econometrics, with many new advances. However, these new methods have not been fully utilized in asset pricing application. In this paper, we adopt the factor models, especially matrix factor models in various forms, to construct new statistical factors that explain the variation of stock returns. Furthermore, we evaluate the contribution of these statistical factors beyond the existing factors available in the asset pricing literature. To demonstrate the power of the new factors, U.S. monthly stock data are analyzed, and the partial F test and double selection LASSO method are conducted. The results show that the new statistical factors bring additional information and add explanatory power in asset pricing. Our method opens a new direction for portfolio managers to seek additional risk factors to improve the estimation of portfolio returns.

Keywords: Asset pricing; Fama–French portfolios; Matrix factor models; Principal component analysis; Portfolio selection; Statistical factors (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06307-8

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