Improving Estimation of Portfolio Risk Using New Statistical Factors

TL;DR

This paper introduces new statistical factors derived from advanced matrix factor models to enhance the estimation of portfolio risk, demonstrating improved explanatory power in asset pricing using U.S. stock data.

Contribution

It applies recent advances in matrix factor models to construct novel risk factors and evaluates their added value in explaining stock return variations.

Findings

New statistical factors improve asset pricing models.

Enhanced explanatory power over existing factors.

Method offers a new direction for portfolio risk estimation.

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.