Work

Estimating Asset Pricing Models in the Presence of Cross-Sectionally Correlated Pricing Errors

Year: 2024

Type: article

Abstract: In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited.

Source: Mathematics

DOI: 10.3390/math12213442

Authors: Hyuksoo Kim, Saejoon Kim

Institution: Sogang University

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea