Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China. (3rd June 2021)

• Record Type: Journal Article
• Authors: Sun, Xi; Chen, Yihao; Chen, Yulin; Lou, Zhusheng; Tao, Lingfeng; Zhang, Yihao
• Other Names: Ma Junhai Academic Editor.
• Is Part Of: Discrete dynamics in nature and society. Volume 2021(2021)
• Publication Date: 2021-06-03
• Subjects: System analysis -- Periodicals; Dynamics -- Periodicals; Chaotic behavior in systems -- Periodicals; Differentiable dynamical systems -- Periodicals; 003.05
• DOI: 10.1155/2021/6670378
• Languages: English
• ISSNs: 1026-0226

Abstract

Abstract : Factor models provide a cornerstone for understanding financial asset pricing; however, research on China's stock market risk premia is still limited. Motivated by this, this paper proposes a four-factor model for China's stock market that includes a market factor, a size factor, a value factor, and a liquidity factor. We compare our four-factor model with a set of prominent factor models based on newly developed likelihood-ratio tests and Bayesian methods. Along with the comparison, we also find supporting evidence for the alternative t-distribution assumption for empirical asset pricing studies. Our results show the following: (1) distributional tests suggest that the returns of factors and stock return anomalies are fat-tailed and therefore are better captured by t-distributions than by normality; (2) under t-distribution assumptions, our four-factor model outperforms a set of prominent factor models in terms of explaining the factors in each other, pricing a comprehensive list of stock return anomalies, and Bayesian marginal likelihoods; (3) model comparison results vary across normality and t-distribution assumptions, which suggests that distributional assumptions matter for asset pricing studies. This paper contributes to the literature by proposing an effective asset pricing factor model and providing factor model comparison tests under non-normal distributional assumptions in the context of China.