Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China
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...
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2021-01-01
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Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2021/6670378 |
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doaj-bd3ad38f2b744dcb9f013614440ccf592021-06-14T00:16:54ZengHindawi LimitedDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/6670378Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from ChinaXi Sun0Yihao Chen1Yulin Chen2Zhusheng Lou3Lingfeng Tao4Yihao Zhang5Hanqing Advanced Institute of Economics and FinanceHanqing Advanced Institute of Economics and FinanceHanqing Advanced Institute of Economics and FinanceHanqing Advanced Institute of Economics and FinanceHanqing Advanced Institute of Economics and FinanceBusiness SchoolFactor 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.http://dx.doi.org/10.1155/2021/6670378 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xi Sun Yihao Chen Yulin Chen Zhusheng Lou Lingfeng Tao Yihao Zhang |
spellingShingle |
Xi Sun Yihao Chen Yulin Chen Zhusheng Lou Lingfeng Tao Yihao Zhang Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China Discrete Dynamics in Nature and Society |
author_facet |
Xi Sun Yihao Chen Yulin Chen Zhusheng Lou Lingfeng Tao Yihao Zhang |
author_sort |
Xi Sun |
title |
Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China |
title_short |
Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China |
title_full |
Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China |
title_fullStr |
Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China |
title_full_unstemmed |
Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China |
title_sort |
comparing asset pricing factor models under multivariate t-distribution: evidence from china |
publisher |
Hindawi Limited |
series |
Discrete Dynamics in Nature and Society |
issn |
1607-887X |
publishDate |
2021-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2021/6670378 |
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