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Asymmetry in Stock Comovements: An Entropy Approach

Published online by Cambridge University Press:  06 August 2018

Lei Jiang ,
Ke Wu  and
Guofu Zhou
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Abstract

We provide an entropy approach for measuring the asymmetric comovement between the return on a single asset and the market return. This approach yields a model-free test for stock return asymmetry, generalizing the correlation-based test proposed by Hong, Tu, and Zhou (2007). Based on this test, we find that asymmetry is much more pervasive than previously thought. Moreover, our approach also provides an entropy-based measure of downside asymmetric comovement. In the cross section of stock returns, we find an asymmetry premium: Higher downside asymmetric comovement with the market indicates higher expected returns.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 53 , Issue 4 , August 2018 , pp. 1479 - 1507
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2018 

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Footnotes

1

We are grateful to Vikas Agarwal, Doron Avramov, Jonathan Brogaard, Jeffrey Busse, Hui Chen, Tarun Chordia, Lauren Cohen, Philip Dybvig, Andrey Ermolov, Ping He, Zhiguo He, David Jacho-Chavez, Ying Jiang, Raymond Kan, Omesh Kini, Junghoon Lee, Tingjun Liu, Esfandiar Maasoumi, Michael Powers, Jun Tu, Aurelio Vasquez, Baozhong Yang, and seminar participants at Cornerstone Research, Case Western Reserve University, Emory University, Georgia State University, Harbin Institute of Technology, ITAM, Peking University, Washington University in St. Louis, the 2014 and 2015 Tsinghua Finance Workshops, the 2015 China Finance Review International Conference, the 2015 Five-Star Workshop in Finance, the 2015 China International Conference in Finance, and the 2017 Midwest Finance Association Annual Meeting for numerous helpful comments and especially to Jennifer Conrad (the editor) and Riccardo Colacito (the referee) for their many insightful and detailed comments that have substantially improved the article. Jiang gratefully acknowledges financial support from the AXA Research Fund, the Initiative Research Program supported by Tsinghua University (20151080398), the National Natural Science Foundation of China (71572091), and the Tsinghua National Laboratory for Information Science and Technology. The authors thank Jing Ding and Jinyu Liu for excellent research assistance.

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