The Risk of Individual Stocks' Tail Dependence with the Market and Its Effect on Stock Returns

Guobin Fan, Eric Girardin (), Wong K. Wong and Yong Zeng
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Guobin Fan: Southwestern University of Finance and Economics [Chengdu, China]
Wong K. Wong: Cardiff University
Yong Zeng: Wuhan Institute of Physics and Mathematics - The Chinese Academy of Sciences, UCAS - University of Chinese Academy of Sciences [Beijing] - CAS - Chinese Academy of Sciences [Beijing]

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Abstract: Traditional beta is only a linear measure of overall market risk and places equal emphasis on upside and downside risks, but actually the latter is always much stronger probably due to the trading mechanism like short-sale constraints. Therefore, this paper employs the nonlinear measure, tail dependence, to measure the extreme downside risks that individual stocks crash together with the whole market and investigates whether such tail dependence risks will affect stock returns. Our empirical evidence based on Shanghai A shares confirms that most stocks display nonnegligible tail dependence with the whole market, and, more importantly, such tail dependence risks can indeed provide additional information beyond beta and other factors for asset pricing. In cross-sectional regression, it is proved that this tail dependence does help to explain monthly returns on Shanghai A shares, whereas the time-series regression further indicates that mimicking portfolio returns for tail dependence can capture strong common variation of Shanghai A stock returns.

Keywords: Economie; quantitative (search for similar items in EconPapers)
Date: 2015-11
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01457389v1
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Published in Discrete Dynamics in Nature and Society, 2015, Online, 2015, pp.1--17 / 980768. ⟨10.1155/2015/980768⟩

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Journal Article: The Risk of Individual Stocks’ Tail Dependence with the Market and Its Effect on Stock Returns (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01457389

DOI: 10.1155/2015/980768

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