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.
Highlights
Based on the classical Capital Asset Pricing Model (CAPM) developed by Sharpe [1], Lintner [2], and Black [3], it is the comovements of individual stocks with the whole markets that determine stocks’ expected returns
The portfolio analysis indicates that the stock returns decrease slightly with leverage, we find that such negative relations are insignificant, which is in line with Fama and French [14]
The cross-sectional evidence above has verified that the tail dependence risk significantly affects the stock returns of Shanghai A shares, and this effect will not disappear even after adding other factors in the regressions
Summary
Based on the classical Capital Asset Pricing Model (CAPM) developed by Sharpe [1], Lintner [2], and Black [3], it is the comovements of individual stocks with the whole markets that determine stocks’ expected returns. We employ tail dependence, which is a flexible measure of extreme comovements and can be calculated using the sound Copula theory, to capture the risk that individual stocks crash together with the whole market and further explore the effect of such tail dependence risk on stock returns.
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