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Cross-sectional analysis of risk-neutral skewness

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  • Taylor, Stephen J.
  • Yadav, Pradeep K.
  • Zhang, Yuanyuan

Abstract

We investigate the association of various firm-specific and market-wide factors with the riskneutral skewness (RNS) implied by the prices of individual stock options. Our analysis covers 149 U.S. firms over a four-year period. Our choice of firms is based on adequate liquidity and trading interest across different strike prices in the options market, ensuring economically meaningful RNS estimates. We also incorporate significant methodological enhancements. Consistent with earlier results, we find that the RNS of individual firms varies significantly and negatively with firm size, firm systematic risk, and market volatility; and significantly and positively with the RNS of the market index; and most of the variation in individual RNS is explained by firm-specific rather than market-wide factors. We also document several interesting new results that are clearly unambiguous and significantly stronger than in earlier work, or opposite to earlier evidence, or for variables that have been examined for the first time. First, we find that market sentiment has a negative and significant effect on RNS. Second, we find that higher a firm's own volatility, the more negative the RNS, a relationship that is in the same direction as for overall market volatility. Third, greater market liquidity is associated with more negative RNS, but the liquidity that is relevant for RNS is that of the options market, rather than that in the underlying stock. Surprisingly, volatility asymmetry is not relevant for RNS. Finally, the leverage ratio is not negatively but positively and strongly related with RNS.

Suggested Citation

  • Taylor, Stephen J. & Yadav, Pradeep K. & Zhang, Yuanyuan, 2009. "Cross-sectional analysis of risk-neutral skewness," CFR Working Papers 09-11, University of Cologne, Centre for Financial Research (CFR).
    • Handle: RePEc:zbw:cfrwps:0911
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    References listed on IDEAS

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    Cited by:

    1. Borochin, Paul & Chang, Hao & Wu, Yangru, 2020. "The information content of the term structure of risk-neutral skewness," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 247-274.
    2. Liu, Qing & Wang, Shouyang & Sui, Cong, 2023. "Risk appetite and option prices: Evidence from the Chinese SSE50 options market," International Review of Financial Analysis, Elsevier, vol. 86(C).
    3. Abderrahmen Aloulou & Siwar Ellouze, 2017. "Does fundamental value run asset price formation process? Evidence from option price information content," Journal of Asset Management, Palgrave Macmillan, vol. 18(4), pages 255-268, July.
    4. Borochin, Paul & Zhao, Yanhui, 2019. "Belief heterogeneity in the option markets and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 107(C), pages 1-1.
    5. Nessim Souissi, 2017. "The Implied Risk Neutral Density Dynamics: Evidence from the S&P TSX 60 Index," Journal of Applied Mathematics, Hindawi, vol. 2017, pages 1-10, June.
    6. Przemysław S. Stilger & Alexandros Kostakis & Ser-Huang Poon, 2017. "What Does Risk-Neutral Skewness Tell Us About Future Stock Returns?," Management Science, INFORMS, vol. 63(6), pages 1814-1834, June.
    7. Lim, Kian Guan & Chen, Ying & Yap, Nelson K.L., 2019. "Intraday information from S&P 500 Index futures options," Journal of Financial Markets, Elsevier, vol. 42(C), pages 29-55.
    8. Birru, Justin & Figlewski, Stephen, 2012. "Anatomy of a meltdown: The risk neutral density for the S&P 500 in the fall of 2008," Journal of Financial Markets, Elsevier, vol. 15(2), pages 151-180.

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    More about this item

    Keywords

    risk-neutral distribution; skewness; stock options; ARCH models;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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