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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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    1. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2001. "Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices," Journal of Financial Economics, Elsevier, vol. 61(3), pages 345-381, September.
    2. Bevan Blair & Ser-Huang Poon & Stephen Taylor, 2002. "Asymmetric and crash effects in stock volatility for the S&P 100 index and its constituents," Applied Financial Economics, Taylor & Francis Journals, vol. 12(5), pages 319-329.
    3. Dennis, Patrick & Mayhew, Stewart & Stivers, Chris, 2006. "Stock Returns, Implied Volatility Innovations, and the Asymmetric Volatility Phenomenon," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 41(02), pages 381-406, June.
    4. Gurdip Bakshi & Nikunj Kapadia & Dilip Madan, 2003. "Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options," Review of Financial Studies, Society for Financial Studies, vol. 16(1), pages 101-143.
    5. David Easley & Maureen O'Hara & P.S. Srinivas, 1998. "Option Volume and Stock Prices: Evidence on Where Informed Traders Trade," Journal of Finance, American Finance Association, vol. 53(2), pages 431-465, April.
    6. Peter Christoffersen & Kris Jacobs & Gregory Vainberg, 2007. "Forward-Looking Betas," CREATES Research Papers 2007-39, Department of Economics and Business Economics, Aarhus University.
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    Cited by:

    1. repec:pal:assmgt:v:18:y:2017:i:4:d:10.1057_s41260-016-0032-5 is not listed on IDEAS
    2. 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.

    More about this item

    Keywords

    risk-neutral distribution; skewness; stock options; ARCH models;

    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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