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Moment-Implied Densities: Properties and Applications

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  • Eric Ghysels
  • Fangfang Wang

Abstract

Suppose one uses a parametric density function based on the first four (conditional) moments to model risk. There are quite a few densities to choose from and depending on which is selected, one implicitly assumes very different tail behavior and very different feasible skewness/kurtosis combinations. Surprisingly, there is no systematic analysis of the tradeoff one faces. It is the purpose of the article to address this. We focus on the tail behavior and the range of skewness and kurtosis as these are key for common applications such as risk management.

Suggested Citation

  • Eric Ghysels & Fangfang Wang, 2014. "Moment-Implied Densities: Properties and Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 88-111, January.
    • Handle: RePEc:taf:jnlbes:v:32:y:2014:i:1:p:88-111
    DOI: 10.1080/07350015.2013.847842
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    Citations

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    Cited by:

    1. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    2. Leonidas S. Rompolis & Elias Tzavalis, 2017. "Retrieving risk neutral moments and expected quadratic variation from option prices," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 955-1002, May.
    3. Søren Asmussen, 2022. "On the role of skewness and kurtosis in tempered stable (CGMY) Lévy models in finance," Finance and Stochastics, Springer, vol. 26(3), pages 383-416, July.
    4. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    5. Papantonis Ioannis & Rompolis Leonidas S. & Tzavalis Elias & Agapitos Orestis, 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.
    6. Greg Orosi, 2017. "Information content of right option tails: Evidence from S&P 500 index options," Journal of Asset Management, Palgrave Macmillan, vol. 18(7), pages 516-526, December.
    7. Hong Li & Yang Lu, 2018. "A Bayesian non-parametric model for small population mortality," Post-Print hal-02419000, HAL.
    8. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    9. Liyuan Jiang & Shuang Zhou & Keren Li & Fangfang Wang & Jie Yang, 2018. "A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps," Papers 1808.05289, arXiv.org, revised Feb 2019.
    10. McGee, Richard J. & McGroarty, Frank, 2017. "The risk premium that never was: A fair value explanation of the volatility spread," European Journal of Operational Research, Elsevier, vol. 262(1), pages 370-380.

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