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CAPM and Option Pricing With Elliptically Contoured Distributions

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  • Mahmoud Hamada
  • Emiliano A. Valdez

Abstract

This article offers an alternative proof of the capital asset pricing model (CAPM) when asset returns follow a multivariate elliptical distribution. Empirical studies continue to demonstrate the inappropriateness of the normality assumption for modeling asset returns. The class of elliptically contoured distributions, which includes the more familiar Normal distribution, provides flexibility in modeling the thickness of tails associated with the possibility that asset returns take extreme values with nonnegligible probabilities. As summarized in this article, this class preserves several properties of the Normal distribution. Within this framework, we prove a new version of Stein's lemma for this class of distributions and use this result to derive the CAPM when returns are elliptical. Furthermore, using the probability distortion function approach based on the dual utility theory of choice under uncertainty, we also derive an explicit form solution to call option prices when the underlying is log-elliptically distributed. The Black-Scholes call option price is a special case of this general result when the underlying is log-normally distributed. Copyright (c) The Journal of Risk and Insurance, 2008.

Suggested Citation

  • Mahmoud Hamada & Emiliano A. Valdez, 2008. "CAPM and Option Pricing With Elliptically Contoured Distributions," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(2), pages 387-409.
  • Handle: RePEc:bla:jrinsu:v:75:y:2008:i:2:p:387-409
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    References listed on IDEAS

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    Cited by:

    1. Fan, Jianqing & Han, Fang & Liu, Han & Vickers, Byron, 2016. "Robust inference of risks of large portfolios," Journal of Econometrics, Elsevier, vol. 194(2), pages 298-308.
    2. Kume, Alfred & Hashorva, Enkelejd, 2012. "Calculation of Bayes premium for conditional elliptical risks," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 632-635.
    3. Polonik, Wolfgang & Yao, Qiwei, 2008. "Testing for multivariate volatility functions using minimum volume sets and inverse regression," Journal of Econometrics, Elsevier, vol. 147(1), pages 151-162, November.
    4. Landsman, Zinoviy & Neslehová, Johanna, 2008. "Stein's Lemma for elliptical random vectors," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 912-927, May.
    5. Lundtofte, Frederik & Wilhelmsson, Anders, 2011. "Idiosyncratic Risk and Higher-Order Cumulants," Working Papers 2011:33, Lund University, Department of Economics.
    6. Nikolay Gospodinov & Raymond Kan & Cesare Robotti, 2012. "Analytical solution for the constrained Hansen-Jagannathan distance under multivariate ellipticity," FRB Atlanta Working Paper 2012-18, Federal Reserve Bank of Atlanta.
    7. Francisco Blasques & Andre Lucas & Erkki Silde, 2013. "Stationarity and Ergodicity Regions for Score Driven Dynamic Correlation Models," Tinbergen Institute Discussion Papers 13-097/IV/DSF59, Tinbergen Institute.
    8. Nikolay Gospodinov & Raymond Kan & Cesare Robotti, 2010. "On the Hansen-Jagannathan distance with a no-arbitrage constraint," FRB Atlanta Working Paper 2010-04, Federal Reserve Bank of Atlanta.
    9. SADEFO KAMDEM Jules, 2004. "VaR and ES for Linear Portfolios with mixture of Generalized Laplace Distributed Risk Factors," Risk and Insurance 0406001, University Library of Munich, Germany.
    10. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2016. "On the properties of the constrained Hansen–Jagannathan distance," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 121-150.
    11. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.

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