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Maximum Entropy Estimates for Risk-Neutral Probability Measures with Non-Strictly-Convex Data

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  • Christopher Bose

    (University of Victoria)

  • Rua Murray

    (University of Canterbury)

Abstract

This article investigates use of the Principle of Maximum Entropy for approximation of the risk-neutral probability density on the price of a financial asset as inferred from market prices on associated options. The usual strict convexity assumption on the market-price to strike-price function is relaxed, provided one is willing to accept a partially supported risk-neutral density. This provides a natural and useful extension of the standard theory. We present a rigorous analysis of the related optimization problem via convex duality and constraint qualification on both bounded and unbounded price domains. The relevance of this work for applications is in explaining precisely the consequences of any gap between convexity and strict convexity in the price function. The computational feasibility of the method and analytic consequences arising from non-strictly-convex price functions are illustrated with a numerical example.

Suggested Citation

  • Christopher Bose & Rua Murray, 2014. "Maximum Entropy Estimates for Risk-Neutral Probability Measures with Non-Strictly-Convex Data," Journal of Optimization Theory and Applications, Springer, vol. 161(1), pages 285-307, April.
    • Handle: RePEc:spr:joptap:v:161:y:2014:i:1:d:10.1007_s10957-013-0349-x
    DOI: 10.1007/s10957-013-0349-x
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    References listed on IDEAS

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    1. Breeden, Douglas T & Litzenberger, Robert H, 1978. "Prices of State-contingent Claims Implicit in Option Prices," The Journal of Business, University of Chicago Press, vol. 51(4), pages 621-651, October.
    2. Buchen, Peter W. & Kelly, Michael, 1996. "The Maximum Entropy Distribution of an Asset Inferred from Option Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(1), pages 143-159, March.
    3. Cassio Neri & Lorenz Schneider, 2012. "Maximum entropy distributions inferred from option portfolios on an asset," Finance and Stochastics, Springer, vol. 16(2), pages 293-318, April.
    4. Marco Avellaneda & Craig Friedman & Richard Holmes & Dominick Samperi, 1997. "Calibrating volatility surfaces via relative-entropy minimization," Applied Mathematical Finance, Taylor & Francis Journals, vol. 4(1), pages 37-64.
    5. Weiyu Guo, 2001. "Maximum Entropy in Option Pricing: A Convex‐Spline Smoothing Method," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(9), pages 819-832, September.
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    Cited by:

    1. José L. Vilar-Zanón & Barbara Rogo, 2024. "Pricing and Hedging Contingent Claims by Entropy Segmentation and Fenchel Duality," Methodology and Computing in Applied Probability, Springer, vol. 26(4), pages 1-20, December.
    2. José L. Vilar-Zanón & Olivia Peraita-Ezcurra, 2019. "A linear goal programming method to recover risk neutral probabilities from options prices by maximum entropy," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 259-276, June.
    3. Salazar Celis, Oliver & Liang, Lingzhi & Lemmens, Damiaan & Tempère, Jacques & Cuyt, Annie, 2015. "Determining and benchmarking risk neutral distributions implied from option prices," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 372-387.

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