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Robust option pricing: Hannan and Blackwell meet Black and Scholes

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  • DeMarzo, Peter M.
  • Kremer, Ilan
  • Mansour, Yishay

Abstract

We apply methods developed in the literature initiated by Hannan and Blackwell on robust optimization, approachability and calibration, to price financial securities. Rather than focus on asymptotic performance, we show how gradient strategies developed to minimize asymptotic regret imply financial trading strategies that yield arbitrage-based bounds for option prices. These bounds are new and robust in that they do not depend on the continuity of the stock price process, complete markets, or an assumed pricing kernel. They depend only on the realized quadratic variation of the price process, which can be measured and, importantly, hedged in financial markets using existing securities. Our results also apply directly to a new class of options called timer options. Finally, we argue that the Hannan–Blackwell strategy is path dependent and therefore suboptimal with a finite horizon. We improve it by solving for the optimal path-independent strategy, and compare the resulting bounds with Black–Scholes.

Suggested Citation

  • DeMarzo, Peter M. & Kremer, Ilan & Mansour, Yishay, 2016. "Robust option pricing: Hannan and Blackwell meet Black and Scholes," Journal of Economic Theory, Elsevier, vol. 163(C), pages 410-434.
  • Handle: RePEc:eee:jetheo:v:163:y:2016:i:c:p:410-434
    DOI: 10.1016/j.jet.2016.01.009
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    Cited by:

    1. Ye Du & Shan Xue & Yanchu Liu, 2019. "Robust upper bounds for American put options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 3-14, January.
    2. Zhenyu Cui & J. Lars Kirkby & Guanghua Lian & Duy Nguyen, 2017. "Integral Representation Of Probability Density Of Stochastic Volatility Models And Timer Options," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(08), pages 1-32, December.
    3. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2018. "Recent advancements in robust optimization for investment management," Annals of Operations Research, Springer, vol. 266(1), pages 183-198, July.
    4. Du, Ye & Lehrer, Ehud, 2020. "Constrained no-regret learning," Journal of Mathematical Economics, Elsevier, vol. 88(C), pages 16-24.
    5. Ashrafi, Hedieh & Thiele, Aurélie C., 2021. "A study of robust portfolio optimization with European options using polyhedral uncertainty sets," Operations Research Perspectives, Elsevier, vol. 8(C).

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    More about this item

    Keywords

    Approachability; Calibration; Regret minimization; Robust optimization; Option pricing; Arbitrage bounds;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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