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Weighted Monte Carlo: A New Technique For Calibrating Asset-Pricing Models

Author

Listed:
  • MARCO AVELLANEDA

    (Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA)

  • ROBERT BUFF

    (Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA)

  • CRAIG FRIEDMAN

    (Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA)

  • NICOLAS GRANDECHAMP

    (Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA)

  • LUKASZ KRUK

    (Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA)

  • JOSHUA NEWMAN

    (Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA)

Abstract

A general approach for calibrating Monte Carlo models to the market prices of benchmark securities is presented. Starting from a given model for market dynamics (price diffusion, rate diffusion, etc.), the algorithm corrects price-misspecifications and finite-sample effects in the simulation by assigning "probability weights" to the simulated paths. The choice of weights is done by minimizing the Kullback–Leibler relative entropy distance of the posterior measure to the empirical measure. The resulting ensemble prices the given set of benchmark instruments exactly or in the sense of least-squares. We discuss pricing and hedging in the context of these weighted Monte Carlo models. A significant reduction of variance is demonstrated theoretically as well as numerically. Concrete applications to the calibration of stochastic volatility models and term-structure models with up to 40 benchmark instruments are presented. The construction of implied volatility surfaces and forward-rate curves and the pricing and hedging of exotic options are investigated through several examples.

Suggested Citation

  • Marco Avellaneda & Robert Buff & Craig Friedman & Nicolas Grandechamp & Lukasz Kruk & Joshua Newman, 2001. "Weighted Monte Carlo: A New Technique For Calibrating Asset-Pricing Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 91-119.
    • Handle: RePEc:wsi:ijtafx:v:04:y:2001:i:01:n:s0219024901000882
    DOI: 10.1142/S0219024901000882
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    Citations

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    Cited by:

    1. Aleksandrina Goeva & Henry Lam & Huajie Qian & Bo Zhang, 2019. "Optimization-Based Calibration of Simulation Input Models," Operations Research, INFORMS, vol. 67(5), pages 1362-1382, September.
    2. Andre Catalao & Rogerio Rosenfeld, 2018. "Analytical Path-Integral Pricing of Moving-Barrier Options under non-Gaussian Distributions," Papers 1804.07852, arXiv.org.
    3. Jim Gatheral, 2023. "Marco Avellaneda: Mathematician and trader," Mathematical Finance, Wiley Blackwell, vol. 33(1), pages 16-18, January.
    4. Pierre Henry-Labordere, 2019. "From (Martingale) Schrodinger bridges to a new class of Stochastic Volatility Models," Working Papers hal-02090807, HAL.
    5. Hilmar Gudmundsson & David Vyncke, 2021. "A Generalized Weighted Monte Carlo Calibration Method for Derivative Pricing," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    6. 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.
    7. Davide Lauria & Wyatt D. Phillips, 2021. "Insuring Hollywood: A Movie Returns Index and the American Stock Market," JRFM, MDPI, vol. 14(5), pages 1-33, April.
    8. Paul Glasserman & Bin Yu, 2005. "Large Sample Properties of Weighted Monte Carlo Estimators," Operations Research, INFORMS, vol. 53(2), pages 298-312, April.
    9. Rama Cont, 2023. "In memoriam: Marco Avellaneda (1955–2022)," Mathematical Finance, Wiley Blackwell, vol. 33(1), pages 3-15, January.
    10. Pierre Henry-Labordere, 2019. "From (Martingale) Schrodinger bridges to a new class of Stochastic Volatility Models," Papers 1904.04554, arXiv.org.
    11. André Catalão & Rogério Rosenfeld, 2020. "Analytical Path-Integral Pricing Of Deterministic Moving-Barrier Options Under Non-Gaussian Distributions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 23(01), pages 1-52, February.
    12. Hadrien De March & Pierre Henry-Labordere, 2019. "Building arbitrage-free implied volatility: Sinkhorn's algorithm and variants," Papers 1902.04456, arXiv.org, revised Jul 2023.
    13. Yanchu Liu & Chen Liu & Yiyao Chen & Xianming Sun, 2024. "Option‐Implied Ambiguity and Equity Return Predictability," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(9), pages 1556-1577, September.
    14. Marcel Nutz & Johannes Wiesel & Long Zhao, 2022. "Martingale Schr\"odinger Bridges and Optimal Semistatic Portfolios," Papers 2204.12250, arXiv.org.
    15. Davide Lauria & W. Brent Lindquist & Stefan Mittnik & Svetlozar T. Rachev, 2022. "ESG-Valued Portfolio Optimization and Dynamic Asset Pricing," Papers 2206.02854, arXiv.org.
    16. Marcel Nutz & Johannes Wiesel & Long Zhao, 2023. "Martingale Schrödinger bridges and optimal semistatic portfolios," Finance and Stochastics, Springer, vol. 27(1), pages 233-254, January.
    17. Hadrien de March & Pierre Henry-Labordere, 2019. "Building Arbitrage-Free Implied Volatility: Sinkhorn'S Algorithm And Variants," Working Papers hal-02011533, HAL.
    18. 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.
    19. Vinicius Albani & Adriano De Cezaro & Jorge P. Zubelli, 2017. "Convex Regularization Of Local Volatility Estimation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-37, February.
    20. S'andor Kuns'agi-M'at'e & G'abor F'ath & Istv'an Csabai & G'abor Moln'ar-S'aska, 2022. "Deep Weighted Monte Carlo: A hybrid option pricing framework using neural networks," Papers 2208.14038, arXiv.org, revised Dec 2022.

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