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Machine learning methods for American-style path-dependent contracts

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  • Matteo Gambara
  • Giulia Livieri
  • Andrea Pallavicini

Abstract

In the present work, we introduce and compare state-of-the-art algorithms, that are now classified under the name of machine learning, to price Asian and look-back products with early-termination features. These include randomized feed-forward neural networks, randomized recurrent neural networks, and a novel method based on signatures of the underlying price process. Additionally, we explore potential applications on callable certificates. Furthermore, we present an innovative approach for calculating sensitivities, specifically Delta and Gamma, leveraging Chebyshev interpolation techniques.

Suggested Citation

  • Matteo Gambara & Giulia Livieri & Andrea Pallavicini, 2023. "Machine learning methods for American-style path-dependent contracts," Papers 2311.16762, arXiv.org.
    • Handle: RePEc:arx:papers:2311.16762
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    References listed on IDEAS

    as
    1. Thompson, Andrew C., 1995. "Valuation of Path-Dependent Contingent Claims with Multiple Exercise Decisions over Time: The Case of Take-or-Pay," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(2), pages 271-293, June.
    2. Sebastian Becker & Patrick Cheridito & Arnulf Jentzen, 2019. "Pricing and hedging American-style options with deep learning," Papers 1912.11060, arXiv.org, revised Jul 2020.
    3. Dai, Min & Li, Peifan & Zhang, Jin E., 2010. "A lattice algorithm for pricing moving average barrier options," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 542-554, March.
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