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KANOP: A Data-Efficient Option Pricing Model using Kolmogorov-Arnold Networks

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  • Rushikesh Handal
  • Kazuki Matoya
  • Yunzhuo Wang
  • Masanori Hirano

Abstract

Inspired by the recently proposed Kolmogorov-Arnold Networks (KANs), we introduce the KAN-based Option Pricing (KANOP) model to value American-style options, building on the conventional Least Square Monte Carlo (LSMC) algorithm. KANs, which are based on Kolmogorov-Arnold representation theorem, offer a data-efficient alternative to traditional Multi-Layer Perceptrons, requiring fewer hidden layers to achieve a higher level of performance. By leveraging the flexibility of KANs, KANOP provides a learnable alternative to the conventional set of basis functions used in the LSMC model, allowing the model to adapt to the pricing task and effectively estimate the expected continuation value. Using examples of standard American and Asian-American options, we demonstrate that KANOP produces more reliable option value estimates, both for single-dimensional cases and in more complex scenarios involving multiple input variables. The delta estimated by the KANOP model is also more accurate than that obtained using conventional basis functions, which is crucial for effective option hedging. Graphical illustrations further validate KANOP's ability to accurately model the expected continuation value for American-style options.

Suggested Citation

  • Rushikesh Handal & Kazuki Matoya & Yunzhuo Wang & Masanori Hirano, 2024. "KANOP: A Data-Efficient Option Pricing Model using Kolmogorov-Arnold Networks," Papers 2410.00419, arXiv.org.
    • Handle: RePEc:arx:papers:2410.00419
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Hatem Ben-Ameur & Michèle Breton & Pierre L'Ecuyer, 2002. "A Dynamic Programming Procedure for Pricing American-Style Asian Options," Management Science, INFORMS, vol. 48(5), pages 625-643, May.
    3. Yangang Chen & Justin W. L. Wan, 2021. "Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions," Quantitative Finance, Taylor & Francis Journals, vol. 21(1), pages 45-67, January.
    4. Masaaki Fujii & Seisho Sato & Akihiko Takahashi, 2015. "An FBSDE Approach to American Option Pricing with an Interacting Particle Method," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(3), pages 239-260, September.
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    Cited by:

    1. Kun Liu & Jin Zhao, 2024. "KACDP: A Highly Interpretable Credit Default Prediction Model," Papers 2411.17783, arXiv.org.

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