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Deep learning-based option pricing for Barndorff–Nielsen and Shephard model

Author

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  • Takuji Arai

    (Department of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan)

Abstract

This paper aims to develop a deep learning-based numerical method for option prices for the Barndorff–Nielsen and Shephard model, a representative jump-type stochastic volatility model. Using that option prices for the Barndorff–Nielsen and Shephard model satisfy a partial-integro differential equation, we will develop an effective numerical calculation method even in settings where conventional numerical methods are unavailable. In addition, we will implement some numerical experiments.

Suggested Citation

  • Takuji Arai, 2023. "Deep learning-based option pricing for Barndorff–Nielsen and Shephard model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(03), pages 1-16, September.
    • Handle: RePEc:wsi:ijfexx:v:10:y:2023:i:03:n:s2424786323500159
    DOI: 10.1142/S2424786323500159
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    Cited by:

    1. Takuji Arai & Yuto Imai, 2024. "Option pricing for Barndorff-Nielsen and Shephard model by supervised deep learning," Papers 2402.00445, arXiv.org.
    2. Arai, Takuji & Imai, Yuto, 2024. "Monte Carlo simulation for Barndorff–Nielsen and Shephard model under change of measure," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 223-234.

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