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An unsupervised deep learning approach to solving partial integro-differential equations

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  • Weilong Fu
  • Ali Hirsa

Abstract

We investigate solving partial integro-differential equations (PIDEs) using unsupervised deep learning. The PIDE is employed for option pricing, when the underlying process is a Lévy process with jumps. The unsupervised deep learning approach employs a neural network as the candidate solution and trains the neural network to satisfy the PIDE. By matching the PIDE and the boundary conditions, the neural network would yield an accurate solution to the PIDE. Unlike supervised learning, this approach does not require any labels for training, where labels are typically option prices as well as Greeks. Additional singular terms are added to the neural network to satisfy the non-smooth initial conditions. Once trained, the neural network would be fast for calculating option prices as well as option Greeks.

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  • Weilong Fu & Ali Hirsa, 2022. "An unsupervised deep learning approach to solving partial integro-differential equations," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1481-1494, August.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:8:p:1481-1494
    DOI: 10.1080/14697688.2022.2057870
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    Cited by:

    1. Weilong Fu & Ali Hirsa, 2022. "Solving barrier options under stochastic volatility using deep learning," Papers 2207.00524, arXiv.org.

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