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A highly efficient tensor network algorithm for multi-asset Fourier options pricing

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  • Michael Kastoryano
  • Nicola Pancotti

Abstract

Risk assessment and in particular derivatives pricing is one of the core areas in computational finance and accounts for a sizeable fraction of the global computing resources of the financial industry. We outline a quantum-inspired algorithm for multi-asset options pricing. The algorithm is based on tensor networks, which have allowed for major conceptual and numerical breakthroughs in quantum many body physics and quantum computation. In the proof-of-concept example explored, the tensor network approach yields several orders of magnitude speedup over vanilla Monte Carlo simulations. We take this as good evidence that the use of tensor network methods holds great promise for alleviating the computation burden of risk evaluation in the financial and other industries, thus potentially lowering the carbon footprint these simulations incur today.

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  • Michael Kastoryano & Nicola Pancotti, 2022. "A highly efficient tensor network algorithm for multi-asset Fourier options pricing," Papers 2203.02804, arXiv.org.
    • Handle: RePEc:arx:papers:2203.02804
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    File URL: http://arxiv.org/pdf/2203.02804
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    Cited by:

    1. Christian Bayer & Chiheb Ben Hammouda & Antonis Papapantoleon & Michael Samet & Ra'ul Tempone, 2024. "Quasi-Monte Carlo for Efficient Fourier Pricing of Multi-Asset Options," Papers 2403.02832, arXiv.org.
    2. Rihito Sakurai & Haruto Takahashi & Koichi Miyamoto, 2024. "Learning parameter dependence for Fourier-based option pricing with tensor trains," Papers 2405.00701, arXiv.org, revised Oct 2024.

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