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Machine learning with kernels for portfolio valuation and risk management

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  • Lotfi Boudabsa
  • Damir Filipovic

Abstract

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.

Suggested Citation

  • Lotfi Boudabsa & Damir Filipovic, 2019. "Machine learning with kernels for portfolio valuation and risk management," Papers 1906.03726, arXiv.org, revised May 2021.
    • Handle: RePEc:arx:papers:1906.03726
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    File URL: http://arxiv.org/pdf/1906.03726
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    Cited by:

    1. A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Deep Stochastic Optimization in Finance," Papers 2205.04604, arXiv.org.
    2. Lucio Fernandez‐Arjona & Damir Filipović, 2022. "A machine learning approach to portfolio pricing and risk management for high‐dimensional problems," Mathematical Finance, Wiley Blackwell, vol. 32(4), pages 982-1019, October.
    3. A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2023. "Deep stochastic optimization in finance," Digital Finance, Springer, vol. 5(1), pages 91-111, March.

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