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Machine Learning With Kernels for Portfolio Valuation and Risk Management

Author

Listed:
  • Lotfi Boudabsa

    (Ecole Polytechnique Fédérale de Lausanne - School of Basic Sciences)

  • Damir Filipović

    (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute)

Abstract

We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size.

Suggested Citation

  • Lotfi Boudabsa & Damir Filipović, 2019. "Machine Learning With Kernels for Portfolio Valuation and Risk Management," Swiss Finance Institute Research Paper Series 19-34, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1934
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    Cited by:

    1. Linda Chamakh & Zolt'an Szab'o, 2021. "Kernel Minimum Divergence Portfolios," Papers 2110.09516, arXiv.org.
    2. Chamakh, Linda & Szabo, Zoltan, 2021. "Kernel minimum divergence portfolios," LSE Research Online Documents on Economics 115723, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    dynamic portfolio valuation; kernel ridge regression; learning theory; reproducing kernel Hilbert space; portfolio risk management;
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