Machine Learning With Kernels for Portfolio Valuation and Risk Management

Swiss Finance Institute Research Paper No. 19-34

Finance and Stochastics, forthcoming

40 Pages Posted: 18 Jun 2019 Last revised: 10 Aug 2021

See all articles by Lotfi Boudabsa

Lotfi Boudabsa

Ecole Polytechnique Fédérale de Lausanne - School of Basic Sciences

Damir Filipović

Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute

Date Written: June 9, 2019

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.

Keywords: dynamic portfolio valuation, kernel ridge regression, learning theory, reproducing kernel Hilbert space, portfolio risk management

JEL Classification: C15, G32

Suggested Citation

Boudabsa, Lotfi and Filipovic, Damir, Machine Learning With Kernels for Portfolio Valuation and Risk Management (June 9, 2019). Swiss Finance Institute Research Paper No. 19-34, Finance and Stochastics, forthcoming, Available at SSRN: https://ssrn.com/abstract=3401539 or http://dx.doi.org/10.2139/ssrn.3401539

Lotfi Boudabsa

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

Lausanne
Switzerland

Damir Filipovic (Contact Author)

Ecole Polytechnique Fédérale de Lausanne ( email )

Odyssea
Station 5
Lausanne, 1015
Switzerland

HOME PAGE: http://people.epfl.ch/damir.filipovic

Swiss Finance Institute

c/o University of Geneva
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Switzerland

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