Machine Learning With Kernels for Portfolio Valuation and Risk Management

39 Pages Posted: 18 Jun 2019 Last revised: 16 Jan 2020

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 statistical 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 terminal cumulative cash flow. The learned value process 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 derive dimension-free sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small 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. 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

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