Machine learning with kernels for portfolio valuation and risk management

Lotfi Boudabsa (Ecole Polytechnique Fédérale de Lausanne), Damir Filipović (Ecole Polytechnique Fédérale de Lausanne and Swiss Finance Institute)

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

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Year of publication:	15 August 2019
Authors:	Boudabsa, Lotfi ; Filipović, Damir
Publisher:	Geneva : Swiss Finance Institute
Subject:	Portfolio-Management \| Portfolio selection \| Künstliche Intelligenz \| Artificial intelligence \| Theorie \| Theory \| Risikomanagement \| Risk management