Machine Learning With Kernels for Portfolio Valuation and Risk Management

Lotfi Boudabsa and Damir Filipović
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Lotfi Boudabsa: Ecole Polytechnique Fédérale de Lausanne - School of Basic Sciences
Damir Filipović: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute

No 19-34, Swiss Finance Institute Research Paper Series from 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.

Keywords: dynamic portfolio valuation; kernel ridge regression; learning theory; reproducing kernel Hilbert space; portfolio risk management (search for similar items in EconPapers)
Pages: 38 pages
Date: 2019-06
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1934

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