Machine learning with kernels for portfolio valuation and risk management

Lotfi Boudabsa and Damir Filipovic

Papers from arXiv.org

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.

Date: 2019-06, Revised 2021-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-pay and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1906.03726

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