Risk management with machine-learning-based algorithms

Simon F\'ecamp, Joseph Mikael and Xavier Warin

Papers from arXiv.org

Abstract: We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets. Sources of incompleteness cover illiquidity, untradable risk factors, discrete hedging dates and transaction costs. The proposed algorithms resulting strategies are compared to classical stochastic control techniques on several payoffs using a variance criterion. One of the proposed algorithm is flexible enough to be used with several existing risk criteria. We furthermore propose a new moment-based risk criteria.

Date: 2019-02, Revised 2020-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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