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Risk management with machine-learning-based algorithms

Author

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  • Simon F'ecamp
  • Joseph Mikael
  • Xavier Warin

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.

Suggested Citation

  • Simon F'ecamp & Joseph Mikael & Xavier Warin, 2019. "Risk management with machine-learning-based algorithms," Papers 1902.05287, arXiv.org, revised Aug 2020.
    • Handle: RePEc:arx:papers:1902.05287
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    References listed on IDEAS

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    3. repec:hal:wpaper:hal-01761234 is not listed on IDEAS
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    6. Achref Bachouch & C^ome Hur'e & Nicolas Langren'e & Huyen Pham, 2018. "Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications," Papers 1812.05916, arXiv.org, revised Jan 2020.
    7. Jim Gatheral, 2010. "No-dynamic-arbitrage and market impact," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 749-759.
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    9. Toft, Klaus Bjerre, 1996. "On the Mean-Variance Tradeoff in Option Replication with Transactions Costs," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(2), pages 233-263, June.
    10. Dimitris Bertsimas & Leonid Kogan & Andrew W. Lo, 2001. "When Is Time Continuous?," World Scientific Book Chapters, in: Marco Avellaneda (ed.), Quantitative Analysis In Financial Markets Collected Papers of the New York University Mathematical Finance Seminar(Volume II), chapter 3, pages 71-102, World Scientific Publishing Co. Pte. Ltd..
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    13. Schweizer, Martin, 1999. "A guided tour through quadratic hedging approaches," SFB 373 Discussion Papers 1999,96, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    Cited by:

    1. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.

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