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A convex duality method for optimal liquidation with participation constraints

Author

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  • Olivier Guéant

    (LJLL - Laboratoire Jacques-Louis Lions - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique)

  • Jean-Michel Lasry

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Jiang Pu

Abstract

In spite of the growing consideration for optimal execution issues in the financial mathematics literature, numerical approximations of optimal trading curves are almost never discussed. In this article, we present a numerical method to approximate the optimal strategy of a trader willing to unwind a large portfolio. The method we propose is very general as it can be applied to multi-asset portfolios with any form of execution costs, including a bid-ask spread component, even when participation constraints are imposed. Our method, based on convex duality, only requires Hamiltonian functions to have C^{1,1} regularity while classical methods require additional regularity and cannot be applied to all cases found in practice.

Suggested Citation

  • Olivier Guéant & Jean-Michel Lasry & Jiang Pu, 2015. "A convex duality method for optimal liquidation with participation constraints," Post-Print hal-01393127, HAL.
    • Handle: RePEc:hal:journl:hal-01393127
    DOI: 10.1142/S2382626615500021
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    Cited by:

    1. Charles-Albert Lehalle & Charafeddine Mouzouni, 2019. "A Mean Field Game of Portfolio Trading and Its Consequences On Perceived Correlations," Papers 1902.09606, arXiv.org.
    2. Olivier Guéant, 2016. "The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making," Post-Print hal-01393136, HAL.
    3. Joaquin Fernandez-Tapia & Olivier Gu'eant, 2020. "Recipes for hedging exotics with illiquid vanillas," Papers 2005.10064, arXiv.org, revised May 2020.

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