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Optimal Asset Allocation Subject to Withdrawal Risk and Solvency Constraints

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  • Areski Cousin

    (Institut de Recherche en Mathématique Avancée, Université de Strasbourg, 7 rue René Descartes, 67084 Strasbourg, France
    These authors contributed equally to this work.)

  • Ying Jiao

    (Institut de Science Financière et d’Assurances, Université Claude Bernard Lyon 1, 50 Avenue Tony Garnier, 69007 Lyon, France
    These authors contributed equally to this work.)

  • Christian Yann Robert

    (ENSAE IPP, 5 Avenue Le Chatelier, 91120 Palaiseau, France
    These authors contributed equally to this work.)

  • Olivier David Zerbib

    (Finance Department, Questrom School of Business, Boston University, 595 Commonwealth Avenue, Boston, MA 02215, USA
    These authors contributed equally to this work.)

Abstract

This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. In accounting for the asset-liability mismatch risk of the institution, we present a general utility optimization problem in a discrete-time setting and provide a dynamic programming principle for the optimal investment strategies. Furthermore, we consider an explicit context, including liquidity risk, interest rate, and credit intensity fluctuations, and show by numerical results that the optimal strategy improves both the solvency and asset returns of the institution compared to a standard institutional investor’s asset allocation.

Suggested Citation

  • Areski Cousin & Ying Jiao & Christian Yann Robert & Olivier David Zerbib, 2022. "Optimal Asset Allocation Subject to Withdrawal Risk and Solvency Constraints," Risks, MDPI, vol. 10(1), pages 1-28, January.
  • Handle: RePEc:gam:jrisks:v:10:y:2022:i:1:p:15-:d:719425
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    Cited by:

    1. Benjamín Vallejo-Jiménez & Francisco Venegas-Martínez & Oscar V. De la Torre-Torres & José Álvarez-García, 2022. "Simulating Portfolio Decisions under Uncertainty When the Risky Asset and Short Rate Are Modulated by an Inhomogeneous and Asset-Dependent Markov Chain," Mathematics, MDPI, vol. 10(16), pages 1-14, August.

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