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Time-Inconsistent Markovian Control Problems Under Model Uncertainty With Application To The Mean-Variance Portfolio Selection

Author

Listed:
  • TOMASZ R. BIELECKI

    (Department of Applied Mathematics, Illinois Institute of Technology, 10 W 32nd Street, Building RE, Room 220, Chicago, IL 60616, USA)

  • TAO CHEN

    (Department of Mathematics, University of Michigan, 530 Church Street, East Hall, Room 2859, Ann Arbor, MI 48109, USA)

  • IGOR CIALENCO

    (Department of Applied Mathematics, Illinois Institute of Technology, 10 W 32nd Street, Building RE, Room 220, Chicago, IL 60616, USA)

Abstract

In this paper, we study a class of time-inconsistent terminal Markovian control problems in discrete time subject to model uncertainty. We combine the concept of the sub-game perfect strategies with the adaptive robust stochastic control method to tackle the theoretical aspects of the considered stochastic control problem. Consequently, as an important application of the theoretical results and by applying a machine learning algorithm we solve numerically the mean-variance portfolio selection problem under the model uncertainty.

Suggested Citation

  • Tomasz R. Bielecki & Tao Chen & Igor Cialenco, 2021. "Time-Inconsistent Markovian Control Problems Under Model Uncertainty With Application To The Mean-Variance Portfolio Selection," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 24(01), pages 1-28, February.
  • Handle: RePEc:wsi:ijtafx:v:24:y:2021:i:01:n:s0219024921500035
    DOI: 10.1142/S0219024921500035
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    Cited by:

    1. Luca De Gennaro Aquino & Sascha Desmettre & Yevhen Havrylenko & Mogens Steffensen, 2024. "Equilibrium control theory for Kihlstrom-Mirman preferences in continuous time," Papers 2407.16525, arXiv.org, revised Oct 2024.
    2. Marcin Pitera & {L}ukasz Stettner, 2022. "Discrete-time risk sensitive portfolio optimization with proportional transaction costs," Papers 2201.02828, arXiv.org.

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