TIME-INCONSISTENT MARKOVIAN CONTROL PROBLEMS UNDER MODEL UNCERTAINTY WITH APPLICATION TO THE MEAN-VARIANCE PORTFOLIO SELECTION

Tomasz R. Bielecki (), Tao Chen and Igor Cialenco ()
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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

International Journal of Theoretical and Applied Finance (IJTAF), 2021, vol. 24, issue 01, 1-28

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.

Keywords: Adaptive robust control; model uncertainty; stochastic control; adaptive robust dynamic programming; recursive confidence regions; time-inconsistent Markovian control problem; optimal portfolio allocation; mean-variance portfolio selection; terminal criteria; machine learning; Gaussian surrogate processes; regression Monte Carlo (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219024921500035

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