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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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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 to tackle the theoretical aspects of the considered stochastic control problem. Consequently, as an important application of the theoretical results, by applying a machine learning algorithm we solve numerically the mean-variance portfolio selection problem under the model uncertainty.

Date: 2020-02, Revised 2020-09
New Economics Papers: this item is included in nep-cmp and nep-ore
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