Model predictive control design for constrained Markov jump bilinear stochastic systems with an application in finance

Vladimir Dombrovskii and Tatiana Pashinskaya

International Journal of Systems Science, 2020, vol. 51, issue 16, 3269-3284

Abstract: In this study, we propose a solution to the model predictive control problem for a class of constrained discrete-time bilinear stochastic systems consisting of two coupled subsystems with Markov jumps. The first one includes a bilinear term in the state variables of the second subsystem and the input, whereas the second subsystem is described by a Markov switching vector autoregressive model. Furthermore, hard constraints imposed on the input manipulated variables. The results obtained are applied to the dynamic investment portfolio selection problem for a financial market with serially dependent returns and switching modes, subject to hard constraints on trading amounts. Our approach is tested on a real dataset from the New York Stock Exchange and the Russian Stock Exchange MOEX.

Date: 2020
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DOI: 10.1080/00207721.2020.1814892

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