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

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  • Vladimir Dombrovskii
  • Tatiana Pashinskaya

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.

Suggested Citation

  • Vladimir Dombrovskii & Tatiana Pashinskaya, 2020. "Model predictive control design for constrained Markov jump bilinear stochastic systems with an application in finance," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3269-3284, December.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:16:p:3269-3284
    DOI: 10.1080/00207721.2020.1814892
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