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In this paper, we explain the application of model predictive control (MPC) to problems of dynamic portfolio optimization. At first we prove that MPC is a suboptimal control strategy for stochastic systems which uses the new information advantageously and thus, is better than pure optimal open-loop control. For a linear Gaussian factor model, we derive the wealth dynamics and the conditional mean and variance. We state the portfolio optimization, where an investor maximizes the mean-variance objective while keeping the portfolio value-at-risk under a given limit. The portfolio optimization is applied in a case study to US asset market data.
Herzog et al. (Sun,) studied this question.