Abstract
This paper develops stochastic receding horizon control for a constrained index tracking problem. By modeling the asset dynamics in the problems as a linear system subject to state and control multiplicative noise, and approximating linear chance constraints with quadratic expectation constraints, we show that index tracking can be approached using stochastic receding horizon control. In particular, we use a closed loop version of stochastic receding horizon control where the on-line optimization is solved as a semi-definite program. Numerical examples demonstrate the computations involved in these problems and indicate that stochastic receding horizon control is a promising new approach to constrained index tracking.
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