Model predictive control for portfolio selection
- 1 January 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
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 dataKeywords
This publication has 10 references indexed in Scilit:
- A two-layered optimisation-based control strategy for multi-echelon supply chain networksComputers & Chemical Engineering, 2004
- Model predictive control for dynamic unreliable resource allocationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Stochastic linear model predictive control using nested decompositionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Strategic Asset AllocationPublished by Oxford University Press (OUP) ,2002
- Closed-loop stochastic dynamic process optimization under input and state constraintsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- The explicit linear quadratic regulator for constrained systemsAutomatica, 2001
- Robust model predictive control under chance constraintsComputers & Chemical Engineering, 2000
- Constrained model predictive control: Stability and optimalityAutomatica, 2000
- Strategic asset allocationJournal of Economic Dynamics and Control, 1997
- Application of Jensen's inequality to adaptive suboptimal designJournal of Optimization Theory and Applications, 1980