Bayesian portfolio selection with multi-variate random variance models

We consider multi-period portfolio selection problems for a decision maker with a specified utility function when the variance of security returns is described by a discrete time stochastic model. The solution of these problems involves a dynamic programming formulation and backward induction. We present a simulation-based method to solve these problems adopting an approach which replaces the preposterior analysis by a surface fitting based optimization approach. We provide examples to illustrate the implementation of our approach.

• Type: Journal
• Year: 2006
• Where: EOR
• Authors: Refik Soyer, Kadir Tanyeri
• Keywords: Dynamic Programming Formulation, EOR 2006, Multi-period Portfolio Selection, Time Stochastic Model