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 pr...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research 2006-06, Vol.171 (3), p.977-990
Main Authors: Soyer, Refik, Tanyeri, Kadir
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian inference
Decision analysis
Dynamic programming
Operations research
Optimization
Portfolio management
Portfolio optimization
Simulation
Stochastic models
Stochastic volatility
Studies
Utility functions
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2005.01.012