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Bayesian portfolio selection with multi-variate random variance models
Institution:1. School of Business and Public Management, Department of Management Science, Monroe Hall 403, The George Washington University, 2115 G Street, NW, Washington, DC 20052, USA;2. Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC 20052, USA;1. Vilnius Gediminas Technical University, Sauletekio st 11, Vilnius, Lithuania;2. DIKU - Department of Computer Science, University of Copenhagen, Universitetsparken 1, DK-2100 Copenhagen, Denmark;1. Otto-von-Guericke-University Magdeburg, Faculty of Economics and Management, Management Science, P.O. Box 4120, 39016 Magdeburg, Germany;2. Department of Industrial & Systems Engineering, University of Florida, 303 West Hall, Gainesville, FL 32611-6595, USA;3. Department of Industrial Engineering, Bogaziçi University, 34342, Bebek-Istanbul, Turkey;4. Koç University, Department of Industrial Engineering, 34450 Sariyer-Istanbul, Turkey;5. University of Vienna, Department of Management Science, Bruenner Strasse 72, 1210 Vienna, Austria;1. Otto-von-Guericke University, Magdeburg, Germany;2. Shippensburg University, Shippensburg, PA, USA;3. Kuehne Logistics University, Hamburg, Germany;1. Intelligent Manufacturing Systems (IMS) Center, Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, ON, Canada;2. Mechanical and Industrial Engineering, University of Minnesota Duluth, Duluth, MN, USA
Abstract: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.
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