Bound estimation of multistage asset allocation based on MCMC

Author

Zhang, Xiao-Tao ; Li, Cui-Yu

Author_Institution

Sch. of Manage., Tianjin Univ., Tianjin

Volume

2

fYear

2008

fDate

12-15 July 2008

Firstpage

1178

Lastpage

1182

Abstract

Stochastic programming models in multistage asset allocation almost always require the calculation of some sort of expectation in the form of integral and its optimization. This paper is to provide a numerical solution of multidimensional integrals via Markov Chain Monte Carlo (MCMC) integration. The method proposed in this paper can alleviate the major drawback of multistage stochastic programming, i.e. the exponential growth of scenarios. The numerical simulation support our view.

Keywords

Markov processes; Monte Carlo methods; estimation theory; investment; stochastic programming; Markov Chain Monte Carlo integration; bound estimation; multidimensional integrals; multistage asset allocation; optimization; stochastic programming; Asset management; Cybernetics; Financial management; Integral equations; Machine learning; Mathematical programming; Monte Carlo methods; Multidimensional systems; Stochastic processes; Utility theory; MCMC; Stochastic programming; asset allocation; bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620582

Filename

4620582