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Solving Multistage Asset Investment Problems by the Sample Average Approximation Method

Jorgen Blomvall (johan***at***mai.liu.se)
Alexander Shapiro (ashapiro***at***isye.gatech.edu)

Abstract: The vast size of real world stochastic programming instances requires sampling to make them practically solvable. In this paper we extend the understanding of how sampling affects the solution quality of multistage stochastic programming problems. We present a new heuristic for determining good feasible solutions for a multistage decision problem. For power and log-utility functions we address the question of how tree structures, number of stages, number of outcomes and number of assets affect the solution quality. We also present a new method for evaluating the quality of first stage decisions.


Category 1: Stochastic Programming

Category 2: Applications -- OR and Management Sciences (Finance and Economics )

Citation: Stochastic Programming, Asset Allocation, Monte Carlo Sampling, Statistical Bounds

Download: [PDF]

Entry Submitted: 06/28/2004
Entry Accepted: 06/28/2004
Entry Last Modified: 06/27/2005

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