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Stochastic Programming Approximations Using Limited Moment Information, with Application to Asset Allocation

In: Stochastic Programming

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  • N. Chanaka P. Edirisinghe

    (College of Business Administration, University of Tennessee)

Abstract

In this chapter, stochastic programming solution methods that utilize bound-based approximations are discussed. The focus is on bounds that use limited moment information of the underlying random vectors to approximate the expected recourse function. Bounds that use first-order moments, as well as higher-order moments are described in the context of saddle functions and specialized for convex recourse functions. Relation of these bounds to generalized moment problems is also addressed. Bounds are applied within sequential approximation schemes to solve the underlying stochastic program to a user-specified degree of accuracy. Appropriate partitioning techniques for the domains of the random variables are described and applied in a financial asset allocation stochastic program. Useful solution characteristics as well as sensitivity analyses are discussed using the bound-based solution procedure.

Suggested Citation

  • N. Chanaka P. Edirisinghe, 2010. "Stochastic Programming Approximations Using Limited Moment Information, with Application to Asset Allocation," International Series in Operations Research & Management Science, in: Gerd Infanger (ed.), Stochastic Programming, chapter 0, pages 97-138, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1642-6_6
    DOI: 10.1007/978-1-4419-1642-6_6
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