Multi-Stage Stochastic Linear Programs for Portfolio Optimization

Abstract

The paper demonstrates how multi-period portfolio optimization problems can be efficiently solved as multi-stage stochastic linear programs. A scheme based on a blending of classical Benders decomposition techniques and a special technique, called importance sampling, is used to solve this general class of multi-stage stochastic linear programs. We discuss the case where stochastic parameters are dependent within a period as well as between periods. Initial computational results are presented.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA242510

Entities

People

  • George Bernard Dantzig
  • Gerd Infanger

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Computations
  • Computer Programming
  • Decomposition
  • Discrete Distribution
  • Estimators
  • Linear Programming
  • Mathematical Models
  • Mathematical Programming
  • Models
  • Operations Research
  • Optimization
  • Probability
  • Regression Analysis
  • Sampling

Readers

  • Operations Research