Parameter estimation in stochastic scenario generation systems

John M. Mulvey, Daniel P. Rosenbaum, Bala Shetty

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Scenario analysis offers an effective tool for addressing the stochastic elements in multi-period financial planning models. Critical to any scenario generation process is the estimation of the input parameters of the underlying stochastic model for economic factors. In this paper, we propose a new approach for estimation, known as the integrated parameter estimation (IPE). This approach combines the significant features of other well-known estimation techniques within a non-convex multiple objective optimization framework, with the objective weights controlling the relative importance of the features. We solve the non-convex optimization problem using adaptive memory programming - a variation of tabu search. Based on a short interest rate model using UK treasury rates from 1980 to 1995, the integrated approach compares favorably with maximum likelihood and the generalized method of moments. We also evaluate performance with Towers Perrin's CAP:Link scenario generation system.

Original languageEnglish (US)
Pages (from-to)563-577
Number of pages15
JournalEuropean Journal of Operational Research
Volume118
Issue number3
DOIs
StatePublished - Nov 1 1999

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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