Parameter estimation in stochastic scenario generation systems

Parameter estimation in stochastic scenario generation systems

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Article ID: iaor20003269
Country: Netherlands
Volume: 118
Issue: 3
Start Page Number: 563
End Page Number: 577
Publication Date: Nov 1999
Journal: European Journal of Operational Research
Authors: , ,
Keywords: heuristics
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. 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.

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