Mean variance optimization of non-linear systems and worst-case analysis

In this paper, we consider expected value, variance and worst-case optimization of nonlinear models. We present algorithms for computing optimal expected values, and variance, based on iterative Taylor expansions. We est
In this paper, we consider expected value, variance and worst-case optimization of nonlinear models. We present algorithms for computing optimal expected values, and variance, based on iterative Taylor expansions. We establish convergence and consider the relative merits of policies beaded on expected value optimization and worst-case robustness. The latter is a minimax strategy and ensures optimal cover in view of the worst-case scenario(s) while the former is optimal expected performance in a stochastic setting. Both approaches are used with a macroeconomic policy model to illustrate relative performances, robustness and trade-offs between the strategies. Klassifikation: C61, E43
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Metadaten
Author:Panos Parpas, Berc Rustem, Volker Wieland, Stan Zakovic
URN:urn:nbn:de:hebis:30-25940
Series (Serial Number):CFS working paper series (2006, 03)
Document Type:Working Paper
Language:English
Date of Publication (online):2006/05/04
Year of first Publication:2006
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2006/05/04
Source:CFS working paper ; 2006,03
HeBIS PPN:190637528
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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