Mean variance optimization of non-linear systems and worst-case analysis
AbstractIn 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. --
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Bibliographic InfoPaper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2006/03.
Date of creation: 2006
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