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

Author

Listed:
  • Parpas, Panos
  • Rustem, Berc
  • Wieland, Volker
  • Zakovic, Stan

Abstract

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.

Suggested Citation

  • Parpas, Panos & Rustem, Berc & Wieland, Volker & Zakovic, Stan, 2006. "Mean variance optimization of non-linear systems and worst-case analysis," CFS Working Paper Series 2006/03, Center for Financial Studies (CFS).
  • Handle: RePEc:zbw:cfswop:200603
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    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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