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Stochastic Optimization Models in Finance

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  • Ziemba, W. T.
  • Vickson, R. G.

Abstract

Stochastic Optimization Models in Finance focuses on the applications of stochastic optimization models in finance, with emphasis on results and methods that can and have been utilized in the analysis of real financial problems. The discussions are organized around five themes: mathematical tools; qualitative economic results; static portfolio selection models; dynamic models that are reducible to static models; and dynamic models. This volume consists of five parts and begins with an overview of expected utility theory, followed by an analysis of convexity and the Kuhn-Tucker conditions. The reader is then introduced to dynamic programming; stochastic dominance; and measures of risk aversion. Subsequent chapters deal with separation theorems; existence and diversification of optimal portfolio policies; effects of taxes on risk taking; and two-period consumption models and portfolio revision. The book also describes models of optimal capital accumulation and portfolio selection. This monograph will be of value to mathematicians and economists as well as to those interested in economic theory and mathematical economics.

Suggested Citation

  • Ziemba, W. T. & Vickson, R. G. (ed.), 1975. "Stochastic Optimization Models in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780127808505.
    • Handle: RePEc:eee:monogr:9780127808505
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    Cited by:

    1. William T. Ziemba, 2013. "Portfolio optimization: theory and practical implementation," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 2, pages 45-72, Edward Elgar Publishing.

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