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Expected Utility Theory, Optimal Portfolios, and Polyhedral Coherent Risk Measures*

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Abstract

Searching for optimal solutions in terms of expected utility theory is reduced to minimizing a risk measure. The technique of polyhedral coherent risk measures is used to reduce the search for optimal portfolio solutions in the obtained problems to the appropriate linear programming problems.

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References

  1. V. S. Kirilyuk, “Polyhedral coherent risk measures and optimal portfolios on the reward–risk ratio,” Cybern. Syst. Analysis, 50, No. 5, 724–740 (2014).

    Article  Google Scholar 

  2. H. M. Markowitz, “Portfolio selection,” J. Finance, 7(1), 77–91 (1952).

    Google Scholar 

  3. H. M. Markowitz, Portfolio Selection: Efficient Diversification of Investments, Wiley, New York (1959).

    Google Scholar 

  4. D. Bernoulli, “Specimen teoriae novae de mensura sortis,” Comment Acad. Sci. Imp. Petropolitanae, V, 175–192 (1738).

  5. J. Von Neumann and O. Morgenstern, Theory of Games and Economic Behavior, Princeton University Press, Princeton (1944).

    MATH  Google Scholar 

  6. L. J. Savage, The Foundation of Statistics, Wiley, New York (1954).

    Google Scholar 

  7. M. Allais, “Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’ecole americaine,” Econometrica, 21, 503–546 (1953).

    Article  MATH  MathSciNet  Google Scholar 

  8. D. Ellsberg, “Risk, ambiguity, and the Savage axioms,” Quart. J. Econ., 75, 643–669 (1961).

    Article  MATH  Google Scholar 

  9. D. Schmeidler, “Subjective probability and expected utility without additivity,” Econometrica, 57, 571–587 (1989).

    Article  MATH  MathSciNet  Google Scholar 

  10. I. Gilboa and D. Schmeidler, “Maxmin expected utility with non-unique prior,” J. Math. Economics, 18, 141–153 (1989).

    Article  MATH  MathSciNet  Google Scholar 

  11. V. I. Ivanenko and V. A. Labkovskii, The Uncertainty Problem in Decision Making [in Russian], Naukova Dumka, Kyiv (1990).

    Google Scholar 

  12. J. Quiggin, Generalized Expected Utility Theory: The Rank-Dependent Expected Utility Model, Kluwer Acad., Boston (1993).

    Book  Google Scholar 

  13. M. E. Yaari, “The dual theory of choice under risk,” Econometrica, 55, 95–115 (1987).

    Article  MATH  MathSciNet  Google Scholar 

  14. G. Choquet, “Theory of capacities,” Annales de l’Institut Fourier Grenoble, 5, 131–295 (1955).

    Article  MathSciNet  Google Scholar 

  15. H. P. Wächter and T. Mazzoni, “Consistent modeling of risk averse behavior with spectral risk measures,” Europ. J. Operational Research, 229(2), 487–495 (2013).

    Article  Google Scholar 

  16. C. Acerbi, “Spectral measures of risk: A coherent representation of subjective risk aversion,” J. Banking & Finance, 26(7), 1505–1518 (2002).

    Article  Google Scholar 

  17. M. Rothschild and J. Stiglitz, “Increasing risk I: A definition,” J. Econ. Theory, 2, 225–243 (1970).

    Article  MathSciNet  Google Scholar 

  18. E. N. Sereda, E. M. Bronshtein, S. T. Rachev, F. J. Fabozzi, W. Sun, and S. V. Stoyanov, “Distortion risk measures in portfolio optimization,” in: J. B. Guerard (ed.), Handbook of Portfolio Construction. Contemporary Applications of Markowitz Techniques, Springer, New York (2010), 649–673.

    Chapter  Google Scholar 

  19. S. S. Wang, V. R. Young, and H. H. Panjer, “Axiomatic characterization of insurance prices,” Insurance: Math. and Econ., 21(2), 173–183 (1997).

    MATH  MathSciNet  Google Scholar 

  20. S. S. Wang, “A class of distortion operators for pricing financial and insurance risks,” J. Risk and Insurance, 67(1), 15–36 (2000).

    Article  Google Scholar 

  21. A. Tsanakas and E. Desli, “Risk measures and theories of choice,” British Actuarial J., 9, 959–991 (2003).

    Article  Google Scholar 

  22. Yu. M. Ermoliev and P. S. Knopov, “Method of empirical means in stochastic programming problems,” Cybern. Syst. Analysis, 42, No. 6, 773–785 (2006).

    Article  MATH  Google Scholar 

  23. V. S. Kirilyuk, “Coherent risk measures and portfolio optimization problems,” Teoriya Optym. Rishen’, V. M. Glushkov Inst. of Cybernetics, NANU, Issue 2, 111–119 (2003).

  24. P. Artzner, F. Delbaen, J. M. Eber, and D. Heath, “Coherent measures of risk,” Math. Finance, 9, 203–228 (1999).

    Article  MATH  MathSciNet  Google Scholar 

  25. R. T. Rockafellar and S. Uryasev, “Optimization of conditional value-at-risk,” J. Risk, 2, 21–41 (2000).

    Google Scholar 

  26. V. S. Kirilyuk and A. S. Babanin, “Polyhedral risk measures and robust solutions,” Teoriya Optym. Rishen’, V. M. Glushkov Inst. of Cybernetics, NASU, Issue 7, 66–72 (2008).

  27. V. S. Kirilyuk, “Polyhedral coherent risk measures and investment portfolio optimization,” Cybern. Syst. Analysis, 44, No. 2, 250–260 (2008).

    Article  MATH  MathSciNet  Google Scholar 

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Correspondence to V. S. Kirilyuk.

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The study was partially supported by the International project within the frameworks of the cooperation with the International Institute of Applied Systems Analysis (IIASA), order of the Presidium of the NAS of Ukraine No. 212 of 2/28/2012.

Translated from Kibernetika i Sistemnyi Analiz, No. 6, November–December, 2014, pp. 63–72.

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Kirilyuk, V.S. Expected Utility Theory, Optimal Portfolios, and Polyhedral Coherent Risk Measures* . Cybern Syst Anal 50, 874–883 (2014). https://doi.org/10.1007/s10559-014-9678-5

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  • DOI: https://doi.org/10.1007/s10559-014-9678-5

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