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Portfolio choice and optimal hedging with general risk functions: A simplex-like algorithm

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  • Balbás, Alejandro
  • Balbás, Raquel
  • Mayoral, Silvia

Abstract

The minimization of general risk functions is becoming more and more important in portfolio choice theory and optimal hedging. There are two major reasons. Firstly, heavy tails and the lack of symmetry in the returns of many assets provokes that the classical optimization of the standard deviation may lead to dominated strategies, from the point of view of the second order stochastic dominance. Secondly, but not less important, many institutional investors must respect legal capital requirements, which may be more easily studied if one deals with a risk measure related to capital losses. This paper proposes a new method to simultaneously minimize several general risk or dispersion measures. The representation theorems of risk functions are applied to transform the general risk minimization problem in a minimax problem, and later in a linear programming problem between infinite-dimensional Banach spaces. Then, new necessary and sufficient optimality conditions are stated and a simplex-like algorithm is developed. The algorithm solves the dual problem and provides both optimal portfolios and their sensitivities. The approach is general enough and does not depend on any particular risk measure, but some of the most important cases are specially analyzed. A final real data numerical example illustrates the practical performance of the proposed methodology.

Suggested Citation

  • Balbás, Alejandro & Balbás, Raquel & Mayoral, Silvia, 2009. "Portfolio choice and optimal hedging with general risk functions: A simplex-like algorithm," European Journal of Operational Research, Elsevier, vol. 192(2), pages 603-620, January.
  • Handle: RePEc:eee:ejores:v:192:y:2009:i:2:p:603-620
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    References listed on IDEAS

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    Cited by:

    1. Yu, Bosco Wing-Tong & Pang, Wan Kai & Troutt, Marvin D. & Hou, Shui Hung, 2009. "Objective comparisons of the optimal portfolios corresponding to different utility functions," European Journal of Operational Research, Elsevier, vol. 199(2), pages 604-610, December.
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    3. Hirbod Assa & Keivan Mallahi Karai, 2013. "Hedging, Pareto Optimality, and Good Deals," Journal of Optimization Theory and Applications, Springer, vol. 157(3), pages 900-917, June.
    4. Balbás, Raquel & Balbás, Alejandro, 2009. "Compatibility between pricing rules and risk measures: the CCVaR," DEE - Working Papers. Business Economics. WB wb090201, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    5. Hirbod Assa, 2015. "Trade-off Between Robust Risk Measurement and Market Principles," Journal of Optimization Theory and Applications, Springer, vol. 166(1), pages 306-320, July.
    6. Balbás, Alejandro & Balbás, Beatriz & Heras, Antonio, 2009. "Optimal reinsurance with general risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 44(3), pages 374-384, June.
    7. Li Cunbin & Liu Yunqi & Li Shuke, 2015. "A Dynamic Model of Procurement Risk Element Transmission in Construction Projects," Journal of Systems Science and Information, De Gruyter, vol. 3(2), pages 133-144, April.
    8. Akhter Mohiuddin Rather, 2012. "Portfolio selection using mean-risk model and mean-risk diversification model," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 14(3), pages 324-342.

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