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Leonid Churilov, Immanuel Bomze, Moshe Sniedovich and Daniel Ralph
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Leonid Churilov: School of Business Systems, Monash University, Melbourne, Australia
Moshe Sniedovich: Department of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
Daniel Ralph: Judge Institute of Management, Cambridge University, England

Asia-Pacific Journal of Operational Research (APJOR), 2004, vol. 21, issue 03, 297-317

Abstract: Hyper Sensitivity Analysis (HSA) is an intuitive generalization of conventional sensitivity analysis, where the term "hyper" indicates that the sensitivity analysis is conducted with respect to functions rather than numeric values. In this paper Composite Concave Programming is used to perform HSA in the area of Portfolio Optimization Problems. The concept of HSA is suited for situations where several candidates for the function quantifying the utility of (mean, variance) pairs are available. We discuss the applications of HSA to two types of mean–variance portfolio optimization problems: the classical one and a discrete knapsack-type portfolio selection problem. It is explained why in both cases the methodology can be applied to full size problems.

Keywords: Hyper sensitivity analysis; portfolio optimization; composite concave programming; quadratic programming; dynamic programming (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1142/S0217595904000175

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Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao

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