Mean-risk analysis with enhanced behavioral content

Alessandra Cillo and Philippe Delquié

European Journal of Operational Research, 2014, vol. 239, issue 3, 764-775

Abstract: We study a mean-risk model derived from a behavioral theory of Disappointment with multiple reference points. One distinguishing feature of the risk measure is that it is based on mutual deviations of outcomes, not deviations from a specific target. We prove necessary and sufficient conditions for strict first and second order stochastic dominance, and show that the model is, in addition, a Convex Risk Measure. The model allows for richer, and behaviorally more plausible, risk preference patterns than competing models with equal degrees of freedom, including Expected Utility (EU), Mean–Variance (M-V), Mean-Gini (M-G), and models based on non-additive probability weighting, such as Dual Theory (DT). In asset allocation, the model allows a decision-maker to abstain from diversifying in a positive expected value risky asset if its performance does not meet a certain threshold, and gradually invest beyond this threshold, which appears more acceptable than the extreme solutions provided by either EU and M-V (always diversify) or DT and M-G (always plunge). In asset trading, the model provides no-trade intervals, like DT and M-G, in some, but not all, situations. An illustrative application to portfolio selection is presented. The model can provide an improved criterion for mean-risk analysis by injecting a new level of behavioral realism and flexibility, while maintaining key normative properties.

Keywords: Risk analysis; Uncertainty modeling; Utility theory; Stochastic dominance; Convex risk measures (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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Working Paper: Mean-Risk Analysis with Enhanced Behavioral Content (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:239:y:2014:i:3:p:764-775

DOI: 10.1016/j.ejor.2014.06.001

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