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

2014 ◽  
Vol 50 (6) ◽  
pp. 874-883 ◽  
Author(s):  
V. S. Kirilyuk
2006 ◽  
Vol 36 (02) ◽  
pp. 505-520 ◽  
Author(s):  
Marisa Cenci ◽  
Massimiliano Corradini ◽  
Andrea Gheno

In this paper the dynamic portfolio selection problem is studied for the first time in a dual utility theory framework. The Wang transform is used as distortion function and well diversified optimal portfolios result both with and without short sales allowed.


2006 ◽  
Vol 36 (01) ◽  
pp. 187-217 ◽  
Author(s):  
Mahmoud Hamada ◽  
Michael Sherris ◽  
John van der Hoek

Standard optimal portfolio choice models assume that investors maximise the expected utility of their future outcomes. However, behaviour which is inconsistent with the expected utility theory has often been observed. In a discrete time setting, we provide a formal treatment of risk measures based on distortion functions that are consistent with Yaari’s dual (non-expected utility) theory of choice (1987), and set out a general layout for portfolio optimisation in this non-expected utility framework using the risk neutral computational approach. As an application, we consider two particular risk measures. The first one is based on the PH-transform and treats the upside and downside of the risk differently. The second one, introduced by Wang (2000) uses a probability distortion operator based on the cumulative normal distribution function. Both risk measures rank-order prospects and apply a distortion function to the entire vector of probabilities.


2003 ◽  
Vol 9 (4) ◽  
pp. 959-991 ◽  
Author(s):  
A. Tsanakas ◽  
E. Desli

ABSTRACTWe discuss classes of risk measures in terms both of their axiomatic definitions and of the economic theories of choice that they can be derived from. More specifically, expected utility theory gives rise to the exponential premium principle, proposed by Gerber (1974), Dhaene et al. (2003), whereas Yaari's (1987) dual theory of choice under risk can be viewed as the source of the distortion premium principle (Denneberg, 1990; Wang, 1996). We argue that the properties of the exponential and distortion premium principles are complementary, without either of the two performing completely satisfactorily as a risk measure. Using generalised expected utility theory (Quiggin, 1993), we derive a new risk measure, which we call the distortion-exponential principle. This risk measure satisfies the axioms of convex measures of risk, proposed by Föllmer & Shied (2002a,b), and its properties lie between those of the exponential and distortion principles, which can be obtained as special cases.


2006 ◽  
Vol 36 (2) ◽  
pp. 505-520
Author(s):  
Marisa Cenci ◽  
Massimiliano Corradini ◽  
Andrea Gheno

In this paper the dynamic portfolio selection problem is studied for the first time in a dual utility theory framework. The Wang transform is used as distortion function and well diversified optimal portfolios result both with and without short sales allowed.


2006 ◽  
Vol 36 (1) ◽  
pp. 187-217 ◽  
Author(s):  
Mahmoud Hamada ◽  
Michael Sherris ◽  
John van der Hoek

Standard optimal portfolio choice models assume that investors maximise the expected utility of their future outcomes. However, behaviour which is inconsistent with the expected utility theory has often been observed.In a discrete time setting, we provide a formal treatment of risk measures based on distortion functions that are consistent with Yaari’s dual (non-expected utility) theory of choice (1987), and set out a general layout for portfolio optimisation in this non-expected utility framework using the risk neutral computational approach.As an application, we consider two particular risk measures. The first one is based on the PH-transform and treats the upside and downside of the risk differently. The second one, introduced by Wang (2000) uses a probability distortion operator based on the cumulative normal distribution function. Both risk measures rank-order prospects and apply a distortion function to the entire vector of probabilities.


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