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Dynamic Portfolio Allocation, the Dual Theory of Choice and Probability Distortion Functions

Published online by Cambridge University Press:  17 April 2015

Mahmoud Hamada
Affiliation:
School of Finance and Economics, University of Technology of Sydney, Sydney, Australia, E-mail: [email protected]
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Abstract

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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.

Type
Articles
Copyright
Copyright © ASTIN Bulletin 2006

References

[1] Allais, M. (1953) Le comportement de l’homme rationnel devant le risque. Economertica, 21, 503546.CrossRefGoogle Scholar
[2] Bufman, G. and Leiderman, L. (1990) Consumption and asset returns under non-expected utility, some new evidence. Economics letters, 34, 231235.CrossRefGoogle Scholar
[3] Camerer, C.F. (1989) An experimental test of several generalized utility theories. Journal of Risk and Uncertainty, 2(1), 61104.CrossRefGoogle Scholar
[4] Epstein, L.G. and Zin, S.E. (1989) Substitution, risk aversion, and the temporal behavior of consumption and asset returns: An empirical analysis. Carnegie-Mellon University, Pittersburg, PA.Google Scholar
[5] Epstein, L.G. and Zin, S.E. (1989) Substitution, risk aversion, and the temporal behavior of consumption and asset returns: A theoretical framework. Econometrica, 57(4), 93769.CrossRefGoogle Scholar
[6] Fishburn, P.C. (1988) Nonlinear preference and utility theory. Johns Hopkins Series in the Mathematical Sciences, no 5. Baltimore and London, pp. xiv, 259.Google Scholar
[7] Giovannini, A. and Jorion, P. (1989) Time-series tests of a non-expected utility model of asset pricing. Columbia University, New York.Google Scholar
[8] Hamada, M. and Sherris, M. (2001) On the relationship between risk neutral valuation and pricing using distortion operators. Working paper.Google Scholar
[9] He, H. (1990) Convergence from discrete to continuous-time contingent claims prices. The Review of Financial Studies, 3(4), 523546.CrossRefGoogle Scholar
[10] Ingersoll, J.E. (1987) “Theory of Financial Decision Making”. Totowa, N.J. : Rowman and Littlefield.Google Scholar
[11] Kahneman, D. and Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica, 47, 263291.CrossRefGoogle Scholar
[12] Koskievic, J.-M. (1999) An intertemporal consumption-leisure model with non-expected utility. Economics Letters, 64, 285289.CrossRefGoogle Scholar
[13] Kreps, D.M. and Porteus, E.L. (1978) Temporal resolution of uncertainty and dynamic choice theory. Econometrica, 46(1), 185200.CrossRefGoogle Scholar
[14] Osborne, M. (2001) “Simplicial Algorithms for Minimizing Polyhedral Functions”. Cambridge University Press.Google Scholar
[15] Quiggin, J. (1982) A theory of anticipated utility. Journal of Economic Behavior and Organization, 3, 323343.CrossRefGoogle Scholar
[16] Roel, A. (1985) Risk aversion in quiggin and yaari’s rank-order model of choice under uncertainty. The Economic Journal.Google Scholar
[17] Ross, S.A. (1981) Some stronger measures of risk aversion in the small and in the large with aplications. Econometrica, 49, 621638.CrossRefGoogle Scholar
[18] Van Der Hoek, J. and Sherris, M. (2001) A class of non-expected utility risk measures and implications for asset allocations. Insurance: Mathematics and Economics 28, 6982.Google Scholar
[19] Wang, S. (1996) Ambiguity aversion and the economics of insurance. University of Waterloo, research report 96-04.Google Scholar
[20] Wang, S. (1996) Premium calculation by transforming the layer premium density. Astin Bulletin, 26, 7192.CrossRefGoogle Scholar
[21] Wang, S. (1998) Implementation of proportional hazards transforms in ratemaking. Proceedings of the Casualty Actuarial Society LXXXV. Available for download on http://www. casact.org/pubs/proceed/proceed98/index.htm.Google Scholar
[22] Wang, S.S. (2000) A class of distortion operators for pricing financial and insurance risks. The Journal of Risk and Insurance, 36(1), 1536.CrossRefGoogle Scholar
[23] Yaari, M.E. (1986) “Univariate and Multivariate Comparison of Risk Aversion: A New Approach”. Cambridge University Press.Google Scholar
[24] Yaari, M.E. (1987) The dual theory of choice under risk. Econometrica, 55, 95115.CrossRefGoogle Scholar