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Dependency of Risks and Stop-Loss Order1
Published online by Cambridge University Press: 29 August 2014
Abstract
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The correlation order, which is defined as a partial order between bivariate distributions with equal marginals, is shown to be a helpfull tool for deriving results concerning the riskiness of portfolios with pairwise dependencies. Given the distribution functions of the individual risks, it is investigated how changing the dependency assumption influences the stop-loss premiums of such portfolios.
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- Copyright © International Actuarial Association 1996
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Work performed under grant OT/93/5 of Onderzoeksfonds K.U.Leuven
References
Aboudi, R. and Thon, D. (1993). Expected utility and the Siegel paradox: a generalisation. Journal of Economics 57(1), 69–93.CrossRefGoogle Scholar
Aboudi, R. and Thon, D. (1995). Second degree stochastic dominance decisions and random initial wealth with applications to the economics of insurance. Journal of Risk and Insurance 62(1), 30–49.CrossRefGoogle Scholar
Barlow, R.E. and Proschan, F. (1975). Statistical Theory of Reliability and Life Testing. Holt, Rinehart and Winston. New York.Google Scholar
Cambanis, S.; Simons, G. and Stout, W. (1976). Inequalities for Ek(X, Y) when marginals are fixed. Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete 36, 285–294.CrossRefGoogle Scholar
Dhaene, J. and Goovaerts, M.J. (1995). On the dependency of risks in the individual life model. Research Report 9539, Departement Toegepaste Economische Wetenschappen, K.U. Leuven. Submitted.Google Scholar
Epstein, L. and Tanny, S. (1980). Increasing generalized correlation: a definition and some economic consequences. Canadian Journal of Economics 8(1), 16–34.CrossRefGoogle Scholar
Fréchet, M. (1951). Sur les tableaux de corrélation dont les marges sont données. Ann. Univ. Lyon Sect. A., Séries, 3, 14, 53–77.Google Scholar
Gerber, H.U. (1979). An introduction to mathematical risk theory. Huebner Foundation Monograph no. 8. Wharton School, Philadelphia.Google Scholar
Goovaerts, M.J.: Kaas, R.: Van Heerwaarden, A.E. and Bauwelinckx, T. (1990). Effective Actuarial Methods. Insurance Series vol. 3, North-Holland.Google Scholar
Heilmann, W.-R. (1986). On the impact of the independence of risks on stop-loss premiums. Insurance: Mathematics and Economics 5, 197–199.Google Scholar
Hoeffding, W. (1940). Masstabinvariante Korrelations-Theorie. Sehr. Math. Inst. Univ. Berlin, 5, 181–233.Google Scholar
Jewell, W.S. (1984). Approximating the distribution of a dynamic risk portfolio. Astin Bulletin 14(2), 135–148.CrossRefGoogle Scholar
Jodgeo, K. (1982). Dependence, concepts of. In: Johnson, N.L. and Kotz, S. (ed.). Encyclopedia of Statistical Sciences.Google Scholar
Lehman, E. (1966). Some concepts of dependence. Annals of Mathematical Statistics 37, 1137–1153.CrossRefGoogle Scholar
Levy, H.Andparoush, J. (1974). Toward multivariate efficiency criteria. Journal of Economic Theory 7, 129–142.CrossRefGoogle Scholar
Meilijson, I. and Nadas, A. (1979). Convex majorization with an application to the length of critical paths. Journal of Applied Probability 16, 671–677.CrossRefGoogle Scholar
Norberg, R. (1989). Actuarial analysis of dependent lives. Mitteilungen der Schweiz. Vereinigung der Versicherungsmathematiker 1989(2), 243–255.Google Scholar
Stoyan, D. (1983). Comparison Methods for Queues and other Stochastic Models, Wiley and Sons, New York.Google Scholar
Tchen, A. (1980). Inequalities for distributions with given marginals. Annals of Probability 8, 814–827.CrossRefGoogle Scholar
Tong, Y. (1980). Probability Inequalities in Multivariate Distributions. New York. Academic Press.Google Scholar
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