Publication:
Optimal reinsurance under risk and uncertainty

Research Projects
Organizational Units
Journal Issue
Abstract
This paper deals with the optimal reinsurance problem if both insurer and reinsurer are facing risk and uncertainty, though the classical uncertainty free case is also included. The insurer and reinsurer degrees of uncertainty do not have to be identical. The decision variable is not the retained (or ceded) risk, but its sensitivity with respect to the total claims. Thus, if one imposes strictly positive lower bounds for this variable, the reinsurer moral hazard is totally eliminated. Three main contributions seem to be reached. Firstly, necessary and sufficient optimality conditions are given in a very general setting. Secondly, the optimal contract is often a bang–bang solution, i.e., the sensitivity between the retained risk and the total claims saturates the imposed constraints. Thirdly, the optimal reinsurance problem is equivalent to other linear programming problem, despite the fact that risk, uncertainty, and many premium principles are not linear. This may be important because linear problems may be easily solved in practice, since there are very efficient algorithms.
Description
Publicado también como artículo: Balbás, A.; Balbás, B.; Balbás, R.; Heras, A. (2015) "Optimal reinsurance under risk and uncertainty" Insurance: Mathematics and Economics, vol. 60, pages 61-74. ISSN: 01676687 http://dx.doi.org/10.1016/j.insmatheco.2014.11.001
UCM subjects
Unesco subjects
Keywords
Citation
Anderson, E.J. and P. Nash, 1987. Linear programming in inÖnite-dimensional spaces. JohnWiley & Sons Arrow, K. J., 1963. Uncertainty and the welfare of medical care. American Economic Review, 53, 941-973. Artzner, P., F. Delbaen, J.M. Eber and D. Heath, 1999. Coherent measures of risk. Mathematical Finance, 9, 203-228 Balb·s, A., B. Balb·s and R. Balb·s, 2013. Good deals in markets with friction. Quantitative Finance, 13, 827ñ836. Balb·s, A., B. Balb·s and A. Heras, 2009. Optimal reinsurance with general risk measures. Insurance: Mathematics and Economics, 44, 374-384. Balb·s, A., B. Balb·s and A. Heras, 2011. Stable solutions for optimal reinsurance problems involving risk measures. European Journal of Operational Research, 214, 796-804. Borch, K. 1960. An attempt to determine the optimum amount of stop loss reinsurance. Transactions of the 16th International Congress of Actuaries I, 597-610. Bossaerts, P., P. Ghirardato, S. Guarnaschelli and W.R. Zame, 2010. Ambiguity in asset markets: Theory and experiment. Review of Financial Studies, 23, 1325-1359. Cai, J., Y Fang, Z. Li and G.E. Willmot, 2012. Optimal reciprocal reinsurance treaties under the joint survival probability and the joint proÖtable probability. Journal of Risk and Insurance, 80, 145-168. Cai, J. and K.S. Tan, 2007. Optimal retention for a stop loss reinsurance under the V aR and CTE risk measures. ASTIN Bulletin, 37, 1, 93-112. Cui, W., J. Yang and L. Wu, 2013. Optimal reinsurance minimizing the distortion risk measure under general reinsurance premium principles. Insurance: Mathematics and Economics, 53, 74-85. Centeno, M.L. and O. Simoes, 2009. Optimal reinsurance. Revista de la Real Academia de Ciencias, RACSAM, 103, 2, 387-405. Chi, Y. and K.S. Tan, 2013. Optimal reinsurance with general premium principles. Insurance: Mathematics and Economics, 52, 180-189. DieudonnÈ, J., 1988. Foundations of modern analysis. Academic Press. Durrett, R., 2010. Probability: Theory and examples. FourthEdition.Cambridge University Press. Gilboa, I. and D. Schmeidler, 1989. Maxmin expected utility with non-unique prior. Journal of Mathematical Economics, 18, 141 - 153. Kaluszka, M., 2005. Optimal reinsurance under convex principles of premium calculation. Insurance: Mathematics and Economics, 36, 375-398. Kelly, J., 1975. General topology. Graduate Texts in Mathematics. Springer. Luenberger, D.G.,1969. Optimization by vector spaces methods. John Wiley & Sons. Maccheroni, F., M. Marinacci and A. Rustichini, 2006. Ambiguity aversion, robustness, and the variational representation of preferences. Econometrica, 74, 1447-1498. Nakayama H., Y. Sawaragi and T. Tanino, 1985. Theory of multiobjective optimization. Academic Press. 0gryczak, W. and A. Ruszczynski, 2002. Dual stochastic dominance and related mean risk models. SIAM Journal on Optimization, 13, 60-78. Rockafellar, R.T., S. Uryasev and M. Zabarankin, 2006. Generalized deviations in risk analysis. Finance & Stochastics, 10, 51-74. Riedel, F., 2009. Optimal stopping with multiple priors. Econometrica, 77, 857-908. Rudin, W., 1973. Functional analysis. McGraw-Hill, Inc. Rudin, W., 1987. Real and complex analysis. Third Edition. McGraw-Hill, Inc. Zhu, S. and M. Fukushima, 2009. Worst case conditional value at risk with applications to robust portfolio management. Operations Research, 57, 5 1155-1168.