Abstract
A novel generating mechanism for non-strict bivariate Archimedean copulas via the Lorenz curve of a non-negative random variable is proposed. Lorenz curves have been extensively studied in economics and statistics to characterize wealth inequality and tail risk. In this paper, these curves are seen as integral transforms generating increasing convex functions in the unit square. Many of the properties of these “Lorenz copulas”, from tail dependence and stochastic ordering, to their Kendall distribution function and the size of the singular part, depend on simple features of the random variable associated to the generating Lorenz curve. For instance, by selecting random variables with a lower bound at zero it is possible to create copulas with asymptotic upper tail dependence. An “alchemy” of Lorenz curves that can be used as general framework to build multiparametric families of copulas is also discussed.
References
[1] Abramowitz, M. and I. A. Stegun (1965). Handbook of Mathematical Functions. Dover Publications, New York.Search in Google Scholar
[2] Alsina, C., M. J. Frank, and B. Schweizer (2006). Associative Functions. World Scientific Publishing, Singapore.Search in Google Scholar
[3] Arnold, B. C. and J. M. Sarabia (2018). Majorization and the Lorenz Order with Applications in Applied Mathematics and Economics. Springer, Cham.10.1007/978-3-319-93773-1Search in Google Scholar
[4] Atkinson, A. B. (1970). On the measurement of inequality. J. Econ. Theory 2(3), 244–263.10.1016/0022-0531(70)90039-6Search in Google Scholar
[5] Avérous, J. and J.-L. Dortet-Bernadet (2004). Dependence for Archimedean copulas and aging properties of their generating functions. Sankhyā 66(4), 607–620.Search in Google Scholar
[6] Balbás, A., J. Garrido, and S. Mayoral (2008). Properties of distortion risk measures. Methodol. Comput. Appl. Probab. 11(3), 385–399.10.1007/s11009-008-9089-zSearch in Google Scholar
[7] Capéraà, P., A.-L. Fougères, and C. Genest (1997). A stochastic ordering based on a decomposition of Kendall’s tau. In V. Beneš and J. Štěpán (Eds.), Distributions with Given Marginals and Moment Problems, pp. 81–86. Springer, Dordrecht.10.1007/978-94-011-5532-8_9Search in Google Scholar
[8] Charpentier, A. and J. Segers (2008). Convergence of Archimedean copulas. Statist. Probab. Lett. 78(4), 412–419.10.1016/j.spl.2007.07.014Search in Google Scholar
[9] Charpentier, A. and J. Segers (2009). Tails of multivariate Archimedean copulas. J. Multivariate Anal. 100(7), 1521–1537.10.1016/j.jmva.2008.12.015Search in Google Scholar
[10] Chotikapanich, D. (2008). Modeling Income Distributions and Lorenz Curves. Springer, New York.10.1007/978-0-387-72796-7Search in Google Scholar
[11] Di Bernardino, E. and D. Rullière (2017). A note on upper-patched generators for Archimedean copulas. ESAIM Probab. Stat. 21, 183–200.10.1051/ps/2017003Search in Google Scholar
[12] Eliazar, I. (2018). A tour of inequality. Ann. Physics 389, 306–332.10.1016/j.aop.2017.12.010Search in Google Scholar
[13] Eliazar, I. and M. H. Cohen (2014). Hierarchical socioeconomic fractality: The rich, the poor, and the middle-class. Phys. A 402, 30–40.10.1016/j.physa.2014.01.059Search in Google Scholar
[14] Feller, W. (1971). An Introduction to Probability Theory and its Applications. Second edition. John Wiley & Sons, New York.Search in Google Scholar
[15] Fontanari, A., P. Cirillo, and C. W. Oosterlee (2018). From concentration profiles to concentration maps. New tools for the study of loss distributions. Insurance Math. Econ. 78, 13–29.10.1016/j.insmatheco.2017.11.003Search in Google Scholar
[16] Fontanari, A., I. Eliazar, P. Cirillo, and C. W. Oosterlee (2020). Portfolio risk and quantum majorization of correlation matrices. IMA J. Manag. Math., to appear. Available at https://doi.org/10.1093/imaman/dpaa011.10.1093/imaman/dpaa011Search in Google Scholar
[17] Gastwirth, J. L. (1971). A general definition of the Lorenz curve. Econometrica 39(6), 1037–1039.10.2307/1909675Search in Google Scholar
[18] Genest, C. and K. Ghoudi (1994). Une famille de lois bidimensionnelles insolite. C. R. Math. Acad. Sci. Paris 318(4), 351–354.Search in Google Scholar
[19] Genest, C., K. Ghoudi, and L.-P. Rivest (1998). Understanding relationships using copulas. N. Am. Actuar. J. 2(3), 143–149.10.1080/10920277.1998.10595749Search in Google Scholar
[20] Genest, C. and L.-P. Rivest (1993). Statistical inference procedures for bivariate Archimedean copulas. J. Amer. Statist. Assoc. 88(423), 1034–1043.10.1080/01621459.1993.10476372Search in Google Scholar
[21] Gini, C. (1921). Measurement of inequality of incomes. Econ. J. 31(121), 124–126.10.2307/2223319Search in Google Scholar
[22] Grossman, M. and R. Katz (1972). Non-Newtonian Calculus. Lee Press, Pigeon Cove MA.Search in Google Scholar
[23] Gupta, R. D. and D. Kundu (1999). Generalized exponential distributions. Aust. N. Z. J. Stat. 41(2), 173–188.10.1111/1467-842X.00072Search in Google Scholar
[24] Johnson, W. P. (2002). The curious history of Faà di Bruno’s formula. Amer. Math. Monthly 109(3), 217–234.Search in Google Scholar
[25] Kleiber, C. and S. Kotz (2003). Statistical Size Distributions in Economics and Actuarial Sciences. John Wiley & Sons, Hoboken NJ.10.1002/0471457175Search in Google Scholar
[26] König, S., H. Kazianka, J. Pilz, and J. Temme (2015). Estimation of nonstrict Archimedean copulas and its application to quantum networks. Appl. Stoch. Models Bus. Ind. 31(4), 464–482.10.1002/asmb.2039Search in Google Scholar
[27] Koshevoy, G. and K. Mosler (1996). The Lorenz zonoid of a multivariate distribution. J. Amer. Statist. Assoc. 91(434), 873– 882.10.1080/01621459.1996.10476955Search in Google Scholar
[28] Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publ. Amer. Statist. Assoc. 9(70), 209–219.10.2307/2276207Search in Google Scholar
[29] McNeil, A. J., R. Frey, and P. Embrechts (2015). Quantitative Risk Management: Concepts, Techniques and Tools. Revised edition. Princeton University Press.Search in Google Scholar
[30] McNeil, A. J. and J. Nešlehová (2009). Multivariate Archimedean copulas, d-monotone functions and l1-norm symmetric distributions. Ann. Statist. 37(5B), 3059–3097.Search in Google Scholar
[31] Meginniss, J. (1980). Non-Newtonian calculus applied to probability, utility and Bayesian analysis. In American Statistical Association: Proceedings of the Business and Economic Statistics Section, pp. 405–414.Search in Google Scholar
[32] Mehran, F. (1976). Linear measures of income inequality. Econometrica 44(4), 805–809.10.2307/1913446Search in Google Scholar
[33] Mikosch, T. (2006). Copulas: Tales and facts. Extremes 9(1), 3–20.10.1007/s10687-006-0015-xSearch in Google Scholar
[34] Muliere, P. and M. Scarsini (1989). A note on stochastic dominance and inequality measures. J. Econom. Theory 49(2), 314–323.10.1016/0022-0531(89)90084-7Search in Google Scholar
[35] Nelsen, R. B. (2006). An Introduction to Copulas. Second edition. Springer, New York.Search in Google Scholar
[36] Nelsen, R. B., J. J. Quesada-Molina, J. A. Rodriguez-Lallena, and M. Ubeda-Flores (2003). Kendall distribution functions. Statist. Probab. Lett. 65(3), 263–268.10.1016/j.spl.2003.08.002Search in Google Scholar
[37] Nelsen, R. B., J. J. Quesada-Molina, J. A. Rodriguez-Lallena, and M. Ubeda-Flores (2009). Kendall distribution functions and associative copulas. Fuzzy Sets and Systems 160(1), 52–57.10.1016/j.fss.2008.05.001Search in Google Scholar
[38] Niculescu, C. P. and L.-E. Persson (2006). Convex Functions and their Applications. Springer, New York.10.1007/0-387-31077-0Search in Google Scholar
[39] Pap, E. (2001). Pseudo-analysis. IFAC Proc. Vol. 34(13), 743–748.10.1016/S1474-6670(17)39082-1Search in Google Scholar
[40] Patel, J. K. and C. B. Read (1996). Handbook of the Normal Distribution. CRC Press, Boca Raton FL.Search in Google Scholar
[41] Sarabia, J. M. (2008). Parametric Lorenz curves: Models and applications. In D. Chotikapanich (Ed.), Modeling Income Distributions and Lorenz Curves, pp. 167–190. Springer, New York.10.1007/978-0-387-72796-7_9Search in Google Scholar
[42] Sarabia, J. M., E. Castillo, and D. J. Slottje (1999). An ordered family of Lorenz curves. J. Econometrics 91(1), 43–60.10.1016/S0304-4076(98)00048-7Search in Google Scholar
[43] Scapparone, P. (1996). Lezioni di Economia Matematica. CLUEB, Bologna.Search in Google Scholar
[44] Shaked, M. and J. G. Shanthikumar (2007). Stochastic Orders. Springer, New York.10.1007/978-0-387-34675-5Search in Google Scholar
[45] Shalit, H. and S. Yitzhaki (1984). Mean-Gini, portfolio theory, and the pricing of risky assets. J. Finance 39(5), 1449–1468.10.1111/j.1540-6261.1984.tb04917.xSearch in Google Scholar
[46] Shalit, H. and S. Yitzhaki (2005). The mean-Gini efficient portfolio frontier. J. Financ. Res. 28(1), 59–75.10.1111/j.1475-6803.2005.00114.xSearch in Google Scholar
[47] Singh, S. K. and G. S. Maddala (2008). A function for size distribution of incomes. In D. Chotikapanich (Ed.), Modeling Income Distributions and Lorenz Curves, pp. 27–35. Springer, New York.10.1007/978-0-387-72796-7_2Search in Google Scholar
[48] Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8, 229–231.Search in Google Scholar
[49] Úbeda-Flores, M., E. de Amo Artero, F. Durante, and J. Fernández-Sánchez (2017). Copulas and Dependence Models with Applications. Springer, Cham.10.1007/978-3-319-64221-5Search in Google Scholar
[50] Wang, S. S. (2000). A class of distortion operators for pricing financial and insurance risks. J. Risk Insurance 67(1), 15–36.10.2307/253675Search in Google Scholar
[51] Yitzhaki, S. and E. Schechtman (2013). The Gini Methodology: A Primer on a Statistical Methodology. Springer, New York.Search in Google Scholar
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