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PROPERTIES OF SECOND-ORDER REGULAR VARIATION AND EXPANSIONS FOR RISK CONCENTRATION

Published online by Cambridge University Press:  30 July 2012

Wenhua Lv
Affiliation:
Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026People's Republic of China E-mails: [email protected]; [email protected]; [email protected]
Tiantian Mao
Affiliation:
Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026People's Republic of China E-mails: [email protected]; [email protected]; [email protected]
Taizhong Hu
Affiliation:
Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026People's Republic of China E-mails: [email protected]; [email protected]; [email protected]

Abstract

The purpose of this study is two-fold. First, we investigate further properties of the second-order regular variation (2RV). These properties include the preservation properties of 2RV under the composition operation and the generalized inverse transform, among others. Second, we derive second-order expansions of the tail probabilities of convolutions of non-independent and identically distributed (i.i.d.) heavy-tail random variables, and establish second-order expansions of risk concentration under mild assumptions. The main results extend some ones in the literature from the i.i.d. case to non-i.i.d. case.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2012

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References

1.Barbe, P. & McCormick, W.P. (2005). Asymptotic expansions of convolutions of regularly varying distributions. Journal of the Australian Mathematical Society 78: 339371.CrossRefGoogle Scholar
2.Bingham, N.H., Goldie, C.M., & Teugels, J.L. (1987). Regular variation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
3.Chen, D., Mao, T., & Hu, T. (2012). Asymptotic behavior of extremal events for aggregate dependent random variables. Submitted.Google Scholar
4.Degen, M. & Embrechts, P. (2008). EVT-based estimation of risk capital and convergence of high quantiles. Advances in Applied Probability 40: 696715.CrossRefGoogle Scholar
5.Degen, M., Lambrigger, D.D., & Segers, J. (2010). Risk concentration and diversification: Second-order properties. Insurance: Mathematics and Economics 46: 541546.Google Scholar
6.de Haan, L. & Ferreira, A. (2006). Extreme value theory: An introduction. Springer Series in Operations Research and Financial Engineering. New York: Springer.CrossRefGoogle Scholar
7.de Haan, L. & Resnick, S.I. (1996). Second order regular variation and rates of convergence in extreme value theory. Annals of Probability 24: 119124.Google Scholar
8.de Haan, L. & Stadtmüller, U. (1996). Generalized regular variation of second order. Journal of the Australian Mathematical Society 61: 381395.CrossRefGoogle Scholar
9.Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling extremal events for finance and insurance. Berlin: Springer-Verlag.CrossRefGoogle Scholar
10.Feller, W. (1971). An introduction to probability theory and its applications: II. New York: Wiley.Google Scholar
11.Geluk, J., de Haan, L., Resnick, S., & Stǎricǎ, C. (1997). Second-order regular variation, convolution and the central limit theorem. Stochastic Processes and Their Applications 69: 139159.CrossRefGoogle Scholar
12.Hua, L. & Joe, H. (2011). Second order regular variation and conditional tail expectation of multiple risks. Insurance: Mathematics and Economics 49: 537546.Google Scholar
13.Lin, F.M, Peng, Z.X., & Nadarajah, S. (2008). Second order regular variation and its applications to rates of convergence in extreme-value distribution. Bulletin of the Korean Mathematical Society 45: 7593.CrossRefGoogle Scholar
14.Resnick, S.I. (2007). Heavy-tail phenomena. Springer Series in Operations Research and Financial Engineering. New York: Springer.Google Scholar