Assessing High-Risk Scenarios by Full-Range Tail Dependence Copulas

North American Actuarial Journal

Volumn 18, Issue 3, 2014, Pages 363-378

  Hua, Lei ^a   Xia, Michelle ^a  

^a NORTHERN ILLINOIS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84906050799     PISSN: 10920277     EISSN: None     Source Type: Journal    
DOI: 10.1080/10920277.2014.888009     Document Type: Article

References (19)

1. Acar, E., R. Craiu, and F. Yao. 2011. Dependence Calibration in Conditional Copulas: A Nonparametric Approach. Biometrics 67: 445-453.
2. Bedford, T., and R.M. Cooke. 2002. Vines-a new Graphical Model for Dependent Random Variables. Annals of Statistics 30: 1031-1068.
3. Czado, C., R. Kastenmeier, E. Brechmann, and A. Min. 2012. A Mixed Copula Model for Insurance Claims and Claim Sizes. Scandinavian Actuarial Journal4: 278-305.
4. de Leon, A., and B. Wu. 2011. Copula-Based Regression Models for a Bivariate Mixed Discrete and Continuous Outcome. Statistics in Medicine 30: 175-185.
5. Donnelly, C., and P. Embrechts. 2010. The Devil is in the Tails: Actuarial Mathematics and the Subprime Mortgage Crisis. Astin Bulletin 40: 1-33.
6. Dunn, P.K., and G.K. Smyth. 1996. Randomized Quantile Residuals. Journal of Computational and Graphical Statistics 5: 236-244.
7. Elidan, G. 2013. Copulas in Machine Learning. In Copulae in Mathematical and Quantitative Finance, pp. 39-60. New York: Springer.
8. Frees, E.W., and E.A. Valdez. 1998. Understanding Relationships Using Copulas. North American Actuarial Journal 2: 1-25.
9. Hua, L. 2013. Tail Negative Dependence and its Applications for Aggregate Loss Modeling. Technical report.
10. Hua, L., and H. Joe. 2011. Tail Order and Intermediate Tail Dependence of Multivariate Copulas. Journal of Multivariate Analysis 102: 1454-1471.
11. Hua, L., and H. Joe. 2013. Intermediate Tail Dependence: A Review and Some New Results. In Stochastic Orders in Reliability and Risk: In Honor of Professor Moshe Shaked, edited by H. Li and X. Li, pp. 291-311. New York: Springer.
12. Hua, L., and H. Joe. 2014. Strength of Tail Dependence Based on Conditional Tail Expectation. Journal of Multivariate Analysis 123: 143-159.
13. Joe, H. 1997. Multivariate Models and Dependence Concepts. Monographs on Statistics and Applied Probability 73. London: Chapman & Hall.
14. Klugman, S.A., and R. Parsa. 1999. Fitting Bivariate Loss Distributions with Copulas. Insurance: Mathematics and Economics 24: 139-148.
15. Krupskii, P., and H. Joe. 2013. Factor Copula Models for Multivariate Data. Journal of Multivariate Analysis 120: 85-101.
16. Kurowicka, D., and H. Joe. 2011. Dependence Modeling: Vine Copula Handbook. Singapore: World Scientific.
17. Oh, D., and A. Patton. 2012. Modelling Dependence in High Dimensions with Factor Copulas. Technical report, Duke University.
18. Rigby, R.A., and D.M. Stasinopoulos. 2005. Generalized Additive Models for Location, Scale and Shape, (with discussion). Applied Statistics 54: 507-554.
19. Vuong, Q.H. 1989. Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses. Econometrica 307-333.