TAIL COMONOTONICITY AND CONSERVATIVE RISK MEASURES

被引:8
|
作者
Hua, Lei [1 ]
Joe, Harry [2 ]
机构
[1] No Illinois Univ, Div Stat, De Kalb, IL 60115 USA
[2] Univ British Columbia, Dept Stat, Vancouver, BC V6T 1Z4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Dependence modeling; copula; Archimedean copula; asymptotic full dependence; conditional tail expectation; Laplace transform; regular variation; ACTUARIAL SCIENCE; REGULAR VARIATION; MULTIVARIATE; DISTRIBUTIONS; COPULAS; FINANCE;
D O I
10.2143/AST42.2.2182810
中图分类号
F [经济];
学科分类号
02 ;
摘要
Tail comonotonicity, or asymptotic full dependence, is proposed as a reasonable conservative dependence structure for modeling dependent risks. Some sufficient conditions have been obtained to justify the conservativity of tail comonotonicity. Simulation studies also suggest that, by using tail comonotonicity, one does not lose too much accuracy but gain reasonable conservative risk measures, especially when considering high scenario risks. A copula model with tail comonotonicity is applied to an auto insurance dataset. Particular models for tail comonotonicity for loss data can be based on the BB2 and BB3 copula families and their multivariate extensions.
引用
收藏
页码:601 / 629
页数:29
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