Multivariate Variance Gamma and Gaussian dependence: a study with copulas

被引：0
|
作者：
Luciano, Elisa [1 ]
Semeraro, Patrizia [2 ]
机构：
[1] Univ Turin, ICER, Turin, Italy
[2] Univ Turin, Dept Appl Math D De Castro, I-10124 Turin, Italy
来源：
MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE | 2010年
关键词：
multivariate variance Gamma; Levy process; copulas; non-linear dependence;
D O I：
暂无
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
This paper explores the dynamic dependence properties of a Levy process, the Variance Gamma, which has non-Gaussian marginal features and non-Gaussian dependence. By computing the distance between the Gaussian copula and the actual one, we show that even a non-Gaussian process, such as the Variance Gamma, can "converge" to linear dependence over time. Empirical versions of different dependence measures confirm the result over major stock indices data.
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页码：193 / +
页数：2
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