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Multivariate Variance Gamma and Gaussian dependence: a study with copulas

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Author Info
Elisa Luciano
Patrizia Semeraro

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Abstract

This paper explores the dynamic dependence properties of a Levy process, the Variance Gamma, which has non Gaussian marginal features and non Gaussian dependence. In a static context, such a non Gaussian dependence should be represented via copulas. Copulas, however, are not able to capture the dynamics of 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.

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Paper provided by Collegio Carlo Alberto in its series Carlo Alberto Notebooks with number 96.

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Length: 11 pages
Date of creation: 2008
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Handle: RePEc:cca:wpaper:96

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C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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