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Multivariate Variance Gamma and Gaussian dependence: a study with copulas

By Elisa Luciano and Patrizia Semeraro

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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  1. (2005). A multivariate Jump-Driven Financial Asset Model.
  2. (2006). A Multivariate Variance Vamma Model for Financial Application.
  3. An introduction to copulas.
  4. (2007). Extending Time-Changed L´ evy Asset Models Through Multivariate Subordinators, working paper, Collegio Carlo Alberto.
  5. (2004). Financial modelling with jump processes. Chapman and hall-CRC financial mathematics series.
  6. (1959). Fonctions de repartition ` a n dimensions et leurs marges,
  7. (2003). L´ evy processes and Infinitely divisible distributions Cambridge studies in advanced mathematics Cambridge
  8. (1990). The v.g. model for share market returns.Journal of

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