Abstract
This paper explores the dynamic dependence properties of a Lévy 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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Luciano, E., Semeraro, P. (2010). Multivariate Variance Gamma and Gaussian Dependence: a study with copulas. In: Corazza, M., Pizzi, C. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Milano. https://doi.org/10.1007/978-88-470-1481-7_20
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DOI: https://doi.org/10.1007/978-88-470-1481-7_20
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-1480-0
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