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A parsimonious multivariate copula for tail dependence modeling
Abstract : Copulas are increasingly studied both in theory and practice as they are a convenient tool to construct multivariate distribution functions. However the material essentially covers the bi-variate case while in applications the number of variables is much higher. Furthermore, when one wants to take into account tail dependence, a desirable property is to have enough flexibility in the tails while avoiding the exponential growth of the number of parameters. We propose in this communication a one-factor model which exhibits this feature.
Cited literature [6 references]
https://hal.archives-ouvertes.fr/hal-00863540
Contributor : Stephane Girard <>
Submitted on : Thursday, September 19, 2013 - 10:36:54 AM
Last modification on : Tuesday, November 24, 2020 - 4:38:03 PM
Long-term archiving on: : Friday, December 20, 2013 - 3:06:34 PM
Contributor : Stephane Girard <>
Submitted on : Thursday, September 19, 2013 - 10:36:54 AM
Last modification on : Tuesday, November 24, 2020 - 4:38:03 PM
Long-term archiving on: : Friday, December 20, 2013 - 3:06:34 PM
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EVT2013presentername.pdf
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