2009
DOI: 10.1080/02664760802520785
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Archimedean copulae for risk measurement

Abstract: In this paper some Archimedean copula functions for bivariate financial returns are studied. The choice of\ud this family is due to their ability to capture the tail dependence, which is an association measurewecan detect\ud in many bivariate financial time-series.Atime-varying version of these copulae is also investigated. Finally,\ud the Value-at-Risk is computed and its performance is compared across different copula specifications

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Cited by 8 publications
(11 citation statements)
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“…Considering the dependence structure between the stock returns via copula [17,26,34] is another interesting research topic. A first approach to develop NPI in order to take such dependence into account has recently been published [13] and research on this topic is ongoing.…”
Section: Discussionmentioning
confidence: 99%
“…Considering the dependence structure between the stock returns via copula [17,26,34] is another interesting research topic. A first approach to develop NPI in order to take such dependence into account has recently been published [13] and research on this topic is ongoing.…”
Section: Discussionmentioning
confidence: 99%
“…The non-parametric bivariate coefficient of lower tail dependence, λ L NP , can be obtained as (De Luca and Rivieccio, 2009) λ L NP (k) = P(X 2 ≤ x 2 * |X 1 ≤ x 1 * ), or conversely, where x i * is assumed to be µ i − kσ i . This statistic depends on k.…”
Section: Non Parametric Tail Dependence Measuresmentioning
confidence: 99%
“…A multivariate generalization of the tail dependence coefficients (De Luca and Rivieccio, 2009) consists in to consider h variables and the conditional probability associated to the remaining n -h var iables, given, respectively, by It is simple to show that Ĉ is strictly related to the copula function through the following relationship…”
Section: Non Parametric Tail Dependence Measuresmentioning
confidence: 99%
“…However, intraday time series of returns for each asset are considered here, so a different marginal model class is specified. We choose copula methods to account for multivariate dependence because of their versatility in reproducing nonlinear dependence: see for example Cherubini et al (2004), De Luca andRivieccio (2009), and Kurowicka and Joe (2011). As far as we know, this is the first study using MCGARCH and D-vine copula models to forecast IVaR.…”
Section: Introductionmentioning
confidence: 99%
“…As far as we know, this is the first study using MCGARCH and D-vine copula models to forecast IVaR. We choose copula methods to account for multivariate dependence because of their versatility in reproducing nonlinear dependence: see for example Cherubini et al (2004), De Luca andRivieccio (2009), and Kurowicka and Joe (2011).…”
Section: Introductionmentioning
confidence: 99%