Tail Dependence from a Distributional Point of View

By Alessandro Juri, Mario V. Wüthrich. 2003

89 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/7b47ec134db34f674efe27994951a609647fdf10

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Citations in the corpus

Asymptotics for Risk Capital Allocations Based on Conditional Tail Expectation (2011, unknown): “5) The work of Juri and Wüthrich (2003) justifies the above for 0 < β < ∞, while Lemma 4.”
Spatial contagion between financial markets: a copula-based approach (2010, methodology): “A recent and powerful tool for investigating the dependence structure in the tails of the joint df is to consider a conditional model and, then, to investigate the related copula, also called threshold copula or conditional copula [23, 24 , 25, 26, 27, 28, 29, 30].”
Limiting dependence structures for tail events, with applications to credit derivatives (2006, background): “For α = β = 1, Theorem 3.1 generalizes Theorem 2.4 of Juri and Wüthrich (2004), which states that lim u→0 (C, u, u)(x, y) = G(g −1(x), g−1(y)), (3.5) where g : [0, ∞) → [0, ∞) is the strictly increasing, continuous function defined by g(x) := limu→0 C(xu, u)/C(u, u), G(x, y) = yθg(x/y) for (x, y) ∈…”
Tail asymptotics for the sum of two heavy-tailed dependent risks (2006, background): “For Archimedean copulae this result could be exploited in Wüthrich [32] and Alink et al. [2] to give sharp asymptotics of the tail of X1 +X2, see Section 3.3.”
Diversification of aggregate dependent risks (2004, background): “EmbrechtsMcNeil-Straumann [9], Frees-Valdez [11], Juri-Wüthrich [14]) that simple measures of dependence such as the correlation coefficient are insufficient to cover the full range of possible consequences of dependent events.”
Lower tail dependence for Archimedean copulas: Characterizations and pitfalls (2007, unknown): “Moreover, the deﬁnition can be extended by allowing the thresholds for the two margins to be diﬀerent, that is, by conditioning on the event { U ≤ u, V ≤ v } , where ( u, v ) ∈ (0 , 1] 2 are such that C ( u, v ) > 0, see Charpentier and Juri (2004, Deﬁnition 2.5).”
Asymptotic Value-at-Risk Estimates for Sums of Dependent Random Variables (2003, background): “This comes from the fact that the Clayton copula is invariant under certain transformations and plays the role of an attracting copula (see [9]).”
The t Copula and Related Copulas (2005, background): “An elegant general result follows directly from a theorem in Juri & Wuethrich (2002); this shows how to find tail limit copulas for any bivariate copula that is attracted to an EV limiting copula.”
Copulas: A Review and Recent Developments (2006, background): “Juri and Wüthrich[70] characterize the limiting dependence structure in the upper-tails of two random variables assuming their dependence structure is Archimedean.”