Dependence modeling with copulas

By Paul Embrechts, Johana Nešlehová. 2007

1,199 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dc742bc3b9588879c3ece54be14ce22490413c3f

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Citations in the corpus

Graphical models for extremes (2018, background): “The tail chains induce multivariate Pareto distributions that can readily be seen to factorize with respect to a chain; see Smith (1992), Basrak and Segers (2009) and Janssen and Segers (2014).”
Multivariate spearman's ρ for aggregating ranks using copulas (2016, methodology): “The expert reader may skip directly to section 3 to see the definition of multivariate Spearman’s ρ in terms of the multivariate copula.”
Analyzing Dependent Data with Vine Copulas (2019, neutral): “• Joe, H. (2014). Dependence modeling with copulas.”
Nonparametric estimation of simplified vine copula models: comparison of methods (2017, background): “Special classes are the elliptical copulas (including the Gaussian and Student t families), and the Archimedean class (including the Clayton, Frank and Gumbel families); for more see [20].”
Maximum likelihood estimation of skew-t copulas with its applications to stock returns (2018, methodology): “In terms of simple tail asymmetric copulas with vines, the BB1 copula of Joe [7,8] is used in [3].”
Modeling Spatial Processes with Unknown Extremal Dependence Class (2017, methodology): “This two-step approach is common practice in the copula literature and provides consistent inference for the copula under mild regularity conditions (see, e.g., Joe 2015).”
Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, neutral): “For the bivariate case, there are numerous copulas proposed in the literature, and their choice depends on the dependence structure of the data – see Joe (2014) and Schepsmeier et al. (2018) for details.”
The shifting dependence dynamics between the G7 stock markets (2018, neutral): “…h-function in equation (6), which helps recursively compute the likelihood function corresponding to a copula vine. h ( u|v; θu,v ) = ∂Cu,v (u, v; θu,v) ∂v (6) Joe (2015) argues that vines have many desirable properties, such as the inclusion of independence and co-monotonicity, flexible and…”
Factor Copula Models for Replicated Spatial Data (2015, methodology): “Alternatively, one may use vine copula models, the joint distribution of which is constructed using bivariate, conditional linking copulas, including models with general structures, R-vines, and special cases such as C-vines; see Kurowicka and Cooke (2006) and Aas et al.”
Count Time Series: A Methodological Review (2021, neutral): “7) and (Joe 2014, sec.”
A multivariate copula‐based framework for dealing with hazard scenarios and failure probabilities (2016, methodology): “…F i512Fi is the survival function of Xi, with i51; . . . ; d. Note that Ĉ can be written in terms of the copula C by using the Inclusion-Exclusion principle [see Joe, 2014, p. 27], since ĈðuÞ5Cð12uÞ for all u 2 Id , and C is the survival function associated with C given by CðuÞ512 Xd i51 ui1 X…”