Truncated regular vines in high dimensions with application to financial data

By Eike C. Brechmann, Claudia Czado, Kjersti Aas. 2012

295 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/60f22637579d34d50ee3ef1fb6d2d29a269d5c60

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Citations in the corpus

Sequential Bayesian Model Selection of Regular Vine Copulas (2015, background): “A regular vine copula is said to be truncated at level K if all pair copulas conditional on K or more variables are set to bivariate independence copulas (Brechmann et al. (2012)).”
Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas (2018, unknown): “We follow the sequential method proposed in [28], [31], which deals with the model selection tree by tree.”
Pair-Copula Constructions for Financial Applications: A Review (2016, methodology): “Since then, these constructions have been used for equities [48,53,61–63], interest rates [48,64,65], exchange rates [61,66–69], electricity prices and other commodities [61,70–74] and housing prices [75].”
Selecting and estimating regular vine copulae and application to financial returns (2012, methodology): “See Brechmann et al. (2012) for details.”
Copula based hierarchical risk aggregation through sample reordering (2012, background): “However, the numerical complexity of simulation increases exponentially in the dimension, see Brechmann et al. (2010).”
Flexible dependence modeling of operational risk losses and its impact on total capital requirements (2014, background): “simplification could be achieved by even choosing independence copulas for pairs that are conditioned on a certain number of other variables as discussed by Brechmann et al. (2012). Note that if a vine copula is specified completely in terms of Gaussian copulas, it is in fact equivalent to a Gaussia”
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine (2013, methodology): “Inference and model selection of these are treated in Dißmann, Brechmann, Czado, and Kurowicka (2013) and Brechmann, Czado, and Aas (2012), while a large scale ﬁnancial application can be found in Brechmann and Czado (2012).”
Copulas in Machine Learning (2013, unknown): “, [5]) somewhat pushing this boundary.”
Modelling skewed spatial random fields through the spatial vine copula (2014, background): “Besides including spatial trees only up to a certain level, one could as well truncate the upper vine copula at a certain level using a truncated vine (Brechmann et al., 2012).”
Nonparametric estimation of simplified vine copula models: comparison of methods (2017, background): “M E ] 5 A 2010, Kurowicka and Joe, 2011, Brechmann et al., 2012, Spanhel and Kurz, 2015).”
Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50 (2013, methodology): “Modeling using a multivariate Gaussian copula as in the CVMS model is not possible here due to the more general underlying R-vine structure (see Brechmann et al. (2012)).”
Selection strategies for regular vine copulae (2013, methodology): “These tests have been applied in the context of truncating R-vine based models in [Brechmann et al., 2012].”
Pair Copula Constructions for Multivariate Discrete Data (2012, methodology): “This model selection strategy has been shown to be an effective “quick-and-dirty” algorithm in the continuous-data case by Brechmann, Czado, and Aas (2012).”