Pair-Copula Constructions of Multivariate Copulas

By Claudia Czado. 2010

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/af22d5d18d869af04580aace538acf7752bf614b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Finance

In Statistics and Probability

Citations in the corpus

Regime Switching Vine Copula Models for Global Equity and Volatility Indices (2016, methodology): “Markov-switchingR-vine copula models In this section we review the regime switching setup based on R-vine copulas from Stöber and Czado (2014), which will later on be used to model potential changes within the dependence structure of our index data set.”
Maximum likelihood estimation of mixed C-vines with application to exchange rates (2012, methodology): “Our presentation follows Aas et al. (2009) and Czado (2010), but the idea was first developed by Joe (1996) for cdf’s and organized graphically using densities and a sequence of nested trees by Bedford and Cooke (2002).”
Flexible Copula Density Estimation with Penalized Hierarchical B‐splines (2013, background): “Joe (1996) pursues the use of the so-called pair-copulas, where multiple interaction is reduced to bivariate copula modelling; see also Bedford & Cooke (2002) or Czado (2010).”
A New Approach to Assessing Model Risk in High Dimensions (2015, unknown): “Another natural criterion by which to determine the trusted part of the multivariate distribution consists in starting from a given fitted multivariate density f̂ (coming for instance from a multivariate Gaussian model, a multivariate Student model or a PairCopula Construction model (Aas, Czado, Fri”
On Families of Distributions with Shape Parameters (2015, unknown): “This is well reﬂected in the copula literature where high-dimensional direct copulas are becoming largely eschewed in favour of pair-copula constructions (e.g. Bedford & Cooke, 2002, Kurowicka & Cooke, 2006, Aas, Czado, Frigessi & Bakken, 2009, Czado, 2010).”
An Overview of the Goodness-of-Fit Test Problem for Copulas (2012, background): “Other authors have studied the large class of vines: D-vines, C-vines, regular vines more generally (see [1], [20], e.”
Truncated regular vines in high dimensions with application to financial data (2012, methodology): “Alternatively, one may use the stepwise/sequential MLestimator originally proposed by Aas, Czado, Frigessi, and Bakken (2009) and further explored by Hobæk Haff (2010). To determine the sequential parameter estimates of θtRV(K), it is enough to only use transformed variables up to tree K.”
Vine Copula Specifications for Stationary Multivariate Markov Chains (2015, methodology): “We commence with a very brief introduction to the essential features of vine copulae, drawing mostly on the pioneering articles of Bedford and Cooke (2001, 2002) as well as discussions by Kurowicka and Cooke (2006) and Czado (2010).”
Selecting and estimating regular vine copulae and application to financial returns (2012, methodology): “Czado (2010) provides a current survey about these statistical model classes and Joe et al. (2010) investigate and discuss tail dependence properties of vine distributions.”
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine (2013, methodology): “Further possible extensions are Bayesian inference and model selection techniques as used in Min and Czado (2010) and Min and Czado (2011).”
Selection of Vine Copulas (2013, background): “The first comprehensive account of vine copulas is found in [44], a recent survey in [20], and the current developments of this active research area in [45] .”
Nonparametric estimation of simplified vine copula models: comparison of methods (2017, background): “Simpli ed vine copulas, or pair-copula constructions (PCC), have become very popular over the last decade [1, 3, 4, 8, 37].”
Modeling and forecasting multivariate electricity price spikes (2016, background): “This also precludes the popular class of vine copulas to handle dependence dimensions larger than two; see Aas et al. (2009) or Czado (2010) for an introduction.”
Pair Copula Constructions for Multivariate Discrete Data (2012, methodology): “The sample size is 1000, and similar results were obtained for 30 replications of data generated from this model. the covariates used can be found in the online supplement and in Varin and Czado (2010).”