Selecting and estimating regular vine copulae and application to financial returns

By Jeffrey F. Dißmann, Eike C. Brechmann, Claudia Czado, Dorota Kurowicka. Computational Statistics & Data Analysis, 2012

757 citations recorded by the source corpus.

Regular vine distributions which constitute a flexible class of multivariate dependence models are discussed. Since multivariate copulae constructed through pair-copula decompositions were introduced to the statistical community, interest in these models has been growing steadily and they are finding successful applications in various fields. Research so far has however been concentrating on so-called canonical and D-vine copulae, which are more restrictive cases of regular vine copulae. It is shown how to evaluate the density of arbitrary regular vine specifications. This opens the vine copula methodology to the flexible modeling of complex dependencies even in larger dimensions. In this regard, a new automated model selection and estimation technique based on graph theoretical considerations is presented. This comprehensive search strategy is evaluated in a large simulation study and applied to a 16-dimensional financial data set of international equity, fixed income and commodity indices which were observed over the last decade, in particular during the recent financial crisis. The analysis provides economically well interpretable results and interesting insights into the dependence structure among these indices.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f25947507099eabe225cd10f10a50e7395120328

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Citations in the corpus

Nonparametric estimation of simplified vine copula models: comparison of methods (2017, methodology): “We also describe a data-driven structure selection algorithm that was initially proposed in [10].”
Selection of Vine Copulas (2013, methodology): “• the absolute empirical Kendall’s τ as proposed by [23] and [22]; • the AIC of each pair copula corresponding to the discussion in Section 4.”
Flexible dependence modeling of operational risk losses and its impact on total capital requirements (2014, background): “Examples of heavy-tailed (or subexponential) distributions are in particular the log normal, Pareto, Weibull (with shape parameter smaller than 1) and generalized Pareto, while the also commonly used gamma distribution is light-tailed (see Embrechts et al. [1997]).”
From Dependence to Causation (2016, methodology): “One common alternative is to use the greedy algorithm of Dissmann et al. (2013).”
Pair-Copula Constructions for Financial Applications: A Review (2016, methodology): “One such approach was proposed by Morales-Napoles [11] and explored in more detail in [12].”
Sequential Bayesian Model Selection of Regular Vine Copulas (2015, methodology): “Nevertheless, our proposed selection scheme consistently selects better-fitting models than existing selection strategies and is better at detecting sparsity patterns for model reduction than Dißmann et al. (2013)’s frequentist method.”
Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas (2018, methodology): “We follow the sequential method proposed in [28], [31], which deals with the model selection tree by tree.”