Model selection in sparse high-dimensional vine copula models with an application to portfolio risk

DOI10.1016/j.jmva.2019.03.004zbMath1419.62126arXiv1801.09739OpenAlexW2962860740WikidataQ128128747 ScholiaQ128128747MaRDI QIDQ2001097

C. Bumann, Claudia Czado, Thomas Nagler

Publication date: 2 July 2019

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1801.09739

zbMATH Keywords

model selectionvalue-at-riskBICsparsityvine copula

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items

Optimizing effective numbers of tests by vine copula modeling, Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation, On the quantification and efficient propagation of imprecise probabilities with copula dependence, Bayesian ridge regression for survival data based on a vine copula-based prior, Editorial for the special issue on dependence models, Prediction based on conditional distributions of vine copulas, Copula-based Black-Litterman portfolio optimization

Uses Software

• RMetrics
• rvinecopulib
• CopulaModel

Cites Work

• Unnamed Item
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