Sequential Bayesian Model Selection of Regular Vine Copulas
By Lutz F. Gruber, Claudia Czado. 2015
67 citations recorded by the source corpus.
Regular vine copulas can describe a wider array of dependency patterns than the multivariate Gaussian copula or the multivariate Student's t copula. This paper presents two contributions related to model selection of regular vine copulas. First, our pair copula family selection procedure extends existing Bayesian family selection methods by allowing pair families to be chosen from an arbitrary set of candidate families. Second, our method represents the first Bayesian model selection approach to include the regular vine density construction in its scope of inference. The merits of our approach are established in a simulation study that benchmarks against methods suggested in current literature. A real data example about forecasting of portfolio asset returns for risk measurement and investment allocation illustrates the viability and relevance of the proposed scheme.
Subfields: Finance, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d98afc6a154c88dd2ea52a54a1e7b15e162c3a11
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Citations in the corpus
- Pair-Copula Constructions for Financial Applications: A Review (2016, background): “Recently, Bayesian approaches for estimating the posterior distribution of the tree structure of a regular vine have been developed [28].”
- Copula variational inference (2015, unknown): “We set the step-size using ADAM [12].”