文章基本信息

标题：Bayesian Model Selection of Regular Vine Copulas
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作者：Lutz F. Gruber ; Claudia Czado
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2018
卷号：13
期号：4
页码：1111-1135
DOI：10.1214/17-BA1089
语种：English
出版社：International Society for Bayesian Analysis
摘要：Regular vine copulas are a flexible class of dependence models, but Bayesian methodology for model selection and inference is not yet fully developed. We propose sparsity-inducing but otherwise non-informative priors, and present novel proposals to enable reversible jump Markov chain Monte Carlo posterior simulation for Bayesian model selection and inference. Our method is the first to jointly estimate the posterior distribution of all trees of a regular vine copula. This represents a substantial improvement over existing frequentist and Bayesian strategies, which can only select one tree at a time and are known to induce bias. A simulation study demonstrates the feasibility of our strategy and shows that it combines superior selection and reduced computation time compared to Bayesian tree-by-tree selection. In a real data example, we forecast the daily expected tail loss of a portfolio of nine exchange-traded funds using a fully Bayesian multivariate dynamic model built around Bayesian regular vine copulas to illustrate our model’s viability for financial analysis and risk estimation.
关键词：multivariate analysis; dependence modeling; copula modeling; vine copulas; Bayesian inference; posterior simulation; importance sampling;simulation studies; financial analysis; risk forecasting.