Online Program Home
My Program

Abstract Details

Activity Number: 367
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319457 View Presentation
Title: Bayesian Model Selection for Hierarchical Copulas and Vines
Author(s): Arkady Shemyakin* and Alexander Kniazev and Oleg Lepekhin
Companies: University of St. Thomas and Astrakhan State University and Astrakhan State University
Keywords: Bayesian model selection ; Archimedean copulas ; vine copulas ; Metropolis algorithm ; empirical Bayes ; stock indices

Copula models provide an effective tool for modeling joint distributions. Model selection allowing to choose an appropriate subclass of copulas remains a critical issue for many applications. The paper suggests an implementation of Bayesian model selection procedure based on ideas of Bretthorst, Huard et al. It allows us to compare several classes of Archimedean copulas (Frank's, Clayton's, and survival Gumbel-Hougaard families) and elliptical copulas (Gaussian and Student t-copulas). For dimensions higher than 2 we consider several types of hierarchical structures including nested Archimedean copulas, hierarchical Kendall copulas and vines. We consider a portfolio based on four national indices. Extreme market co-movements are modeled by the tail behavior of the joint distribution or index returns and currency exchange rates. Estimation of parameters within suggested copula families and hierarchical structures is carried out via empirical Bayes approach using random walk Metropolis algorithm and other Markov chain Monte Carlo techniques.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association