Sparse Bayesian Graphical Vector Autoregression For Risk Analysis

• Activity Number: 426
• Type: Contributed
• Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Daniel Felix Ahelegbey* and Monica Billio and Roberto Casarin
• Companies: Boston University and University of Venice and University of Venice
• Keywords: High-dimensional Models ; Large Vector Autoregression ; Model Selection ; Prior Distribution ; Sparse Graphical Models ; Risk Analysis

Abstract

This paper considers a sparsity approach for inference in large vector autoregressive (VAR) models. The approach is based on a Bayesian procedure and a graphical representation of VAR models. We discuss a Markov chain Monte Carlo algorithm for sparse graph selection, parameter estimation, and equation-specific lag selection. We show the efficiency of our algorithm on simulated data and illustrate the effectiveness of our approach in measuring contagion risk among financial institutions.