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426 – Financial Risk Analysis
Sparse Bayesian Graphical Vector Autoregression for Risk Analysis
Sponsor: Section on Risk Analysis
Keywords: High-dimensional Models, Large Vector Autoregression, Model Selection, Prior Distribution, Sparse Graphical Models, Risk Analysis
Daniel F. Ahelegbey
University of Boston
Monica Billio
University of Venice
Roberto Casarin
University of Venice
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.