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426 – Financial Risk Analysis
Sparse Bayesian Graphical Vector Autoregression for 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.