A0282
Title: Fast and accurate variational inference for large Bayesian VARs with stochastic volatility
Authors: Xuewen Yu - Purdue University (United States) [presenting]
Joshua Chan - Australian National University (Australia)
Abstract: A new variational approximation is proposed for the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches based on local approximations, the new proposal provides a global approximation that considers the entire support of the joint distribution. A Monte Carlo study shows that the new global approximation is over an order of magnitude more accurate than existing alternatives. We illustrate the proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network connectedness. Our measure is able to detect the drastic increase in global bank network connectedness much earlier than rolling-window estimates from a homoscedastic VAR.
