Fast and accurate variational inference for large Bayesian VARs with stochastic volatility

Vol: 
108/2020
Author name: 
Chan JCC
Yu X
Year: 
2020
Month: 
December
Abstract: 

We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the entire support of the joint distribution. In a Monte Carlo study we show 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.

Publication file: 

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