Common Drifting Volatility in Large Bayesian VARs

Massimiliano Marcellino, Andrea Carriero and Todd Clark

No 8894, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: The estimation of large Vector Autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of estimated volatilities in empirical analyses is often very similar across variables. Using a combination of a standard natural conjugate prior for the VAR coefficients, and an independent prior on a common stochastic volatility factor, we derive the posterior densities for the parameters of the resulting BVAR with common stochastic volatility (BVAR-CSV). Under the chosen prior the conditional posterior of the VAR coefficients features a Kroneker structure that allows for fast estimation, even in a large system. Using US and UK data, we show that, compared to a model with constant volatilities, our proposed common volatility model significantly improves model fit and forecast accuracy. The gains are comparable to or as great as the gains achieved with a conventional stochastic volatility specification that allows independent volatility processes for each variable. But our common volatility specification greatly speeds computations.

Keywords: Bayesian vars; Forecasting; Prior specification; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
Date: 2012-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (36)

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Related works:
Journal Article: Common Drifting Volatility in Large Bayesian VARs (2016)
Working Paper: Common Drifting Volatility in Large Bayesian VARs (2012)
Working Paper: Common drifting volatility in large Bayesian VARs (2012)
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