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Common Drifting Volatility in Large Bayesian VARs

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  • Carriero, Andrea
  • Clark, Todd
  • Marcellino, Massimiliano

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.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8894.

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Date of creation: Mar 2012
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Handle: RePEc:cpr:ceprdp:8894

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Keywords: Bayesian VARs; forecasting; prior specification; stochastic volatility;

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Citations

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Cited by:
  1. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
  2. Christine Garnier & Elmar Mertens & Edward Nelson, 2013. "Trend inflation in advanced economies," Finance and Economics Discussion Series 2013-74, Board of Governors of the Federal Reserve System (U.S.).
  3. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers 2012-14, Scottish Institute for Research in Economics (SIRE).
  4. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2013. "Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility," CEPR Discussion Papers 9312, C.E.P.R. Discussion Papers.

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