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Forecasting with VAR models: fat tails and stochastic volatility

Ching-Wai (Jeremy) Chiu, Haroon Mumtaz and Gabor Pinter

No 528, Bank of England working papers from Bank of England

Abstract: In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student’s t distribution and time-varying variance. We estimate this model using US data on output growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference is especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student’s t-distributed disturbances may lead to improved forecast accuracy.

Keywords: Bayesian VAR; fat-tails; stochastic volatility; Great Recession (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ore and nep-rmg
Date: 2015-05-29
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Related works:
Journal Article: Forecasting with VAR models: Fat tails and stochastic volatility (2017) Downloads
Working Paper: Forecasting with VAR Models: Fat Tails and Stochastic Volatility (2015) Downloads
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