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Forecasting with VAR Models: Fat Tails and Stochastic Volatility

Author

Listed:
  • Ching-Wai (Jeremy) Chiu

    () (Bank of England)

  • Haroon Mumtaz

    () (Queen Mary University of London)

  • Gabor Pinter

    () (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 that features both stochastic volatility and Student�s t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with Student�s t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference appears to be 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.

Suggested Citation

  • Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2015. "Forecasting with VAR Models: Fat Tails and Stochastic Volatility," CReMFi Discussion Papers 2, CReMFi, School of Economics and Finance, QMUL.
  • Handle: RePEc:qmm:wpaper:2
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    Cited by:

    1. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pinter, Gabor, 2016. "VAR models with non-Gaussian shocks," LSE Research Online Documents on Economics 86238, London School of Economics and Political Science, LSE Library.
    2. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    3. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    4. Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
    5. Magnus Reif, 2018. "Macroeconomic Uncertainty and Forecasting Macroeconomic Aggregates," ifo Working Paper Series 265, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    6. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España;Working Papers Homepage.
    7. Joshua C.C. Chan, 2015. "Large Bayesian VARs: A flexible Kronecker error covariance structure," CAMA Working Papers 2015-41, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Garreth Rule, 2015. "Understanding the central bank balance sheet," Handbooks, Centre for Central Banking Studies, Bank of England, edition 1, number 32.

    More about this item

    Keywords

    Financial Frictions; Predictive Densities; Great Recession; Threshold VAR;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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