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Fat-tails in VAR Models

Author

Listed:
  • Ching-Wai (Jeremy) Chiu

    (Bank of England)

  • Haroon Mumtaz

    (Queen Mary University of London)

  • Gabor Pinter

    (Bank of England)

Abstract

We confirm that standard time-series models for US output growth, inflation, interest rates and stock market returns feature non-Gaussian error structure. We build a 4-variable VAR model where the orthogonolised shocks have a Student t-distribution with a time-varying variance. We find that in terms of in-sample fit, the VAR model that features both stochastic volatility and Student-t disturbances outperforms restricted alternatives that feature either attributes. The VAR model with Student-t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity. This difference appears to be especially stark over the recent financial crisis.

Suggested Citation

  • Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2014. "Fat-tails in VAR Models," Working Papers 714, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:wp714
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    Cited by:

    1. Dimitrakopoulos, Stefanos, 2017. "Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility," Economics Letters, Elsevier, vol. 150(C), pages 10-14.
    2. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers 720, Queen Mary University of London, School of Economics and Finance.
    3. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "Bayesian Vector Autoregressions with Non-Gaussian Shocks," CReMFi Discussion Papers 5, CReMFi, School of Economics and Finance, QMUL.
    4. Franta, Michal, 2017. "Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 136-157.

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    More about this item

    Keywords

    Bayesian VAR; Fat tails; Stochastic volatility;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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