Fat-tails in VAR Models
AbstractWe 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.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 714.
Date of creation: Mar 2014
Date of revision:
Bayesian VAR; Fat tails; Stochastic volatility;
Find related papers by 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-04-18 (All new papers)
- NEP-BAN-2014-04-18 (Banking)
- NEP-ECM-2014-04-18 (Econometrics)
- NEP-ETS-2014-04-18 (Econometric Time Series)
- NEP-FOR-2014-04-18 (Forecasting)
- NEP-RMG-2014-04-18 (Risk Management)
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