Addressing COVID-19 outliers in BVARs with stochastic volatility

Arbeitspapier

The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard VARs. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the pandemic period, as well as for earlier subsamples of relatively high volatility. In historical forecasting, outlier-augmented SV schemes fare at least as well as a conventional SV model.

Object Details

• ISBN: 978-3-95729-881-2
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Deutsche Bundesbank Discussion Paper ; No. 13/2022

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; General Aggregative Models: Forecasting and Simulation: Models and Applications ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation: Models and Applications
• Subject: Bayesian VARs ; stochastic volatility ; outliers ; pandemics ; forecasts

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Carriero, Andrea ; Clark, Todd E. ; Marcellino, Massimiliano ; Mertens, Elmar
• Event: Veröffentlichung
• (who): Deutsche Bundesbank
• (where): Frankfurt a. M.
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/253393
• Last update: 10.03.2025, 11:42 AM CET