Backtesting Global Growth-at-Risk

69 Pages Posted: 10 Oct 2019 Last revised: 2 Sep 2020

See all articles by Christian T. Brownlees

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences; Barcelona Graduate School of Economics (Barcelona GSE)

Andre B.M. Souza

Universitat Pompeu Fabra - Department of Economics and Business; Barcelona Graduate School of Economics (Barcelona GSE)

Date Written: September 29, 2019

Abstract

We conduct an out-of-sample backtesting exercise of multivariate Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression (QR) and GARCH models. We find evidence of predictability up to one year ahead, and that GARCH outperforms QR. In particular, our results show that standard volatility models such as GARCH produce more accurate GaR forecasts than QR models based on predictors of downside risks to GDP.

Keywords: Growth-at-Risk, Backtesting, Quantile Regression, GARCH

JEL Classification: C22, C23, C52, C53, C58

Suggested Citation

Brownlees, Christian T. and Souza, Andre B.M., Backtesting Global Growth-at-Risk (September 29, 2019). Available at SSRN: https://ssrn.com/abstract=3461214 or http://dx.doi.org/10.2139/ssrn.3461214

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain

HOME PAGE: http://84.89.132.1/~cbrownlees/

Barcelona Graduate School of Economics (Barcelona GSE) ( email )

Ramon Trias Fargas 25-27
Barcelona, Catalonia 08014
Spain

Andre B.M. Souza (Contact Author)

Universitat Pompeu Fabra - Department of Economics and Business ( email )

Barcelona
Spain

HOME PAGE: http://andrebmsouza.com

Barcelona Graduate School of Economics (Barcelona GSE) ( email )

Ramon Trias Fargas, 25-27
Barcelona, Barcelona 08005
Spain

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
244
Abstract Views
1,307
rank
139,139
PlumX Metrics