Backtesting global Growth-at-Risk

Recognition Program

Authors: Christian Brownlees and André B.M. Souza

Journal of Monetary Economics, Vol. 118, 312-330, March, 2021

We conduct an out-of-sample backtesting exercise of Growth-at-Risk (GaR) predictions for 24 OECD countries. We consider forecasts constructed from quantile regression and GARCH models. The quantile regression forecasts are based on a set of recently proposed measures of downside risks to GDP, including the national financial conditions index. The backtesting results show that quantile regression and GARCH forecasts have a similar performance. If anything, our evidence suggests that standard volatility models such as the GARCH(1,1) are more accurate.

This paper is acknowledged by the Barcelona School of Economics Recognition Program