Backtesting global Growth-at-Risk

Autor: André B.M. Souza, Christian T. Brownlees
Rok vydání: 2021
Předmět:
Zdroj: Journal of Monetary Economics. 118:312-330
ISSN: 0304-3932
DOI: 10.1016/j.jmoneco.2020.11.003
Popis: 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.
Databáze: OpenAIRE