Template-type: ReDIF-Article 1.0
Author-Name: Aleksei Kipriyanov
Author-Email: akipriyanov@hse.ru
Author-Workplace-Name: HSE University, International College of Economics and Finance
Title: Comparison of Models for Growth-at-Risk Forecasting
Abstract: During the past several decades, the importance of assessing the risk of GDP growth downturns has increased tremendously. The financial crisis of 2008-2009 and the global lockdown caused by the COVID-19 pandemic demonstrated the vulnerability of the modern economy. As a result, a new framework (Growth-at-Risk) has been developed which allows the estimation of the size of the potential downfall of future GDP growth. However, most of the research focuses on the performance of quantile regression. I apply different approaches to forecasting growth-at-risk, including quantile regression, quantile random forests, and generalised autoregressive conditional heteroscedastic (GARCH) models, using the US economy for the analysis. I find that GARCH-type models perform worse at 5% and 10% coverage levels, but that quantile random forests and quantile regressions seem to have equal predictive ability.
Classification-JEL: C22, C52, C53, C58
Keywords: growth-at-risk, quantile regression, quantile random forest, GARCH, backtesting
Journal: Russian Journal of Money and Finance
Pages: 23-45
Volume: 81
Issue: 1
Year: 2022
Month: March
DOI: 10.31477/rjmf.202201.23
File-URL: https://rjmf.econs.online/upload/iblock/73c/Comparison-of-Models-for-Growth-at-Risk-Forecasting.pdf
Handle: RePEc:bkr:journl:v:81:y:2022:i:1:p:23-45