Comparison of Models for Growth-at-Risk Forecasting

Aleksei Kipriyanov ()
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Aleksei Kipriyanov: HSE University, International College of Economics and Finance

Russian Journal of Money and Finance, 2022, vol. 81, issue 1, 23-45

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.

Keywords: growth-at-risk; quantile regression; quantile random forest; GARCH; backtesting (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 C58 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:81:y:2022:i:1:p:23-45

DOI: 10.31477/rjmf.202201.23

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