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Comparison of Models for Growth-at-Risk Forecasting

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  • Aleksei Kipriyanov

    (HSE University, International College of Economics and Finance)

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.

Suggested Citation

  • Aleksei Kipriyanov, 2022. "Comparison of Models for Growth-at-Risk Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 23-45, March.
    • Handle: RePEc:bkr:journl:v:81:y:2022:i:1:p:23-45
    DOI: 10.31477/rjmf.202201.23
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    growth-at-risk; quantile regression; quantile random forest; GARCH; backtesting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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