High-frequency monitoring of growth at risk

Jean-Guillaume Sahuc, Matteo Mogliani and Laurent Ferrara

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Abstract: Monitoring changes in financial conditions provides valuable information on the contribution of financial risks to future economic growth. For that purpose, central banks need Q3 real-time indicators to promptly adjust their policy stance. In this paper, we extend the quarterly growth-at-risk (GaR) approach of Adrian et al. (2019) by accounting for the high-frequency nature of financial conditions indicators. Specifically, we use Bayesian mixed-data sampling (MIDAS) quantile regressions to exploit the information content of both a financial stress index and a financial conditions index, leading to real-time Q4 high-frequency GaR measures for the euro area. We show that our daily GaR indicator (i) displays good GDP nowcasting properties, (ii) can provide an early signal of GDP downturns, and (iii) allows day-to-day assessment of the effects of monetary policies. During the first six months of the Covid-19 pandemic period, it has provided a timely measure of the tail risks to euro-area GDP.

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Date: 2022
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Citations: View citations in EconPapers (26)

Published in International Journal of Forecasting, 2022, 38, pp.582-595

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Related works:
Journal Article: High-frequency monitoring of growth at risk (2022)
Working Paper: High-frequency monitoring of growth-at-risk (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03361425

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