High-frequency monitoring of growth-at-risk

Laurent Ferrara, Matteo Mogliani and Jean-Guillaume Sahuc

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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 real-time indicators to adjust promptly the stance of their policy. 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 high-frequency GaR measures for the euro area. We show that our daily GaR indicator (i) provides an early signal of GDP downturns and (ii) 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 tail risks on euro area GDP.

Keywords: Growth-at-Risk; mixed-data sampling; Bayesian quantile regressions; financial conditions; euro area. (search for similar items in EconPapers)
JEL-codes: C22 E37 E44 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2020-11
New Economics Papers: this item is included in nep-eec, nep-mac and nep-rmg
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Citations: View citations in EconPapers (6)

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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 (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2020-97

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