High-Frequency Monitoring of Growth-at-Risk

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.

JEL Classification: C22, E37, E44