IDEAS home Printed from https://ideas.repec.org/p/een/camaaa/2020-97.html
   My bibliography  Save this paper

High-frequency monitoring of growth-at-risk

Author

Listed:
  • Laurent Ferrara
  • Matteo Mogliani
  • Jean-Guillaume Sahuc

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.

Suggested Citation

  • Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2020-97
    as

    Download full text from publisher

    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2020-11/97_2020_ferrara_mogliani_sahuc_0.pdf
    Download Restriction: no

    More about this item

    Keywords

    Growth-at-Risk; mixed-data sampling; Bayesian quantile regressions; financial conditions; euro area.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:een:camaaa:2020-97. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Cama Admin). General contact details of provider: http://edirc.repec.org/data/asanuau.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.