IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

The financial content of inflation risks in the euro area

  • Andrade, P.
  • Fourel, V.
  • Ghysels, E.
  • Idier, I.

Recent studies emphasize that survey-based inflation risk measures are informative about future inflation and thus useful for monetary authorities. However, these data are typically available at a quarterly frequency whereas monetary policy decisions require a more frequent monitoring of such risks. Using the ECB survey of professional forecasters, we show that high-frequency financial market data have predictive power for the low-frequency survey-based inflation risk indicators observed at the end of a quarter. We rely on MIDAS regressions to handle the problem of mixing data with different frequencies that such an analysis implies. We also illustrate that upside and downside risks react differently to financial indicators.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.banque-france.fr/uploads/tx_bdfdocumentstravail/DT437_01.pdf
Download Restriction: no

Paper provided by Banque de France in its series Working papers with number 437.

as
in new window

Length: 35 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:bfr:banfra:437
Contact details of provider: Postal:
Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS

Web page: http://www.banque-france.fr/

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
  2. Kilian, Lutz & Manganelli, Simone, 2007. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," CEPR Discussion Papers 6031, C.E.P.R. Discussion Papers.
  3. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  4. Carlos Capistrán & Allan Timmermann, 2006. "Forecast Combination with Entry and Exit of Experts," Working Papers 2006-08, Banco de México.
  5. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro-area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
  6. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
  7. Yuriy Gorodnichenko & Olivier Coibion, 2010. "What can survey forecasts tell us about informational rigidities?," 2010 Meeting Papers 277, Society for Economic Dynamics.
  8. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
  9. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
  10. Libero Monteforte & Gianluca Moretti, 2013. "Real‐Time Forecasts of Inflation: The Role of Financial Variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 51-61, 01.
  11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  12. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  13. Nicholas Bloom, 2007. "The Impact of Uncertainty Shocks," NBER Working Papers 13385, National Bureau of Economic Research, Inc.
  14. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2007. "The ECB survey of professional forecasters (SPF) – A review after eight years’ experience," Occasional Paper Series 59, European Central Bank.
  15. Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997. "Efficient Inflation Estimation," NBER Working Papers 6183, National Bureau of Economic Research, Inc.
  16. Gita Gopinath & Roberto Rigobon, 2008. "Sticky Borders," The Quarterly Journal of Economics, Oxford University Press, vol. 123(2), pages 531-575.
  17. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
  18. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
  19. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2011. "Extracting deflation probability forecasts from Treasury yields," Working Paper Series 2011-10, Federal Reserve Bank of San Francisco.
  20. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  21. Stephen G. Cecchetti, 2006. "Measuring the Macroeconomic Risks Posed by Asset Price Booms," NBER Working Papers 12542, National Bureau of Economic Research, Inc.
  22. García, Juan Angel & Manzanares, Andrés, 2007. "What can probability forecasts tell us about inflation risks?," Working Paper Series 0825, European Central Bank.
  23. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:bfr:banfra:437. 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: (Michael brassart)

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.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.