IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbwps/20202458.html
   My bibliography  Save this paper

Vulnerable growth in the Euro Area: Measuring the financial conditions

Author

Listed:
  • Figueres, Juan Manuel
  • Jarociński, Marek

Abstract

This paper examines which measures of financial conditions are informative about the tail risks to output growth in the euro area. The Composite Indicator of Systemic Stress (CISS) is more informative than indicators focusing on narrower segments of financial markets or their simple aggregation in the principal component. Conditionally on the CISS one can reproduce for the euro area the stylized facts known from the US, such as the strong negative correlation between conditional mean and conditional variance that generates stable upper quantiles and volatile lower quantiles of output growth. JEL Classification: C12, E37, E44

Suggested Citation

  • Figueres, Juan Manuel & Jarociński, Marek, 2020. "Vulnerable growth in the Euro Area: Measuring the financial conditions," Working Paper Series 2458, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20202458
    Note: 400529
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2458~849f0d9847.en.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Matheson, Troy D., 2012. "Financial conditions indexes for the United States and euro area," Economics Letters, Elsevier, vol. 115(3), pages 441-446.
    4. Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," Documents de Travail de l'OFCE 2020-06, Observatoire Francais des Conjonctures Economiques (OFCE).
    5. Adrian, Tobias & Duarte, Fernando & Liang, Nellie & Zabczyk, Pawel, 2020. "Monetary and Macroprudential Policy with Endogenous Risk," CEPR Discussion Papers 14435, C.E.P.R. Discussion Papers.
    6. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    7. Kremer, Manfred & Lo Duca, Marco & Holló, Dániel, 2012. "CISS - a composite indicator of systemic stress in the financial system," Working Paper Series 1426, European Central Bank.
    8. repec:ecb:ecbwps:20111426 is not listed on IDEAS
    9. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    10. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    11. Chavleishvili, Sulkhan & Manganelli, Simone, 2019. "Forecasting and stress testing with quantile vector autoregression," Working Paper Series 2330, European Central Bank.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Macroeconomic risks across the globe due to the Spanish Flu," Working Paper Series 2466, European Central Bank.
    2. Milan Szabo, 2020. "Growth-at-Risk: Bayesian Approach," Working Papers 2020/3, Czech National Bank.
    3. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.
    4. Angelini, Elena & Darracq Pariès, Matthieu & Zimic, Srečko & Damjanović, Milan, 2020. "ECB-BASIR: a primer on the macroeconomic implications of the Covid-19 pandemic," Working Paper Series 2431, European Central Bank.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.
    2. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    3. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    4. Mandler, Martin & Scharnagl, Michael, 2020. "Estimating the effects of the Eurosystem's asset purchase programme at the country level," Discussion Papers 29/2020, Deutsche Bundesbank.
    5. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    6. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2012. "Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 732-740.
    7. Del Negro, Marco & Giannone, Domenico & Giannoni, Marc P. & Tambalotti, Andrea, 2019. "Global trends in interest rates," Journal of International Economics, Elsevier, vol. 118(C), pages 248-262.
    8. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    9. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    10. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Working Papers IES 2017/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2017.
    11. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 185-226.
    12. Zijian Zeng & Meng Li, 2020. "Bayesian Median Autoregression for Robust Time Series Forecasting," Papers 2001.01116, arXiv.org, revised Dec 2020.
    13. Okimoto, Tatsuyoshi & Takaoka, Sumiko, 2020. "No-arbitrage determinants of credit spread curves under the unconventional monetary policy regime in Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 64(C).
    14. Dimitriadis, Timo & Liu, Xiaochun & Schnaitmann, Julie, 2020. "Encompassing tests for value at risk and expected shortfall multi-step forecasts based on inference on the boundary," Hohenheim Discussion Papers in Business, Economics and Social Sciences 11-2020, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    15. Eric Jondeau & Benoit Mojon & Jean-Guillaume Sahuc, 2020. "Bank Funding Cost and Liquidity Supply Regimes," BIS Working Papers 854, Bank for International Settlements.
    16. Shukur, Ghazi & Zeebari, Zangin, 2011. "On the median regression for SURE models with applications to 3-generation immigrants data in Sweden," Economic Modelling, Elsevier, vol. 28(6), pages 2566-2578.
    17. Zaghini, Andrea, 2017. "A tale of fragmentation: Corporate funding in the euro-area bond market," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 59-68.
    18. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    19. International Monetary Fund, 2018. "Euro Area Policies; Financial Sector Assessment Program-Technical Note-Systemic Risk Analysis," IMF Staff Country Reports 18/231, International Monetary Fund.
    20. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.

    More about this item

    Keywords

    downside risk; macro-financial linkages; quantile regression;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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:ecb:ecbwps:20202458. 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: (Official Publications). General contact details of provider: http://edirc.repec.org/data/emieude.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.

    If CitEc recognized a reference but did not link an item in RePEc 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 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.