IDEAS home Printed from https://ideas.repec.org/a/eur/ejesjr/30.html
   My bibliography  Save this article

Excessive Credit Growth - An Early Indicator of Financial Instability

Author

Listed:
  • Ela Golemi

Abstract

This article discusses the issue of excessive credit growth, which is generally considered as an early indicator of financial and macroeconomic instability. It focuses methods that should be used in order to evaluate if the level of credit growth is excessively enough in order to start applying “countercyclical capital buffer†, a macro prudential tool proposed in the new regulatory framework of Basel Committee on Banking Supervision. Analysis focused in Central and Eastern European countries experiences with credit growth approach before the global financial crisis, show that the HP filter calculation proposed by the Basel Committee is not a suitable indicator of excessive credit growth for converging countries. A broader set of indicators and methods based in economic fundamentals of each country should be employ to determine a country’s position in the credit cycle.

Suggested Citation

  • Ela Golemi, 2015. "Excessive Credit Growth - An Early Indicator of Financial Instability," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 1, ejes_v1_i.
  • Handle: RePEc:eur:ejesjr:30
    DOI: 10.26417/ejes.v2i1.p174-179
    as

    Download full text from publisher

    File URL: https://brucol.be/index.php/ejes/article/view/5229
    Download Restriction: no

    File URL: https://brucol.be/files/articles/ejes_v1_i2_15/Ela.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.26417/ejes.v2i1.p174-179?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    2. Christopher F. Baum & Mustafa Caglayan & Neslihan Ozkan, 2004. "Nonlinear effects of exchange rate volatility on the volume of bilateral exports," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(1), pages 1-23.
    3. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    4. Ansgar Belke & Ralph Setzer, 2003. "Exchange Rate Variability and Labor Market Performance in the Visegrád Countries," Economic Change and Restructuring, Springer, vol. 36(2), pages 153-175, June.
    5. Bali, Rakesh & Guirguis, Hany, 2007. "Extreme observations and non-normality in ARCH and GARCH," International Review of Economics & Finance, Elsevier, vol. 16(3), pages 332-346.
    6. Enrique Sentana, 1995. "Quadratic ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(4), pages 639-661.
    7. Orlowski, Lucjan T., 2004. "Exchange rate risk and convergence to the Euro," ZEI Working Papers B 25-2004, University of Bonn, ZEI - Center for European Integration Studies.
    8. Engle, Robert F. & Mustafa, Chowdhury, 1992. "Implied ARCH models from options prices," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 289-311.
    9. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-368, July.
    Full references (including those not matched with items on IDEAS)

    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. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    2. Juraj Stanèík, 2007. "Determinants of Exchange-Rate Volatility: The Case of the New EU Members," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 57(9-10), pages 414-432, October.
    3. repec:ipg:wpaper:2014-503 is not listed on IDEAS
    4. Shehu Usman Rano Aliyu, 2012. "Does inflation have an impact on stock returns and volatility? Evidence from Nigeria and Ghana," Applied Financial Economics, Taylor & Francis Journals, vol. 22(6), pages 427-435, March.
    5. Marcin Chlebus, 2016. "Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Magdalena Osińska (ed.), Statistical Review, vol. 63, 2016, 3, edition 1, volume 63, chapter 4, pages 329-350, University of Lodz.
    6. Wu, Pei-Shan & Huang, Chien-Ming & Chiu, Chien-Liang, 2011. "Effects of structural changes on the risk characteristics of REIT returns," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 645-653, October.
    7. A. B. M. Rabiul Alam Beg & Sajid Anwar, 2014. "Detecting volatility persistence in GARCH models in the presence of the leverage effect," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2205-2213, December.
    8. Albrecht, Peter & Kočenda, Evžen, 2024. "Volatility connectedness on the central European forex markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    9. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    10. Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July.
    11. Manfred M. Fischer & Wolfgang Koller, 2001. "Testing for Non-Linear Dependence in Univariate Time Series: An Empirical Investigation of the Austrian Unemployment Rate," ERSA conference papers ersa01p233, European Regional Science Association.
    12. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    13. Madhavan, Vinodh, 2013. "Nonlinearity in investment grade Credit Default Swap (CDS) Indices of US and Europe: Evidence from BDS and close-returns tests," Global Finance Journal, Elsevier, vol. 24(3), pages 266-279.
    14. Charfeddine, Lanouar, 2016. "Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis," Economic Modelling, Elsevier, vol. 53(C), pages 354-374.
    15. Charfeddine, Lanouar, 2014. "True or spurious long memory in volatility: Further evidence on the energy futures markets," Energy Policy, Elsevier, vol. 71(C), pages 76-93.
    16. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    17. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, November.
    18. Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.
    19. David Ardia, 2009. "Bayesian estimation of a Markov-switching threshold asymmetric GARCH model with Student-t innovations," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 105-126, March.
    20. Liu, Heping & Erdem, Ergin & Shi, Jing, 2011. "Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed," Applied Energy, Elsevier, vol. 88(3), pages 724-732, March.
    21. Chlebus Marcin, 2017. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.

    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:eur:ejesjr:30. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Revistia Research and Publishing (email available below). General contact details of provider: https://revistia.com/index.php/ejes .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.