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Kauko, K., Tölö, E. Banking Crisis Prediction with Differenced Relative Credit. Applied Economics Quarterly, 65(4), 277-297. https://doi.org/10.3790/aeq.65.4.277
Kauko, Karlo and Tölö, Eero "Banking Crisis Prediction with Differenced Relative Credit" Applied Economics Quarterly 65.4, , 277-297. https://doi.org/10.3790/aeq.65.4.277
Kauko, Karlo/Tölö, Eero: Banking Crisis Prediction with Differenced Relative Credit, in: Applied Economics Quarterly, vol. 65, iss. 4, 277-297, [online] https://doi.org/10.3790/aeq.65.4.277
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Banking Crisis Prediction with Differenced Relative Credit

Kauko, Karlo | Tölö, Eero

Applied Economics Quarterly, Vol. 65 (2019), Iss. 4 : pp. 277–297

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/aeq.65.4.277
Language
German
Pages
21
Keywords

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Individual
€ 24.00 (incl. local VAT if applicable)

Author Details

Kauko, Karlo; corresponding author. Bank of Finland, Snellmaninaukio, 00101 Helsinki, Finland. Tel.: +358 50 387 0337

Tölö, Eero; Bank of Finland and University of Helsinki, Snellmaninaukio, 00101 Helsinki, Finland

References

  1. Alessi, L./Detken, C. (2018): “Identifying excessive credit growth and leverage,” Journal of Financial Stability 38, 289–308.  Google Scholar
  2. Antunes, A./Bonfim, D./Monteiro, N./Rodrigues, P. M. M. (2018): “Forecasting banking crises with dynamic panel probit models,” International Journal of Forecasting 34, 249–275.  Google Scholar
  3. Aslanidis, N./Fountas, S. (2014): “Is Real GDP Stationary? Evidence from a Panel Unit Root Test with Cross-Sectional Dependence and Historical Data,” Empirical Economics 46, 101–108.  Google Scholar
  4. Basel Committee on Banking Supervision (2010a): Countercyclical Capital Buffer Proposal, Basel Committee on Banking Supervision, Consultative Document, Bank for International Settlements July 2010.  Google Scholar
  5. Basel Committee on Banking Supervision (2010b): Guidance for national authorities operating the countercyclical capital buffer, Bank for International Settlements, December 2010.  Google Scholar
  6. Bege, T. J./Jordà, O. (2011): “Evaluating the classification of economic activity into recessions and expansions.” American Economic Journal: Macroeconomics 3, 246–277.  Google Scholar
  7. Borio, C./Lowe, P. (2002): “Assessing the risk of banking crises,” BIS Quarterly Review (December), 43–54.  Google Scholar
  8. Burns, A. F./Mitchell, W. C. (1946): Measuring Business Cycles, NBER Books, New York.  Google Scholar
  9. Castro, C./Estrada, A./Martínez, J. (2014): “The countercyclical capital buffer in Spain: An exploratory analysis of key guiding indicators,” Financial Stability Journal (Bank of Spain) 27.  Google Scholar
  10. Demirgüç-Kunt, A./Detragiache, E. (1998): “The determinants of banking crises in developing and developed countries,” IMF Staff Papers 45, 81–109.  Google Scholar
  11. Detken, C./Weeken, O./Alessi, L./Bonfim, D./Boucinha, M. M./Castro, C./Frontczak, S./Giordana, G./Giese, J./Jahn, N./Kakes, J./Klaus, B./Lang, J. H./Puzanova, N./Welz, P. (2014): “Operationalising the countercyclical capital buffer: indicator selection, threshold identification and calibration options,” ESRB Occasional Paper Series 5.  Google Scholar
  12. Drehmann, M./Borio, C./Gambacorta, L./Jiménez, G./Trucharte, C. (2010): “Countercyclical capital buffers: exploring options,” BIS Working Papers 317.  Google Scholar
  13. Gonzalez, R. B./Marinho, L. S. G./de Vasconcellos e Lima, J. I. A. (2017): “Re-anchoring Countercyclical Capital Buffers: Bayesian Estimates and Alternatives Focusing on Credit Growth,” International Journal of Forecasting 33, 1007–1024.  Google Scholar
  14. Groth, A./Ghil, M./Hallegatte, S. /Dumas, P. (2015): “The role of oscillatory modes in US business cycles,” OECD Journal: Journal of Business Cycle Measurement and Analysis.  Google Scholar
  15. Hamilton, J. D. (2018): “Why you should never use the Hodrick-Prescott filter,” Review of Economics and Statistics 100, 831–841.  Google Scholar
  16. Hanley, J. A./McNeil, B. J. (1982): “The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve,” Radiology 143, 29–36.  Google Scholar
  17. Jagric, T. (2003): “Business Cycles in Central and East European Countries,” Eastern European Economics 41, 6–23.  Google Scholar
  18. Kaminsky, G. L./Reinhart, C. M. (1999): “The twin crises: the causes of banking and balance-of-payments problems,” American Economic Review 89, 473–500.  Google Scholar
  19. Kauko, K. (2012): “Triggers for countercyclical capital buffers,” Bank of Finland, BoF Online 7/2012.  Google Scholar
  20. Kauko, K. (2014): “How to foresee banking crises? A survey of the empirical literature,” Economic Systems 38, 289–308.  Google Scholar
  21. Laeven, L./Valencia, F. (2012): “Systemic Banking Crises Database: An Update,” IMF Working Paper, WP/12/163.  Google Scholar
  22. Lo Duca, M./Koban, A./Basten, M./Bengtsson, E./Klaus, B./Kusmierczy, P./Lang, J. H./Detken, C. (2017): “A new database for financial crises in European countries,” ECB Occasional Paper Series 194.  Google Scholar
  23. Repullo, R./Saurina, J. (2011): “The Countercyclical Capital Buffer of Basel III: A Critical Assessment,” CEMFI Working Papers 1102.  Google Scholar
  24. Schularick, M./Taylor, A. M. (2012): “Credit booms gone bust: monetary policy, leverage cycles and financial crises 1870–2008,” American Economic Review 102, 1029–1061.  Google Scholar
  25. Schüler, Y. S./Hiebert, P. P./Peltonen, T. A. (2017): “Coherent financial cycles for G-7 countries: Why extending credit can be an asset.” ECB Working Paper Series 43/2017.  Google Scholar
  26. Tölö, E./Laakkonen, H./Kalatie, S. (2018): “Evaluating Indicators for Use in Setting the Countercyclical Capital Buffer,” International Journal of Central Banking 14, 51–111.  Google Scholar
  27. Verona, F. (2016): “Time-frequency characterization of the U.S. financial cycle,” Economics Letters 144, 75–79.  Google Scholar

Abstract

Abstract

Indicators based on the ratio of credit to GDP have been found to be highly useful predictors of banking crises. Differences in this ratio seem a highly promising early warning indicator. We test a large number of slightly different versions of the differenced credit-to-GDP ratio with data on euro area members. The optimal time interval of the difference is about two years. Using the moving average of GDP over several years rather than the latest annual data is shown to have little impact on forecasting performance. The proposed indicator demonstrates particular promise at relatively short forecasting horizons (2–3 years).

Table of Contents

Section Title Page Action Price
Karlo Kauko / Eero Tölö: Banking Crisis Prediction with Differenced Relative Credit 1
Abstract 1
1. Introduction 1
1.1 Research Question 1
1.2 Previous Literature 2
2. Method and Data 4
2.1 Alternative Ways of Calculating the Indicator 4
2.2 Data 6
2.3 Assessment Method 7
3. Results 9
4. Forecasting Horizons 1
5. Robustness Test 1
6. Conclusions 1
References 2

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