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Financial Crises, Macroprudential Policy and the Reliability of Credit-to-GDP Gaps

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Abstract

The Basel III regulation explicitly prescribes the use of Hodrick–Prescott filters to estimate credit cycles and calibrate countercyclical capital buffers. However, the filter has been found to suffer from large ex-post revisions, raising concerns on its fitness for policy use. To investigate this problem, we study credit cycles in a panel of 26 countries between 1971 and 2018. We reach two conclusions. The bad news is that the limitations of the one-side HP filter are serious and pervasive. The good news is that they can be easily mitigated. The filtering errors are persistent and hence predictable. This can be exploited to construct real-time estimates of the cycle that are less subject to ex-post revisions, forecast financial crises more reliably, and stimulate the build-up of bank capital before a crisis.

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Notes

  1. The data are available at https://www.esrb.europa.eu/pub/financial-crises/html/index.en.html.

  2. “Table A.2 of the Appendix” reports the crisis periods country by country. Our key results are robust to using a specific chronology rather than merging the crisis episodes: see Table A.3.

  3. All calculations are based on the entire sample period, so the smoothing revisions would have not been available in real time. The question of how the patterns in the data can be exploited in real time will be tackled in Sects. 4 and 5.

  4. The title “Commonwealth economies” refers to Australia, Canada, New Zealand, South Africa, UK and USA. Northern European countries include Austria, Belgium, Denmark, Finland, France, Germany, Netherlands, Norway, Sweden and Switzerland. As common, the acronym GIIPS refers to Greece, Ireland, Italy, Portugal and Spain. Asian countries include India, Japan, Korea Singapore and Thailand.

  5. “Figure A.1 of the Appendix” shows the filtered and smoothed credit cycles for all countries in our sample. We discuss specific cases and regional differences in Sects. 5 and 6.

  6. We rely throughout the analysis on a smoothing parameter \(\lambda\)= 400,000, as prescribed by the Basel agreements. The reason is, once again, that we intend to focus on the endpoint problems of the filter selected by the regulator rather than its general performance.

  7. Notice that the error of interest is the discrepancy between (i) the real-time estimate of the correction delivered by the models, and (ii) the corresponding full-sample estimate calculated ex-post by the Hodrick–Prescott filter, i.e., \({\widehat{C}}_t-C_{t-h|T}\). The errors are model-, country- and horizon-specific. For each model and horizon, the table reports the RMSE obtained by averaging the errors over countries and quarters. We provide additional details on the estimated equations in “Table A.4 of the Appendix”.

  8. We refer the reader to Sect. 4 for formal definitions of these terms.

  9. This highlights a general challenge for our approach: insofar as forecasts are less volatile than their targets, a predictive model may be unable to generate corrections that have both the right sign and a plausible magnitude when the credit gap is underestimated to start with. However, in Sects. 5.2 and 6 we show that (i) the adjusted filter performs well on average across the crisis episodes included in our sample, and (ii) it can be further improved by introducing additional predictors in the regression model.

  10. The statistics are calculated over the full sample. The results are similar if the evaluation is carried out using a restricted sample 1981–2007 that excludes some initial observations (for which the estimates might be unstable) and the global financial crisis: see Table A.5.

  11. Canada, New Zealand, Singapore and South Africa are excluded from this part of the analysis because of lack of information about financial crises. A full description of the crisis episodes is provided in “Table A.2 of the Appendix”.

  12. See “Appendix 1.1” and BCBS (2010) for more details on the BCBS CCyB rule.

  13. See Sect. 2 for a description of the dataset.

  14. The Global financial cycle indicator is constructed applying a dynamic factor model to a large panel of asset prices, and, as such, it has no direct relation to the credit quantities used to calculate credit-to-GDP gaps.

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Acknowledgements

We acknowledge comments and suggestions from Stijn Claessens, Luigi Federico Signorini and two anonymous referees of the IMF Economic Review, and retain all responsibility for any remaining errors. The views expressed in this paper are those of the authors and should not be attributed to the Bank of Italy or the Eurosystem.

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Authors and Affiliations

  1. Directorate General for Economics, Statistics and Research, Banca d’Italia, Rome, Italy

    Piergiorgio Alessandri, Pierluigi Bologna & Maddalena Galardo

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  1. Piergiorgio Alessandri
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Correspondence to Maddalena Galardo.

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Appendix

Appendix

1.1 Basel Committee Recommendations on the Calculation of the Countercyclical Capital Buffer

According to BCBS (2011) the credit-to-GDP gap is defined as the difference between an economy’s aggregate credit-to-GDP ratio and its long-run trend. The long-term trend of credit-to-GDP ratio is computed with a one-side (recursive) Hodrick–Prescott filter with a smoothing parameter \(\lambda\) = 400,000. Credit denotes a broad measure of the stock of domestic credit to the private non-financial sector outstanding at the end of quarter t. The credit-to-GDP gap is then translated in a percentage of the bank risk-weighted assets by calculating a benchmark buffer rate based on the piece-wise linear rule:

  • \(CCyB_t=0\) if \(GAP_t<2\%\).

  • \(CCyB_t=0.3125*GAP_t-0.625\) if \(2\%<GAP_t<10\%\).

  • \(CCyB_t=2.5\%\) if \(GAP_t>10\%\).

The credit definition suggested by BCBS (2011) includes all private credit issued by banks and non-bank financial institutions. Authorities are, however, allowed to use (i) additional measures of the credit gap and/or (ii) alternative de-trending methods in order to better capture the specificities of their national economies.

See Tables A.1, A.2, A.3, A.4, A.5, A.6, A.7 and Fig. A.1.

Table A.1 Credit-to-GDP ratios, sample statistics
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Table A.2 Number of quarters
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Table A.3 Alternative crisis database
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Table A.4 Model selection: ARDL
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Table A.5 Relation between ex-ante and ex-post estimates of the credit gap, 1981q1–2007q4
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Table A.6 Extended model selection: ARDL
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Table A.7 Impact of the information set on the estimated credit gaps
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Fig. A.1
figure 6figure 6figure 6figure 6

Credit-to-GDP gap: comparing filtering methods

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Alessandri, P., Bologna, P. & Galardo, M. Financial Crises, Macroprudential Policy and the Reliability of Credit-to-GDP Gaps. IMF Econ Rev 70, 625–667 (2022). https://doi.org/10.1057/s41308-022-00168-6

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