1. Simon van Norden HEC Montréal and CIRANO Marc Wildi Institute of Data Analysis and Process Design, Winterthur

3. Miss Rate Crisis Occurs soon after No Crisis Occurs soon after Crisis Signaled(a) Correct(b) Type II error No Crisis Signaled(c ) Type I error(d) Correct 0% 100% ROC Curve Hit Rate

4.  Banking Crisis Dates (quarterly) ◦ Taken from Drehmann et al. (2012 IJCB) ◦ Based on Laeven and Valencia (2008, 2010) & Reinhart and Rogoff (2008)  47 Banking crises post-1959 ◦ "Crisis" decision is judgemental ◦ includes failures, govt. intervention, etc

5. Number Crises 1959-2011 Nations 0CA TW HK 1AU AT BE FI DE GR IT IE JP LU NL NZ NO PT CN IN ID KR SG 2DK FR SE CH US CL MX ZA TR 3ES GB 4AR

7.  Credit ◦ Banking lending to non-financial private sector ◦ Quarterly data from national sources  34 countries from 1959Q1-2011Q2  definitions vary slightly from country to country  same as Drehmann et al. (2012)  Credit/GDP is still non-stationary ◦ real-time detrending problem ◦ Wildi has good results with optimized frequency- based filters, so.....

8.  Using only Credit data ◦ look for evidence of a "credit cycle" ◦ design a univariate filter to isolate it ◦ Wildi's designs optimize speed & reliability  Using the "Banking Crisis" dates ◦ Any relationship to measured credit "gaps"?  Help predict? significantly? ◦ Benchmark to BIS, IMF measures ◦ Sensitivity analysis

9.  Is there a credit cycle? What does it look like?  Pool data across countries ◦ Assume they have a common autocorrelogram.  OLS estimation ◦ Use the estimated autocorrelogram to estimate the spectral density. ◦ Is there evidence of a credit cycle? Where?

10. Credit Cycle? Seasonal Target 10-30 yr movements

11.  Target Filter ◦ 10-30 yr band-pass on differenced data  Sample period: 1979Q2-2011Q6 ◦ Check robustness with sample split @ 2005Q4  Crisis Window: 5-12Q ◦ How long after signal may crisis occur? ◦ 5-32Q, 9-32Q also examined (not reported here)  Range of customization values for λ, w (3 x 3) ◦ λ penalizes lags, w penalizes frequency errors.

13. AUC  AUC: Area Under the Curve ◦ 0.5 => no useful information ◦ 1.0 => perfect prediction  H 0 : AUC = 0.5 vs H A : AUC > 0.5 ◦ test based on Mann-Whitney U-statistic ◦ compares ranks of gaps across two samples  crisis & non-crisis  Tests require independent observations ◦ We bootstrap instead

14. NationsPeriod IMFBISDFA 1DFA 9 34FULL AUC0.58630.54950.69350.7335 p-Value6.5%10.5%0.1%0.0% 34 pre- 2006 AUC0.54450.53250.71070.7498 p-Value23.1%29.0%0.5%0.3% 11FULL AUC0.59680.58570.81990.8487 p-Value6.7%1.6%0.0% 11 pre- 2006 AUC0.61270.58050.89370.8829 p-Value16.1%12.0%0.0%0.4%

17.  Best filter (#9) has AUC = 0.73  Warning before 90% of crises gives a false alarm rate of 55%  Warning before 50% of crises gives a false alarm rate of 15%.

18.  Standardizing national gaps to N(0,1) does not qualitatively change the results  Longer event windows (>12Q) generally reduce performance  Long filters (20-yr weighted average) do better than short (10-yr) ◦ Better distinguishes long cycles from trends.  Post-2005 performance hard to evaluate ◦ Small samples, volatile results

21. NationsPeriod IMFBISDFA 1DFA 9 34 post- 2005 AUC0.47610.52620.49110.5561 p-Value75.1%50.1%52.4%34.3% 11 post- 2005 AUC0.62410.70040.91670.9202 p-Value24.1%7.1%2.3%3.5%

22.  Credit gaps have significant predictive power for banking crises ◦ More power for biggest financial markets  Better filter design consistently improves the forecasting ability of the gaps  Catching most crises may require high false alarm rates. ◦ 50% hit rate  20% false alarm rate

23.  Do our filters improve significantly on BIS? IMF?  What alarm threshold should regulators use? ◦ Depends on relative costs of missed alarms and false alarms.  Can these indicators significantly improve expected welfare?