Basel III and the Prediction of Financial

CrisesSimon van NordenHEC Montréal and CIRANO

Marc Wildi Institute of Data Analysis and Process Design, Winterthur

Full SampleAll Nations

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

Receiver Operating Characteristics(ROC)

0%0%

100%

100%

ca

a

dbb

ROC Curve

HitRate

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

Data – Banking Crises

Number Crises1959-2011

Nations

0 CA TW HK

1 AU AT BE FI DE GR IT IE JP LU NL NZ NO PT CN IN ID KR SG

2 DK FR SE CH US CL MX ZA TR

3 ES GB

4 AR

Banking Crises by Country

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.....

Credit

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

Experimental Design

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?

What should the credit "gap" measure?

Credit Cycle?

Seasonal

Target 10-30 yr movements

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.

Filters: Application

Full SampleAll Nations

AUC: Area Under the Curve◦ 0.5 => no useful information◦ 1.0 => perfect prediction

H0: AUC = 0.5 vs HA: 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

Scoring ROC Curves

Nations

Period   IMF BIS DFA 1 DFA 9

34 FULL

AUC 0.5863 0.5495 0.6935 0.7335

p-Value 6.5% 10.5% 0.1% 0.0%

34pre-2006

AUC 0.5445 0.5325 0.7107 0.7498

p-Value 23.1% 29.0% 0.5% 0.3%

11 FULL

AUC 0.5968 0.5857 0.8199 0.8487

p-Value 6.7% 1.6% 0.0% 0.0%

11pre-2006

AUC 0.6127 0.5805 0.8937 0.8829

p-Value 16.1% 12.0% 0.0% 0.4%

Comparing AUCs

Pre-2006All Nations

Full SampleBig Nations (CA,CH,CN,DE,ES,FR,HK,IT,JP,UK,US)

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%.

How useful is this?

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

Sensitivity Analysis (ongoing)

Post-2005All Nations

Post-2005Big Nations

Nations Period   IMF BIS DFA 1 DFA 9

34post-2005

AUC 0.4761 0.5262 0.4911 0.5561

p-Value 75.1% 50.1% 52.4% 34.3%

11post-2005

AUC 0.6241 0.7004 0.9167 0.9202

p-Value 24.1% 7.1% 2.3% 3.5%

Comparing (recent) AUCs

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

Conclusions

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?

Further Research...