Improving early warning indicators for banking crises – satisfying policy requirements

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Improving early warning indicators for banking crises – satisfying policy requirements

Mathias Drehmann and Mikael Juselius

Bank for International Settlements

“Understanding Macroprudential Regulation”

Norges Bank, Oslo, 29–30 November 2012

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CGFS report No 48

Operationalizing the selection and application of

macroprudential instruments

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Operationalising macroprudential policies

 Report focusses on 3 high-level criteria that are key in determining instrument selection and application in practice

 The ability to determine the appropriate timing for the activation or deactivation of the instrument

 The effectiveness of the MPI in achieving the stated objective

 The efficiency of the instrument in terms of a cost- benefit assessment

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Report ends with 9 questions and answers

1. To what extent are vulnerabilities building up or crystallising?

2. How (un)certain is the risk assessment?

3. Is there a robust link between changes in the instrument and the stated policy objective?

4. How are expectations affected?

5. What is the scope for leakages and arbitrage?

6. How quickly and easily can an instrument be implemented?

7. What are the costs of applying a macroprudential instrument?

8. How uncertain are the effects of the policy instrument?

9. What is the optimal mix of tools to address a given vulnerability?

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Report analysis three groups of macroprudential instruments

 Capital-based tools (countercyclical capital buffers, sectoral capital requirements and dynamic provisions)

 Liquidity-based tools (countercyclical liquidity requirements)

 Asset-side tools (loan-to- value (LTV) and debt-to-income (DTI) ratio caps)

 For all tools report proposes ‘transmission maps’

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Increase resilience

Impact on the credit cycle

↑ lending spreads

 dividend and bonuses

Undertake SEOs¹

 credit demand

Options to address shortfall

Asset prices

Loan market

Increasecapital requirements or provisions

 credit supply

 Voluntary buffers

Arbitrage away

Leakages to non-

banks

Expectation channel Reprice

loans

 assets, especially with

high RWA

↑ Loss Absorbency

Tighter risk management

Transmission map for capital based tools

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Improving early warning indicators for banking crises – satisfying policy

requirements

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Introduction

 CGFS (2012): Policymakers need to be able to determine the appropriate timing for the activation or deactivation of the instrument

 In this paper we want to find reliable early warning indicators (EWIs) for systemic banking crises

 What policy requirements do EWIs need to satisfy?

 Need to be evaluated with preference free methodology

 Need to have right timing

 Need to be stable

 Need to be robust

 Need to be understood by policymakers

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 We assess a broad range of indicators

 We find

 Credit-to-GDP gap best indicator for predicting crises 2- 5 years in advance

 Debt service ratios highly successful indicator for predicting crises 1-2 years in advance

Implementing the framework

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 To fully evaluate quality of a signal would need to know

preferences of policymakers, which are unknown (eg CGFS (2012))

 What are costs of acting on wrong signals (false positives)?

 What are the benefits of acting on correct signals (true positives)?

→ Need to evaluate signalling quality independent of preferences

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How to evaluate the goodness of an EWI?

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The ROC curve

 Policymakers receive noisy signal S

 S higher → higher risk of a crisis

 At which threshold you policymakers act?

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Area under ROC curve as measure of signalling quality

 Area under the ROC curve (AUROC) provides summary measure of the classification ability (eg Jorda and Taylor, 2011):



 AUROC=0.5 → uninformative indicator

 AUROC=1 → fully informative indicator

 AUROC ideal measure if preferences are not known

 Benefits

 Can be estimated non-paramterically

 Has convenient statistical properties

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

 ¹

0ROC(FP)dFP AUROC

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Timing of ideal EWIs

 Ideal EWI needs to signal crisis early enough

 Likely to be 1-2 year lead-lag relationship (e.g.

countercyclical capital buffers)

 Policymakers tend to observe trends before reacting (e.g. Bernanke, 2004)

 Ideal EWI signal crises not too early

 Introducing buffers too early may undermine effectiveness (e.g. Caruana, 2010)

 We look at individual quarters within a 5 year horizon

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EWIs need to be stable and robust

 Policymakers adjust policy stance gradually

 Optimal for MP (Bernanke, 2004, Orphanides, 2003)

 Indictor should issue consistent signals

 Consistency of signal tied to persistency of underlying series (eg Park and Phillips (2000))

 High degree of persistency problematic for statistical inference

 Non-parametric approach

 EWIs need to be robust to different samples and specifications

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Interpretability of EWI

 Evidence that practitioners value sensibility of forecasts more than accuracy (Huss, 1987) adjust forecasts if the lack justifiable explanations (Onka-Atay et al (2009)

 Purely statistical approaches are not suitable for policy purposes and communication

 Our indicators reflect

 excessive leverage and asset price booms (Kindleberger, 2000, and Minsky, 1982)

 non-core deposits (Hahm et al, 2012)

 the business cycle

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Analysing potential EWIs

 We construct and test a range of potential early warning indicators building on Drehmann et al (2011)

 We select indicator variables from...

 Credit measures: Credit-to-GDP gap and real credit growth

 Asset prices: Real property and equity price gaps and real property and equity price growth

 None-core bank liabilities (Hahm, Shin, and Shin (2012)):

 GDP growth

 History of financial crises

 ...and add one new measure:

 Debt service ratio (DSR) (Drehmann and Juselius

(2012)): interest payments and repayments on debt divided by income

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 We analyse quarterly time-series data from 27 countries.

 The sample starts in 1980 for most countries and series, and at the earliest available date for the rest

 Use balanced sample

 We follow the dating of systemic banking crises in Laeven and Valencia (2012)

 We ignore crises which are driven by cross-boarder exposures

 We adjust dating for some crisis after discussions with CBs

Analysing potential EWIs (II)

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 Several of the variables display dynamics which are hard to distinguish from I(2) process

 Indicators which have performed well in the past are more persistent

→ Benefits of a non-parametric approach

Persistency

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Behaviour around systemic crises

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-5051015

-20 -16 -12 -8 -4 0 4 8 12 DSR

-2002040

-20 -16 -12 -8 -4 0 4 8 12 Credit-to-GDP gap

-40-2002040

-20 -16 -12 -8 -4 0 4 8 12 Property pr. gap

-50050100

-20 -16 -12 -8 -4 0 4 8 12 Equity pr. gap

-10-50510

-20 -16 -12 -8 -4 0 4 8 12 GDP growth

-200204060

-20 -16 -12 -8 -4 0 4 8 12 Non-core deposit ratio

-1001020

-20 -16 -12 -8 -4 0 4 8 12 Credit growth

-2002040

-20 -16 -12 -8 -4 0 4 8 12 Prop. price gr.

-50050100

-20 -16 -12 -8 -4 0 4 8 12 Equity price gr.

0.511.52

-20 -16 -12 -8 -4 0 4 8 12 history

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ROC curves for 2 year forecast horizon

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0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP DSR

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Credit-to-GDP gap

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Property pr. gap

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Equity pr. gap

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP GDP growth

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Non-core deposits ratio

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Credit growth

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Prop. price gr.

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP Equity price gr.

0.2.4.6.81ROC

0 .2 .4 .6 .8 1

FP History

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ROC curves over time

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.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

DSR AUROC .2.3.4.5.6.7.8.91

-20 -15 -10 -5 0

Horizon

Credit-to-GDP gap AUROC .2.3.4.5.6.7.8.91

-20 -15 -10 -5 0

Horizon Property pr. gap

.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

Equity pr. gap ^.2

.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon GDP growth

.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

Non-core deposits ratio AUROC .2.3.4.5.6.7.8.91

-20 -15 -10 -5 0

Horizon

Credit growth ^.2

.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon Prop. price gr.

.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

Equity price gr. AUROC .2.3.4.5.6.7.8.91

-20 -15 -10 -5 0

Horizon History

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Combining variables

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.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

Credit/GDP gap Property gap Credit\GDP gap and prop. gap

.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

Credit/GDP gap DSR Credit\GDP gap and DSR

.2.3.4.5.6.7.8.91AUROC

-20 -15 -10 -5 0

Horizon

DSR Property gap DSR and prop. gap

Credit to GDP gap and

property price gap Credit to GDP gap and DSR

DSR and property price gap

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Robustness checks

 Robust across samples

 Robust to different crisis dating

 Robust to balanced versus unbalanced samples

 Robust if partial ROC curves are used

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 We argue that EWIs need satisfy six policy requirements:

 Need to be evaluated with preferences free methodology

 Need to have right timing

 Need to be stable

 Need to be robust

 Need to be understood by policymakers

 Appliying this approch to data from 27 countries we find that:

 The DSR and the credit-to-GDP gap dominate other EWIs

 The DRS dominates at shorter horizons and the credit- to-GDP gap dominates at longer ones

Conclusion

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