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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? 4

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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

Incr

ease

capi

tal r

equi

rem

ents

or

prov

ision

s

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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Informative signal Uninformative signal Fully informative signal

False positive rate

True positive rate

1

1

W1 W2

False positive rate

True positive rate

1

1

W1

W2

False positive rate

True positive rate

1

1

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

0)( dFPFPROCAUROC

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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 16

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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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-50

510

15

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

DSR

-20

020

40

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

Cr edit - t o- G DP gap

-40

-20

020

40

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

Pr oper t y pr . gap

-50

050

100

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

Equit y pr . gap

-10

-50

510

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

G DP gr owt h

-20

020

4060

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

Non- cor e deposit r at io

-10

010

20

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

Cr edit gr owt h-2

00

2040

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

Pr op. pr ice gr .

-50

050

100

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

Equit y pr ice gr .

0.5

11.

52

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

hist or y

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ROC curves for 2 year forecast horizon

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0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

DS R

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

Cre d it-to -GDP g a p

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

Pro p e rty p r. g a p

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

Eq u ity p r. g a p

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

GDP g ro wth

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

No n -c o re d e p o s its ra tio

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

Cre d it g ro wth0

.2.4

.6.8

1R

OC

0 .2 .4 .6 .8 1F P

Pro p . p ric e g r.

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

Eq u ity p ric e g r.

0.2

.4.6

.81

RO

C

0 .2 .4 .6 .8 1F P

His to ry

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ROC curves over time

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.2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

DSR .2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Cr edit - t o- G DP gap .2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Pr oper t y pr . gap

.2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Equit y pr . gap

.2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

G DP gr owt h

.2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Non- cor e deposit s r at io .2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Cr edit gr owt h .2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Pr op. pr ice gr .

.2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Equit y pr ice gr . .2.3

.4.5

.6.7

.8.9

1AU

ROC

-2 0 -1 5 -1 0 -5 0Hor iz on

Hist or y

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Combining variables

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.2.3

.4.5

.6.7

.8.9

1A

UR

OC

-20 -15 -10 -5 0Ho ri z o n

Credit / GDP gap Proper t y gap Credit \ GDP gap and prop. gap

.2.3

.4.5

.6.7

.8.9

1A

UR

OC

-20 -15 -10 -5 0Ho ri z o n

Credit / GDP gap DSR Credit \ GDP gap and DSR

.2.3

.4.5

.6.7

.8.9

1A

UR

OC

-20 -15 -10 -5 0Ho ri z o n

DSR Proper t y 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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