restricted improving early warning indicators for banking crises – satisfying policy requirements...
TRANSCRIPT
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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
cap
ital r
equi
rem
ents
or
prov
isio
ns
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
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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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-5
05
10
15
- 20- 16- 12 - 8 - 4 0 4 8 12
DSR
-2
00
20
40
- 20- 16- 12 - 8 - 4 0 4 8 12
Cr edit - t o- G DP gap
-4
0-
20
02
04
0
- 20- 16- 12 - 8 - 4 0 4 8 12
Pr oper t y pr . gap
-5
00
50
10
0
- 20- 16- 12 - 8 - 4 0 4 8 12
Equit y pr . gap
-1
0-
50
51
0
- 20- 16- 12 - 8 - 4 0 4 8 12
G DP gr owt h
-2
00
20
40
60
- 20- 16- 12 - 8 - 4 0 4 8 12
Non- cor e deposit r at io
-1
00
10
20
- 20- 16- 12 - 8 - 4 0 4 8 12
Cr edit gr owt h-
20
02
04
0
- 20- 16- 12 - 8 - 4 0 4 8 12
Pr op. pr ice gr .
-5
00
50
10
0
- 20- 16- 12 - 8 - 4 0 4 8 12
Equit y pr ice gr .
0.
51
1.
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
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
DSR .2.3
.4.5
.6.7
.8.9
1A
UR
OC
-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
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
Pr oper t y pr . gap
.2.3
.4.5
.6.7
.8.9
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
Equit y pr . gap
.2.3
.4.5
.6.7
.8.9
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
G DP gr owt h
.2.3
.4.5
.6.7
.8.9
1A
UR
OC
-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
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
Cr edit gr owt h .2.3
.4.5
.6.7
.8.9
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
Pr op. pr ice gr .
.2.3
.4.5
.6.7
.8.9
1A
UR
OC
-2 0 -1 5 -1 0 -5 0Hor iz on
Equit y pr ice gr . .2.3
.4.5
.6.7
.8.9
1A
UR
OC
-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 Propert 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 Propert 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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