identifying excessive credit growth and leverage

Intro Data Predictive model Results Out-of-sample Conclusion

Identifying excessive credit growthand leverage

Lucia Alessi 1 Carsten Detken2

1European Commission - Joint Research Centre

2European Central Bank

This work was carried out while Lucia Alessi was affiliated with the ECB. The views in this presentation arethose of the authors and do not necessarily reflect those of the ECB or the European Commission.

Alessi/Detken - Identifying excessive credit growth and leverage


Aim of the paperEarly warning indicators for macropru instruments targeting credit

Excessive credit

growth and

leverage

Macropru instruments

(CCBs, SRB, LR, LTV-LTI caps)

TRIGGER



Target variableSystemic banking crises and ‘near misses’

Banking crises dataset by Expert Group:

• based on the HoR database compiled by the MaRs

• amended in order to include:

1. only systemic banking crises associated with a domesticcredit/financial cycle

2. periods in which in the absence of policy action or of anexternal event that dampened the credit cycle a crisis as in1. would likely have occurred



Target variable

AT eeeeeeeeeeeeeeeeeeee

BE eeeeeeeeeeeeeeeeeeee

CY pppppppppppppppppeeeccccccc

DK pppppppppppppppppeeecccccccccccccccccccccccccccc pppppppppppppppppeeecccccccccccccccccccccc

EE pppppppppppppppppeeeccc eeeeeeeeeeeeeeeeeeee

FI pppppppppppppppppeeecccccccccccccccccc eeeeeeeeeeeeeeeeeeee

FR pppppppppppppppppeeecccccccccc pppppppppppppppppeeecccccccccccccccccccccc

DE pppppppppppppppppeeecccccccccccccccc eeeeeeeeeeeeeeeeeeee

GR pppppppppppppppppeeecccccccccccccccccccccccc

IE pppppppppppppppppeeecccccccccccccccccccccc

IT pppppppppppppppppeeecccccccc eeeeeeeeeeeeeeeeeeee

LV pppppppppppppppppeeecccccccceeeeeeeeeeeee

LU eeeeeeeeeeeeeeeeeeee

MT eeeeeeeeeeeeeeeeeeee

NL pppppppppppppppppeeeccccccpppppppppppppppppeeecccccccccccccccccccccc

PT pppppppppppppppppeeeccccc pppppppppppppppppeeeccccccccccccccccccccc

SK eeeeeeeeeeeeeeeeeeee

SI pppppppppppppppppeeecccccccccccc pppppppppppppppppeeecccccccccccccccccccccccc

ES pppppppppppppppppeeeccccccccccccccccccccccccccccccc pppppppppppppppppeeeccccccccccccccccccc

SE pppppppppppppppppeeecccccccccccccc pppppppppppppppppeeecccccccccceeeeeeeeeeee

GB ppppppppppppeeeccccccccc pppppppppppppppppeeecccccccccccccccc pppppppppppppppppeeecccccccccccccccccccccccccc

precrisis excluded crisis

12 1306 07 08 09 10 1100 01 02 03 04 0594 95 96 97 98 9988 89 90 91 92 9382 83 84 85 86 8776 77 78 79 80 8170 71 72 73 74 75



Early warning indicators

• Credit related indicators, based on total credit and bankcredit, credit to households and non-financial corporations,the debt service ratio and public debt

• Real estate indicators based on residential property prices,incl. valuation measures

• Market-based indicators such as the short and long terminterest rates and equity prices

• Macroeconomic variables such as real GDP growth, M3,real effective exchange rate, current account



Classification treesIs the minimum systolic blood

pressure over the initial 24 hour period > 91 ?

No Yes

High risk Is age > 62.5?

No Yes

Is sinus tachyardia present?

No Yes

High risk Low risk

Low risk



Recursive partitioning

GINI(f ) =∑i,j

Cij fi fj



The Random Forest

Bootstrap and aggregation of a multitude of trees, each grownon a randomly selected set of indicators and observations.

⇓Robust technique



Random forest performanceAUROC=0.94, out-of-sample missclassification=7%

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

False Positive Rate

Tru

e P

ositi

ve R

ate



Random Forest rankingKey indicators

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Indi

cato

r Im

port

ance

Ban

k cr

edit/

GD

P

Glo

bal c

redi

t/GD

P g

ap (

400K

)

HH

cre

dit/G

DP

Deb

t ser

vice

rat

io

Glo

bal c

redi

t gro

wth

Ban

k cr

edit/

GD

P g

ap (

400K

)

Hou

se p

rice/

inco

me

Bro

ad c

redi

t/GD

P

Hou

se p

rice

gap

(400

K)

Hou

se p

rice

grow

th

ST

rat

e

HH

Deb

t ser

vice

rat

io

Glo

bal c

redi

t/GD

P g

ap (

26K

)

Ban

k cr

edit

grow

th

NF

C c

redi

t/GD

P

Equ

ity p

rice

grow

th

M3

gap

(26K

)

Bas

el g

ap

M3

gap

(400

K)

Hou

se p

rice

gap

(26K

)

NF

C D

ebt s

ervi

ce r

atio

NF

C c

redi

t/GD

P g

ap (

400K

)

Hou

se p

rice/

rent

Gov

’t de

bt

Bro

ad c

redi

t gro

wth

HH

cre

dit/G

DP

gap

(40

0K)

NF

C c

redi

t/GD

P g

ap (

26K

)

LT g

ov’t

bond

yld

HH

cre

dit g

row

th

Hou

sing

loan

gro

wth

Cur

rent

acc

ount

Bro

ad c

redi

t/GD

P g

ap (

26K

)

Ban

k cr

edit/

GD

P g

ap (

26K

)

HH

cre

dit/G

DP

gap

(26

K)

Effe

ctiv

e ex

ch. r

ate

GD

P g

row

th

M3

grow

th

NF

C c

redi

t gro

wth



Early warning tree

Warningcrisis pr.= 1

7 obs.

HH credit/GDP

Bank credit/GDP

< 92

>54.5

> 92

<3.6 p.p.

Warningcrisis pr.= 0.62

63 obs.

No warningcrisis pr.= 0227 obs.

No warning

crisis pr.= 0.07375 obs.

Debt service ratio

> 10.6%

Debt service ratio

> 18%< 18%

No warning

crisis pr.= 018 obs.

Warningcrisis pr.= 1

5 obs.

M3 gap

< ‐0.2 p.p. > ‐0.2 p.p.

ST rate

House price gap


< 10.6%

< ‐0.5%

Bank credit growth

> 7.3%

> ‐0.5%

< 0.2 p.p. > 0.2 p.p.< 7.3%

No warningcrisis pr.= 0.25

4 obs.

Bank credit gap

>3.6 p.p. <54.5


Warningcrisis pr.= 0.9139 obs.

< 8%

House price/income

> 27 p.

> 8%

< 2 p.p. > 2 p.p.

< 27 p.

House price growth

Basel gap

House price/income

> ‐3 p.< ‐3 p.


HH credit/GDP

Equity price growth

> 36 %< 36 %

WarningNo warningcrisis pr.= 0.75

8 obs.crisis pr.= 0.08

83 obs.

Bank credit/GDP

Bank credit/GDP

House price/income


No warningcrisis pr.= 0

5 obs.

Warning Warning

WarningNo warning

< 46.7 > 46.7

> 42.4< 42.4 > 98.5< 98.5

> ‐2 p.< ‐2 p.

No warning




crisis pr.= 07 obs.



Evaluation metricsCrisis No Crisis

Signal A BNo signal C D

θ = 2/3

TPR AA+C 85%

FPR (Type II error) BB+D 4%

Type I error CA+C 15%

N2S BB+D)/

AA+C 5%

Loss θ CA+C + (1− θ) B

B+D 0.12

Usefulness min[θ;1− θ]− Loss 0.22

Rel. Usefulness Usefulnessmin[θ;1−θ] 0.65



Out-of-sample exerciseImagine you were in mid-2006

Crisis No crisisWarning FR, IE, ES, FI, IT

SE, DK, UKNo warning GR, PT, LV, AU, BE, LU, DE,

SI, NL EE, SK, MT, CY**Crisis started beyond prediction horizon.

Not classified in terminal nodes owing to lack of data.

AT BE CY DK EE FI FR DE GR IE IT LV LU MT NL PT SK SI ES SE GB2006Q3 x x x x x x x x2006Q4 x x x x x x x x2007Q1 x x x x x x x x2007Q2 x x x x x x x x2007Q3 x x x x x x x x2007Q4 x x x x x x x x x2008Q1 x x x x x x x x x2008Q2 x x x x x x x x x2008Q3 x x x x x



Conclusion

• The Random Forest/Early Warning methodology canbecome a useful quantitative tool to:

• spur discussion on country risks• provide information on the most appropriate policy

instrument to address identified vulnerabilities

• Additional relevant (potentially country specific) informationcan be included


identifying excessive credit growth and leverage

Economy & Finance