Excessive Credit Growth and Countercyclical Capital Buffers in Basel IIIAn Empirical Evidence from Central and East European Countries

Adam Geršl

Jakub Seidler

Česká a světová ekonomika po globální finanční kriziInternational Scientific Conference VŠFS

Prague, CNB, 25 November 2011

2

Countercyclical capital buffer in Basel III

• should protect the banking sector from the credit cycle • act against procyclicality in the financial system• protect the banking sector from periods of excess aggregate credit

growth that have often been associated with the build up of system-wide risk

• the aim is to ensure that the banking sector in aggregate has the capital on hand to help maintain the flow of credit in the economy when the broader financial system experiences stress after a period of excess credit growth

• may help to lean against the build-up phase of the cycle• raising the cost of credit, and therefore dampening its demand, when

there is evidence that the stock of credit has grown to excessive levels relative to the benchmarks of past experience

• this potential moderating effect on the build-up phase of the credit cycle should be viewed as a positive side benefit, rather than the primary aim of the countercyclical capital buffer regime

3

The role of credit-to-GDP gap

• the common reference guide is based on the aggregate private sector credit-to-GDP gap

• a gap between the observed value and the calculated long-term trend of private sector credit to GDP

• for calculation of the long-term trend, the Basel committee suggests using the Hodrick-Prescott filter with a high smoothing parameter (lambda=400,000)

• buffer set as a function of the credit-to-GDP gap

• role for macroprudential authority to assess, decide and apply/remove

Countercyclical capital buffer(% of RWA as a function of credit-to-GDP gap in pps)

-1,4-1,3-1,2-1,1

-1-0,9-0,8-0,7-0,6-0,5-0,4-0,3-0,2-0,1

00,10,2

0

0,5

1

1,5

2

2,5

3

3,5

4

-4 -2 0 2 4 6 8 10 12 14

Credit-to-GDP gap (in %)

44

The role of a macroprudential authority regarding CCB

• to monitor credit and its dynamics (and potentially other indicators) and make assessments of whether system-wide risks are being built up

• based on this assessment, to decide whether the CCB requirement should be imposed (set above the zero value)

• to apply judgment to determine whether the CCB should increase or decrease over time (within the range of zero to 2.5% of risk weighted assets, in very strong credit booms even above 2.5%)

• to be prepared to remove the requirement on a timely basis if the system-wide risk crystallizes

555

Buffer as a countercyclical instrument

turning point (start of crisis): credit growth falls, lending conditions tighten

period of financial exuberance

time

Credit dynamics (e.g. y-o-y growth)

period of financial distress

CCB set to zero

CCB set to zero again

CCB set at maximum 2,5 %

6

Countercyclical capital buffer in Europe (CRD IV)

• a series of discussions and consultations in the EU to• appropriately implement Basel III in the EU• and, at the same time, reflect a unique nature of EU financial

markets (large integration, common rules = single rulebook)• the discussions are still going on (draft proposal for CRR/CRD IV

published in July 2011)• open issues related to the buffer

• cyclical versus structural buffer• voluntary versus obligatory cross-border reciprocity • national discretion versus single rulebook• role of European Systemic Risk Board (ESRB) in setting

guidance (ex ante agreement within the ESRB on indicators versus ex post reaction by ESRB via warnings and recommendations)

7

Converging EU countries from the CEE region

• CEE countries experienced rapid credit growth in the pre-crisis period 2002–2007

• policymakers in some countries assessed the credit boom as excessive, applying a number of tools to limit it (with mixed evidence as to their effectiveness)

Credit growth and number of tools applied to limit credit boom(horizontal axes: number of tools; vertical axes - average y-o-y real credit growth 2005-2007)

Source: IMF, national authorities' websites

HU

PLSK

CZ

SIBGEE

ROLT LV

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2 4 6 8 10 12

Tools to limit credit growth

Monetary policy tools

- interest rate increases

- reserve requirements

Regulatory measures

- higher risk weights/capital charges

- restrictions on LTV/LTI

- provisioning rate

- tight regulation on large exposures

- rules on collateral valuation

Administrative measures

- quantitative restrictions on credit growth

- eligibility criteria for borrowers

- tax treatment of loan-related payments

- guidelines and recommendations

8

Particular features of credit boom in the CEE countries

• despite rapid credit growth, the level of credit still relatively low in many CEE countries in the pre-crisis year 2007 if compared to the rest of the EU; however,

• some CEE countries already aproached levels observed in the euro area

Bank credit to the private sector in selected EU countries(as % of GDP)

Source: IMF IFS, authors' calculations

0%

50%

100%

150%

200%

250%

CY IE NL GR BE FI EE LV SI CZ SK PL RO

CEE countries

• may be underestimated (only bank credit captured, but in the CEE also cross-border and non-bank credit plays a role)

• FX lending prevalent in several CEE countries (but not in all)

• credit booms funded via external borrowing of domestic banks, usually from parent companies (foreign ownership of banking sectors in the CEE)

9

CEE countries as a case study to try out the buffer calculation

• application of the suggested credit-to-GDP guide challenging for CEE countries

• short time-series (Basel recommends 20 years of quarterly data)• high stable rise of credit growth is incorporated in the trend

(convergence in credit to GDP)Development of credit to GDP ratio in CEE countries(%)

Source: IMF IFS, authors' calculations

0

10

20

30

40

50

60

70

80

90

100

1998Q1 1999Q3 2001Q1 2002Q3 2004Q1 2005Q3 2007Q1 2008Q3

Hungary Poland Romania Slovenia

Development of credit to GDP ratio in CEE countries(%)

Source: IMF IFS, authors' calculations

0

20

40

60

80

100

120

1998Q1 1999Q3 2001Q1 2002Q3 2004Q1 2005Q3 2007Q1 2008Q3

Bulgaria Estonia Latvia Lithuania

10

CEE countries and buffer calculation

• initial undershooting and catching up hypothesis• banking sector restructuring (bad assets, bank privatization) and

changes in the composition of credit to the private sector• end-point bias as an obstacle to conduct of macroprudential policy

Development of credit to GDP ratio in CEE countries(%)

Source: IMF IFS, authors' calculations

0

10

20

30

40

50

60

70

80

1998Q1 1999Q3 2001Q1 2002Q3 2004Q1 2005Q3 2007Q1 2008Q3

Czech Republic Slovak Republic

Stock of bank credit to the real sector in the Czech Republic(in CZK bil)

Source: Czech National Bank

0

200

400

600

800

1000

1200

1400

1600

1800

2000

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Non-financial corporations Households

11

The Czech Republic

• using HP filter with recommended lambda would indicate excess credit growth (since 2003 if estimated continuously)

• holds even if other reasonable denominators are used (assets)• discretion of policymakers needed

Bank credit to the real sector in relative terms(%)

Source: CNB, CZSO

0

10

20

30

40

50

60

70

1996 1998 2000 2002 2004 2006 2008 2010Credit-to-GDPCredit-to-financial-assetsCredit-to-assets

Credit gaps in the Czech Republic with alternative denominators(%)

Source: CNB, authors' calculations

-15

-10

-5

0

5

10

15

20

03/98 03/00 03/02 03/04 03/06 03/08 03/10

Credit-to-GDP gap

Credit-to-GDP gap (actual period estimation)

Credit-to-financial-assets gap

Credit-to-assets gap

12

Methods used to find equilibrium credit

Frequently used• Detrending data (HP – filter)

• Hilbers et al. (2005)• VAR / Cointegration for individual country

• Hoffman (2001)• Out-of-sample panel regression

• Cottareli et al. (2005), Égert et al. (2006)• Panel VAR

• Eller et al. (2010)

Less frequently used – not used so far for excess credit• Kalman filter, e.g. Bacchetta et al. (1999)• Structural models / DSGE, e.g. Gerali et al. (2010)

13

Historical comparison

• indicates that the Czech Republic has lower level of credit than selected core EU countries when at similar level of economic development (in the 1980s)

• other features of credit growth in the Czech Republic also do not indicate the build up of system-wide risk (no FX loans to households; no external funding; high deposit-to-loan ratio; low LTV ratios)

• contrasts with Latvia that had comparatively much higher stock of credit in 2008, several „dangerous“ features of credit boom and lower level of GDP per capita

Credit to GDP for similar level of economic development(GDP per capital in 2005 USD = 17 ths USD; in %)

Source: IMF IFS, authors' calculations

0

10

20

30

40

50

60

70

80

90

100

CZ(2009)

IT(1986)

NL(1984)

AT(1984)

FR(1985)

ES(1990)

DE(1983)

Credit to GDP for similar level of economic development(GDP per capital in 2005 USD = 14 ths USD; in %)

Source: IMF IFS, authors' calculations

0

10

20

30

40

50

60

70

80

90

100

Latvia(2008)

IT (1984) ES (1988) AT (1985) DE (1985)

14

The „out-of-sample“ method

• a way how to form judgment about the sustainable level of credit in the economy, based on (see Backe, Egert and Zumer 2006; Kiss, Nagy and Vonnák 2006):

• regressing the credit to GDP on a range of economic fundamentals (GDP per capita; households consumption; inflation etc.), using data for developed countries

• applying the estimated elasticities „out of sample“, i.e. on CEE countries to calculate „equilibrium credit“

• in contrast to HP filter, the out-of-sample method takes into account the economic fundamentals influencing the level of credit in the economy

15

Model specification

• Dynamic nonstationary heterogenous panel estimator

• Pesaran et al. (1999)• Short-run effect different for cross-sections • The same long-run cointegration relationship for all countries• SR effect:

• inflation (-), • ∆ (consumption to gdp)(+)

• LR effect:• consumption to gdp(+),• gdp per capita (+),

- other determinants tested

16

The „out-of-sample“ method: results

• the out-of-sample estimation leads to different credit-to-GDP gaps• Czech Republic does not seem to have been in excessive credit situation

in 2009, while Estonia and Latvia do• for results for other countries see Annex

Czech Republic

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

2000q1 2002q1 2004q1 2006q1 2008q1

HPgap OUTgap

Estonia

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

2000q1 2001q4 2003q3 2005q2 2007q1 2008q4

HPgap OUTgap

Latvia

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

2000q1 2001q4 2003q3 2005q2 2007q1 2008q4

HPgap OUTgap

17

The „out-of-sample“ method

• different estimations of „gap“ lead to different levels of buffer• to be fair, the out-of-sample method also has a number of drawbacks

• fundamental variables selection (housing prices seem to be a relevant variable, but can themselves suffer from bubbles)

• different fundamentals in out-of-sample countries and countries of our interest at the current stage of development

• estimation method suffers by losing country-specific constant

HP filter Out-of-sample HP filter Out-of-sampleBG 11.4 10.8 2.5 2.5CZ 9.5 -15.0 2.4 0.0EE 5.3 27.9 1.0 2.5LT 6.9 -8.3 1.5 0.0LV 1.0 19.6 0.0 2.5HU -1.4 -10.7 0.0 0.0PL 3.0 -23.3 0.3 0.0RO 6.1 -27.3 1.3 0.0SK 6.1 -22.8 1.3 0.0SI 5.4 5.5 1.1 1.1Source: authors' calculations

Credit-to-GDP gap (%)Countercyclical capital buffer

(% of RWA)

18

Countries above equilibrium credit

• however, it seems that for CEE countries, the out-of-sample method better predicts the problem countries

• empirical evidence shows that the four countries identified as being above equilibrium credit (LV, BG, EE, SI) and the two close to the border (HU and LT) did not show particularly high Tier 1 capital ratios before crisis in 2008 (except Bulgaria) and some of them experienced relatively high drop in RoE of banks

Credit-to-GDP gap via out-of-sample and Tier 1 ratio in 2008(gap in pps; Tier 1 capital ratio in 2008)

Source: IMF, authors' calculations

EESI

BGCZ

LT

LV

HU

PL

RO

SK

6

7

8

9

10

11

12

13

-40.0 -20.0 0.0 20.0 40.0

Credit-to-GDP gap via out-of-sample and change in RoE(gap in pps; change in RoE of banking sector in pps)

Source: IMF, authors' calculations

EE

SIBG

CZ

LT

LV

HUPLRO

SK

-70

-60

-50

-40

-30

-20

-10

0

10

-40.0 -20.0 0.0 20.0 40.0

19

Thank your for your attention!

20

Annex: comparison of HP and out-of-sample methods

Bulgaria

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

2000q1 2001q4 2003q3 2005q2 2007q1 2008q4

HPgap OUTgap

Slovakia

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

2000q1 2002q1 2004q1 2006q1 2008q1

HPgap OUTgap

Hungary

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

2000q1 2001q4 2003q3 2005q2 2007q1 2008q4

HPgap OUTgap

Lithuania

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

2000q1 2001q4 2003q3 2005q2 2007q1 2008q4

HPgap OUTgap

21

Annex: comparison of HP and out-of-sample methods

Poland

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

2000q1 2001q4 2003q3 2005q2 2007q1 2008q4

HPgap OUTgap

Romania

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

2000q1 2002q1 2004q1 2006q1 2008q1 2010q1

HPgap OUTgap

Slovenia

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

2000q1 2002q1 2004q1 2006q1 2008q1 2010q1

HPgap OUTgap

22

References

• Bacchetta, P., Gerlach, S. (1997): Consumption and credit constraints: International evidence, Journal of Monetary Economics, Volume 40, Issue 2, Monetary Policy and Financial Markets, October 1997, pp. 207–238.

• Boissay, F., Calvo-Gonzales, O. and Kozluk, T. (2006): Is Lending in Central and Eastern Europe Developing too Fast?, Finance and Consumption Workshop presentation, June 2006.

• Brzoza-Brzezina, M. (2005): Lending Booms in Europe’s Periphery: South-Western Lessons for Central Eastern Members. EconWPA, February.

• Calza, A., Gartner, Ch. and Sousa, J. (2003): Modelling the Demand for Loans to the Private Sector in the Euro Area , Applied Economics, Vol. 35, No. 1, pp. 107–117.

• Cottarelli, C., Giovanni D. and Vladkova-Hollar, I. (2005): Early birds, late risers, and sleeping beauties: Bank credit growth to the private sector in Central and Eastern Europe and in the Balkans, Journal of Banking & Finance 29, no. 1 (January): 83–104.

• Égert, B., Backé, P. and Zumer T. (2006): Credit growth in Central and Eastern Europe - new (over)shooting stars?, European Central Bank WP, No. 687, October 2006.

• Eller, M., Frömmel, M., and Srzentic, N. (2010): Private Sector Credit in CESEE: Long-Run Relationships and Short-Run Dynamics, Focus on European Economic Integration, no. 2.

• Gerali, A. & Neri, S., Sessa, L. and Signoretti, F. (2010): Credit and banking in a DSGE model of the euro area, Working papers No. 740, Bank of Italy, Economic Research Department.

• Hilbers, P., Otker-Robe, I., Pazarbasioglu, C. and Johnsen, G. (2005): Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies, IMF Working Paper, Vol. 151, No. 5, pp. 1–59.

• Hofmann, B. (2001): The determinants of private sector credit in industrialized countries: Do property prices metter? BIS Working Paper 108.

• Kiss, G., Nagy, M. and Vonnák, B. (2006): Credit Growth in Central and Eastern Europe: Convergence or Boom?, MNB Working Papers 2006/10, Magyar Nemzeti Bank (The Central Bank of Hungary).