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Excessive Credit Growth and Countercyclical Capital Buffers in Basel III An Empirical Evidence from Central and East European Countries Adam Geršl Jakub Seidler Česká a světová ekonomika po globální finanční krizi International Scientific Conference VŠFS Prague, CNB, 25 November 2011 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 systemwide 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 2 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 Countercyclical capital buffer • for calculation of the long-term (% of RWA as a function of credit-to-GDP gap in pps) trend, the Basel committee -1,4 suggests -1,3 using the Hodrick-1,2 Prescott -1,1filter with a high -1 smoothing parameter -0,9 -0,8 (lambda=400,000) -0,7 -0,6 • buffer set as a function of the -0,5 -0,4 credit-to-GDP gap -0,3 • role for-0,2 macroprudential authority -0,1 0 to assess, decide and 0,1 0,2 apply/remove 4 3,5 3 2,5 2 1,5 1 0,5 0 -4 -2 0 2 4 6 8 10 12 14 Credit-to-GDP gap (in %) 3 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 4 Buffer as a countercyclical instrument Credit dynamics (e.g. y-o-y growth) period of financial exuberance period of financial distress CCB set to zero again time CCB set at maximum 2,5 % CCB set to zero turning point (start of crisis): credit growth falls, lending conditions tighten 5 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) 6 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) 0.4 Tools to limit credit growth Monetary policy tools - interest rate increases LT 0.35 LV RO - reserve requirements Regulatory measures 0.3 - higher risk weights/capital charges EE 0.25 BG SI - restrictions on LTV/LTI CZ 0.2 - provisioning rate SK 0.15 - tight regulation on large exposures PL - rules on collateral valuation HU 0.1 Administrative measures 0.05 - quantitative restrictions on credit growth - eligibility criteria for borrowers 0 0 2 4 6 8 10 12 - tax treatment of loan-related payments - guidelines and recommendations Source: IMF, national authorities' websites 7 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) 250% CEE countries 200% 150% 100% 50% 0% CY IE NL GR BE FI Source: IMF IFS, authors' calculations EE LV SI CZ SK PL RO • may be underestimated (only bank credit captured, but in the CEE also cross-border and nonbank 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) 8 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 (%) Development of credit to GDP ratio in CEE countries (%) 120 100 90 100 80 70 80 60 60 50 40 40 30 20 20 10 0 1998Q1 1999Q3 2001Q1 2002Q3 2004Q1 2005Q3 2007Q1 2008Q3 Hungary Poland Romania Slovenia 0 1998Q1 1999Q3 2001Q1 2002Q3 2004Q1 2005Q3 2007Q1 2008Q3 Bulgaria Estonia Source: IMF IFS, authors' calculations Source: IMF IFS, authors' calculations Latvia Lithuania 9 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 (%) Stock of bank credit to the real sector in the Czech Republic (in CZK bil) 80 2000 70 1800 1600 60 1400 50 1200 40 1000 30 800 600 20 400 10 200 0 1998Q1 1999Q3 2001Q1 2002Q3 2004Q1 2005Q3 2007Q1 2008Q3 Czech Republic Source: IMF IFS, authors' calculations Slovak Republic 0 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 Non-financial corporations Source: Czech National Bank Households 10 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 Credit gaps in the Czech Republic with alternative denominators (%) Bank credit to the real sector in relative terms (%) 70 20 60 15 50 10 40 5 30 0 20 -5 10 -10 0 1996 1998 2000 Source: CNB, CZSO 2002 2004 2006 2008 Credit-to-GDP Credit-to-financial-assets Credit-to-assets 2010 -15 03/98 03/00 03/02 03/04 03/06 03/08 Credit-to-GDP gap Credit-to-GDP gap (actual period estimation) Credit-to-financial-assets gap Credit-to-assets gap 03/10 11 Source: CNB, authors' calculations 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) 12 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 %) Credit to GDP for similar level of economic development (GDP per capital in 2005 USD = 14 ths USD; in %) 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 CZ (2009) IT (1986) NL (1984) AT (1984) Source: IMF IFS, authors' calculations FR (1985) ES (1990) DE (1983) Latvia (2008) IT (1984) ES (1988) Source: IMF IFS, authors' calculations AT (1985) DE (1985) 13 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 14 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 15 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 Latvia Estonia 20% 50% 40% 15% 40% 30% 10% 20% 30% 10% 5% 20% 0% 0% 10% -5% -10% 0% -10% -20% -15% -10% -20% -20% -40% -25% 2000q1 2002q1 2004q1 2006q1 2008q1 -30% 2000q1 2001q4 2003q3 2005q2 2007q1 2008q4 -50% 2000q1 2001q4 2003q3 2005q2 2007q1 2008q4 HPgap OUTgap -30% HPgap OUTgap HPgap OUTgap 16 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 Countercyclical capital buffer (% of RWA) Out-of-sample HP filter Out-of-sample 10.8 2.5 2.5 -15.0 2.4 0.0 27.9 1.0 2.5 -8.3 1.5 0.0 19.6 0.0 2.5 -10.7 0.0 0.0 -23.3 0.3 0.0 -27.3 1.3 0.0 -22.8 1.3 0.0 5.5 1.1 1.1 Credit-to-GDP gap (%) HP filter BG 11.4 CZ 9.5 EE 5.3 LT 6.9 LV 1.0 HU -1.4 PL 3.0 RO 6.1 SK 6.1 SI 5.4 Source: authors' calculations 17 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) Credit-to-GDP gap via out-of-sample and change in RoE (gap in pps; change in RoE of banking sector in pps) 10 CZ 13 0 RO 12 -40.0 BG CZ -20.0 RO 0.0 HU PL -10 SI 20.0 40.0 BG 11 SK SK PL -20 LV 10 LT HU -30 EE SI 9 -40 LV 8 -50 LT 7 6 -40.0 -20.0 0.0 Source: IMF, authors' calculations 20.0 40.0 -60 -70 Source: IMF, authors' calculations EE 18 Thank your for your attention! 19 Annex: comparison of HP and out-of-sample methods Bulgaria Slovakia 20% 20% 15% 10% 10% 0% 5% 0% -10% -5% -20% -10% -15% -30% -20% -40% -25% -50% 2000q1 2001q4 2003q3 2005q2 2007q1 2008q4 HPgap -30% 2000q1 2002q1 2004q1 2006q1 2008q1 OUTgap Lithuania HPgap OUTgap Hungary 20% 15% 10% 10% 5% 0% 0% -10% -5% -10% -20% -15% -30% -20% -25% -40% -30% -50% 2000q1 2001q4 2003q3 2005q2 2007q1 2008q4 HPgap OUTgap -35% 2000q1 2001q4 2003q3 2005q2 2007q1 2008q4 HPgap OUTgap 20 Annex: comparison of HP and out-of-sample methods Poland Romania 15% 10% 10% 0% 5% -10% 0% -5% -20% -10% -30% -15% -20% -40% -25% -50% -30% -35% 2000q1 2001q4 2003q3 2005q2 2007q1 2008q4 HPgap OUTgap -60% 2000q1 2002q1 2004q1 2006q1 2008q1 2010q1 HPgap OUTgap Slovenia 20% 15% 10% 5% 0% -5% -10% -15% -20% -25% -30% 2000q1 2002q1 2004q1 2006q1 2008q1 2010q1 HPgap OUTgap 21 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). 22