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Manuel Buchholz. Caps on banks’ leverage and domestic credit after the crisis

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Open Seminar
Eesti Pank
September 28, 2015

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Manuel Buchholz. Caps on banks’ leverage and domestic credit after the crisis

  1. 1. Caps on banks’ leverage and domestic credit after the crisis Manuel Buchholz1,2 1Visiting Researcher at Eesti Pank 2Halle Institute for Economic Research (IWH) Open Seminar Eesti Pank September 28, 2015 The views expressed in the presentation are those of the author and do not necessarily reflect the views of Eesti Pank (Bank of Estonia) or the IWH. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 1 / 29
  2. 2. Motivation – The (not so) new era of macro pru In the wake of the Global Financial Crisis, macroprudential policy has become a key element of the ongoing regulatory debate In several (advanced) economies, macroprudential policy instruments have recently been implemented or are to be implemented in the near future (loan-to-value ratios, countercyclical capital buffers) Shift from microprudential to macroprudential perspective of financial supervision: Focus is on safeguarding stability of financial system as a whole and not only of individual institutions Many emerging market (and few advanced) countries have already implemented macroprudential tools in the past These experiences allow studying the effects of macroprudential policy on financial and real outcomes M. Buchholz Leverage caps & domestic credit Sep 28, 2015 2 / 29
  3. 3. Table of contents 1 Introduction Motivation Macroprudential policy Research question Related literature 2 Data Caps on banks’ leverage Real credit growth 3 Empirical specification and estimation results Empirical specification Estimation results 4 Robustness 5 Concluding remarks M. Buchholz Leverage caps & domestic credit Sep 28, 2015 3 / 29
  4. 4. The aim and effectiveness of macroprudential policy The aims of macroprudential policy Safeguarding stability of the financial system by means of reducing the build-up of systemic risks Ultimate target: Making financial crises less likely and reduce their adverse impact on the real economy Intermediate target: Smoothing the financial cycle by means of reducing (excessive) credit growth in boom times and stabilizing provision of credit in times of financial downturns (countercyclicality) Results of empirical research suggest that macroprudential policy is effective in reducing credit growth in boom times Evidence is less clear regarding the potentially stabilizing role of macroprudential policy during financial downturns ⇒ Focus of this paper M. Buchholz Leverage caps & domestic credit Sep 28, 2015 4 / 29
  5. 5. A specific tool: Caps on banks’ leverage A cap on leverage implies that banks have to hold a minimum amount of capital with respect to their total (non-risk-weighted) assets Such a leverage ratio is currently tested under the Basel III framework and set at 3% to complement the already existing capital requirements incorporating risk weights (BCBS 2014) The goals prevent the build-up of excessive leverage in the financial system function as a backstop for risk-weighted regulatory capital ratios Possible channel how the cap might stabilize lending after the crisis: Higher pre-crisis capital buffer might relax the binding constraint due to risk-weighted capital requirements in times of financial downturn M. Buchholz Leverage caps & domestic credit Sep 28, 2015 5 / 29
  6. 6. Research question Did the implementation of a leverage cap prior to the crisis affect provision of credit to the private sector after the crisis? Endogeneity: Pre-crisis implementation of leverage cap is not random but rather conditional on several (potentially unobserved) economic factors Empirical strategy: difference-in-differences approach The Global Financial Crisis affected the capability of banks to provide credit The years after the crisis are an example for a period of financial downturn Investigate if real credit growth after the crisis relative to the pre-crisis period differs for countries which had implemented a cap on banks’ leverage prior to the crisis M. Buchholz Leverage caps & domestic credit Sep 28, 2015 6 / 29
  7. 7. Overview of related literature (1) Empirical studies based on individual countries Aiyvar et al. (2014a): Capital surcharges lead to lower lending by UK-owned banks and foreign subsidiaries; leakage due to increased credit provision by foreign branches Buch et al. (2014): Banks affected by the German bank levy implemented in 2011 reduce lending and increase deposit rates Jiménez et al. (2014): Dynamic provisioning regulation on Spanish banks effective in smoothing credit cycle Danisewicz et al. (2015): Lending by foreign subsidiaries and branches located in the UK differs significantly in response to the implementation of macroprudential policy in their home countries M. Buchholz Leverage caps & domestic credit Sep 28, 2015 7 / 29
  8. 8. Overview of related literature (2) Multi-country studies Claessens et al. (2013): Macroprudential policy decreases banks’ leverage and asset growth during boom times; limited evidence for stabilizing effects of countercyclical instruments in downturns Aiyvar et al. (2014b): Capital surcharges lead to slowdown in growth of cross-border credit by UK banks Cerutti et al. (2015): Macroprudential policy is effective in dampening real credit growth but works less well in busts M. Buchholz Leverage caps & domestic credit Sep 28, 2015 8 / 29
  9. 9. Table of contents 1 Introduction Motivation Macroprudential policy Research question Related literature 2 Data Caps on banks’ leverage Real credit growth 3 Empirical specification and estimation results Empirical specification Estimation results 4 Robustness 5 Concluding remarks M. Buchholz Leverage caps & domestic credit Sep 28, 2015 9 / 29
  10. 10. Data – Caps on banks’ leverage Source: Cerutti et al. (2015) – based on the IMF survey on Global Macroprudential Policy Instruments (GMPI) – provide the year of implementation of the cap but not its intensity Country Year of implementation 1 Value of leverage cap 2 Advanced Canada ≤ 2000 5% United States ≤ 2000 3% / 4% Emerging Chile ≤ 2000 5% Ecuador 2001 n.a. Jordan 2003 6% Paraguay ≤ 2000 8% / 12% Saudi Arabia ≤ 2000 6.25% St. Kitts and Nevis ≤ 2000 n.a. 1 Source is Cerutti et al. (2015). 2 Measured as the implied minimum ratio of capital to total assets. For illustrative purposes only. Correctness cannot be guaranteed because sources include non-official documents. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 10 / 29
  11. 11. Data – Real credit growth before and after crisis 0510152025 Realcredittoprivatesector(growthratein%) 2002 2004 2006 2008 2010 2012 2014 Year Cap on leverage: no Cap on leverage: yes Source: Computation of growth rates of real credit to the private sector based on data from the Other Depository Corporations Survey (IMF) and the World Development Indicators (World Bank). Growth rates were winsorized at the 1% and 99% quantiles. Sample is based on the estimation sample of 69 countries. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 11 / 29
  12. 12. Data – Summary statistics Variable Country group Observations Mean Standard deviation Min Max Leverage cap: yes 89 11.62 11.44 -14.10 60.74 Leverage cap: no 730 13.81 16.86 -14.10 78.34 Total 819 13.57 16.37 -14.10 78.34 Real GDP growth rate (in %) Leverage cap: yes 89 3.87 3.35 -5.60 14.22 Leverage cap: no 730 3.30 3.76 -11.77 18.23 Total 819 3.36 3.72 -11.77 18.23 Monetary policy rate (in %) Leverage cap: yes 89 4.56 3.40 0.13 15.36 Leverage cap: no 730 5.95 9.97 0.08 150.00 Total 819 5.80 9.49 0.08 150.00 CPI inflation rate (in %) Leverage cap: yes 89 3.99 3.04 -0.73 14.99 Leverage cap: no 730 4.77 7.01 -1.09 109.59 Total 819 4.68 6.70 -1.09 109.59 Leverage cap: yes 89 53.39 28.57 14.62 134.99 Leverage cap: no 725 65.06 40.55 4.20 172.41 Total 814 63.78 39.57 4.20 172.41 Leverage cap: yes 82 3.01 1.31 1.00 5.00 Leverage cap: no 677 1.38 1.27 0.00 6.00 Total 759 1.55 1.37 0.00 6.00 Leverage cap: yes 82 0.34 0.57 0.00 2.00 Leverage cap: no 677 0.34 0.63 0.00 2.00 Total 759 0.34 0.62 0.00 2.00 Capital ratio (in %) Leverage cap: yes 63 8.34 3.78 0.00 14.03 Leverage cap: no 691 9.91 4.26 0.00 22.56 Total 754 9.77 4.24 0.00 22.56 Financial institutions-targeted macroprudential index (0-10) Borrower-targeted macroprudential index (0-2) Private credit-to-GDP ratio (in %) Real credit to the private sector (growth rate, in %) All variables were winsorized at the 1% and 99% quantiles. Sample is based on the estimation sample of 69 countries. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 12 / 29
  13. 13. Table of contents 1 Introduction Motivation Macroprudential policy Research question Related literature 2 Data Caps on banks’ leverage Real credit growth 3 Empirical specification and estimation results Empirical specification Estimation results 4 Robustness 5 Concluding remarks M. Buchholz Leverage caps & domestic credit Sep 28, 2015 13 / 29
  14. 14. Empirical specification – Difference-in-differences The effect of the leverage cap in the difference-in-differences setup: ∆ log RealCreditit = αi + γt + β[DLEV × DPostCrisis] + Xitδ + εit ∆ log RealCreditit: Growth rate of real credit (domestic currency) DLEV : Leverage cap dummy (0/1); 1 if implemented prior to 2008 DPostCrisis: Post-crisis dummy (0/1); 1 from 2009 on Xit: Control variables i: country, t: year, αi: country fixed effects, γt: time fixed effects β: Effect of leverage cap implemented prior to the crisis on post-crisis credit growth; stabilizing role of leverage cap if β > 0 Estimation period: 2002 to 2014; Frequency: yearly; Countries: 73 emerging and advanced countries or less, 69 in baseline specification (depending on availability of controls) M. Buchholz Leverage caps & domestic credit Sep 28, 2015 14 / 29
  15. 15. Identifying assumptions Common trends: Same pattern of real credit growth after the crisis in both groups of countries if none of them had implemented the leverage cap prior to the crisis Assumption not testable but pre-treatment analysis can shed light on plausibility The crisis has to be an exogenous event in the sense that it did not affect implementation of leverage cap through anticipation effects Viewing the financial crisis as a generally unanticipated event appears plausible Narrative evidence that leverage caps where implemented primarily for microprudential reasons, i.e. to reduce leverage at the individual bank level (Bordeleau et al. 2009, Lim et al. 2011) The caps on leverage might have been implemented for different reasons such as a general preference for financial stability, quality of institutions, etc. The difference-in-differences approach allows to control for such country-specific characteristics even if they are unobservable M. Buchholz Leverage caps & domestic credit Sep 28, 2015 15 / 29
  16. 16. Test for pre-treatment differential effects -100102030 -2 -1 0 Year since crisis Coefficient 95% Confidence interval Graph shows the difference-in-difference estimate over time. The crisis dummy is put to one already three years before the actual post-crisis period, i.e. from 2006 on. Point estimates are surrounded by 95% confidence intervals. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 16 / 29
  17. 17. The effect of the leverage cap on real credit growth Dependent variable: Real credit growth (%) (1) (2) (3) DLEV x DPostCrisis 7.044** 6.659*** 6.029*** (2.966) (2.181) (2.123) Real GDP growth (%) 1.568*** 1.638*** (0.236) (0.248) Monetary policy rate (%) 0.197*** (0.073) Country FE y y y Year FE y y y Countries 73 72 69 Observations 885 868 819 R2 0.28 0.37 0.38 Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 17 / 29
  18. 18. The influence of additional time-varying controls Dependent variable: Real credit growth (%) (1) (2) (3) (4) (5) (6) DLEV x DPostCrisis 6.029*** 5.943*** 6.178*** 5.373** 6.028** 5.792** (2.123) (2.065) (2.129) (2.322) (2.382) (2.229) Real GDP growth (%) 1.638*** 1.629*** 1.643*** 1.562*** 1.566*** (0.248) (0.248) (0.253) (0.242) (0.248) Monetary policy rate (%) 0.197*** 0.297 0.197*** 0.153** 0.176** (0.073) (0.190) (0.073) (0.076) (0.075) Inflation rate (%) -0.148 (0.281) Private credit-to-GDP (%) 0.012 (0.044) MacroPru index (fin. sector) -1.808 (1.848) MacroPru index (borrower) -2.054 (1.605) Leverage cap (0/1) -4.585 (12.568) Real GDP growth (lag, %) 1.144*** (0.208) Monetary policy rate (lag, %) 0.284*** (0.048) Country FE y y y y y y Year FE y y y y y y Countries 69 69 69 69 69 69 Observations 819 819 814 759 759 824 R2 0.38 0.38 0.38 0.37 0.37 0.34 Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 18 / 29
  19. 19. The role of country-specific characteristics Decision to implement leverage cap is likely to depend on country-specific characteristics If these characteristics also affect real credit growth, we have to control for them to get unbiased results This has been achieved by our specification which controls for country (and time) fixed effects How do key (observable) country-specific variables affect credit growth? To answer this question, we use the correlated random effects approach, which allows both controlling for fixed effects and including constant country-specific variables (Mundlak 1978, Wooldridge 2010) In particular, we consider the credit-to-GDP gap (pre-crisis avg. 2002-07) the capital and deposit-to-assets ratio (pre-crisis avg. 2002-07) the dummy variable DLEV indicating implementation of the leverage cap prior to the crisis M. Buchholz Leverage caps & domestic credit Sep 28, 2015 19 / 29
  20. 20. The role of country-specific characteristics – Results Dependent variable: Real credit growth (%) (1) (2) (3) DLEV x DPostCrisis 6.029*** 6.147** 6.029*** (2.140) (2.329) (2.142) Real GDP growth (%) 1.638*** 1.624*** 1.638*** (0.250) (0.278) (0.250) Monetary policy rate (%) 0.197*** 0.202*** 0.197*** (0.073) (0.073) (0.073) Avg. real GDP growth 2002-07 (%) 1.101** 1.050* 1.106** (0.514) (0.619) (0.514) Avg. monetary policy rate 2002-07 (%) 0.043 0.245 0.060 (0.363) (0.417) (0.363) Avg. credit-to-GDP ratio 2002-07 (%) -0.076*** -0.078*** -0.076*** (0.017) (0.017) (0.017) Avg. capital ratio 2006/07 (%) 0.260* (0.154) Avg. deposit ratio 2006/07 (%) 0.003 (0.039) DLEV (0/1) -3.991 (7.062) Country FE y y y Year FE y y y Countries 69 61 69 Observations 819 746 819 R2 0.54 0.54 0.54 Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 20 / 29
  21. 21. Does the effect work through the pre-crisis capital ratio? The stabilizing effect of the leverage cap might work through higher capital buffers built up by banks prior to the crisis Banks might draw on these buffers after the crisis to continue lending Therefore, we expect the effect of leverage caps on real credit growth to be stronger for higher pre-crisis capital ratios We can test if the effect of the leverage cap on real credit growth works through the pre-crisis capital ratio In the empirical specification, we introduce an additional interaction of the the capital ratio with the interaction term of the post-crisis and leverage cap dummy: ∆ log RealCreditit = αi + γt + β1[DLEV × DPostCrisis] + β2[DPostCrisis × CapRatioi] + β3[DLEV × DPostCrisis × CapRatioi] + Xitδ + εit M. Buchholz Leverage caps & domestic credit Sep 28, 2015 21 / 29
  22. 22. Does the effect work through the pre-crisis capital ratio? – Results 1 Dependent variable: Real credit growth (%) (1) DLEV x DPostCrisis -1.059 (3.923) DPostCrisis x Capital ratio -0.368 (0.349) DLEV x DPostCrisis x Capital ratio 0.862** (0.416) Real GDP growth (%) 1.562*** (0.282) Monetary policy rate (%) 0.195** (0.081) Country FE y Year FE y Countries 61 Observations 746 R2 0.61 Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 22 / 29
  23. 23. Does the effect work through the pre-crisis capital ratio? – Results 2 -10-5051015 0 2 4 6 8 10 12 Pre-crisis capital ratio (%) The figure shows the effect of the leverage cap on real credit growth (solid line, in percentage points) for different values of the pre-crisis capital ratio (vertical axis). The dashed lines show 95% confidence bands based on robust standard errors clustered by country. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 23 / 29
  24. 24. Table of contents 1 Introduction Motivation Macroprudential policy Research question Related literature 2 Data Caps on banks’ leverage Real credit growth 3 Empirical specification and estimation results Empirical specification Estimation results 4 Robustness 5 Concluding remarks M. Buchholz Leverage caps & domestic credit Sep 28, 2015 24 / 29
  25. 25. Robustness: Competing explanations There are other possible explanations for the result Pre-crisis credit boom: pattern of real credit growth rates after crisis reflects standard adjustment to the stance of the financial cycle prior to the crisis Severity of crisis: Countries which experienced a more severe crisis are likely to have depressed credit growth rates afterwards M. Buchholz Leverage caps & domestic credit Sep 28, 2015 25 / 29
  26. 26. Robustness: Competing explanations – Results Dependent variable: Real credit growth (%) (1) (2) DLEV x DPostCrisis 6.250*** 5.256** (2.250) (2.371) Pre-crisis credit boom x DPostCrisis 0.011 (0.032) Severity of crisis x DPostCrisis -0.461 (0.433) Real GDP growth (%) 1.633*** 1.541*** (0.245) (0.257) Monetary policy rate (%) 0.193*** 0.205*** (0.070) (0.075) Country FE y y Year FE y y Countries 69 69 Observations 819 819 R2 0.38 0.39 Pre-crisis credit boom: private credit-to-GDP prior to the crisis (average of 2006/07). Severity of crisis: GDP growth rate of 2009 multiplied by minus one. Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 26 / 29
  27. 27. Robustness: Subsample analysis Role of other macroprudential instruments: include only countries in the control group which did not implement any instrument prior to the crisis Run analysis for sample of emerging market countries only Symmetric window of four pre and post-crisis years: Run for years 2005-12 Clear-cut pre versus post-crisis period: Exclude years 2008/09 Excluding countries from treatment group (see Appendix) M. Buchholz Leverage caps & domestic credit Sep 28, 2015 27 / 29
  28. 28. Robustness: Subsample analysis – Results (1) (2) (3) (4) No MacroPru Emerging economies 2005-12 Exclude 2008/09 DLEV x DPostCrisis 7.800* 7.759*** 6.191** 7.232*** (4.145) (2.598) (2.720) (2.353) Real GDP growth (%) 1.223*** 1.664*** 1.625*** 1.823*** (0.257) (0.301) (0.227) (0.297) Monetary policy rate (%) -1.013 0.211*** -0.194 0.231*** (0.741) (0.066) (0.177) (0.070) Country FE y y y y Year FE y y y y Countries 24 44 69 69 Observations 275 529 521 689 R2 0.41 0.39 0.43 0.40 Dependent variable: Real credit growth (%) Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 28 / 29
  29. 29. Concluding remarks Evidence on stabilizing role of macroprudential policy gained by studying the example of a cap on banks’ leverage The effect is significant in economic and statistical terms It is robust with respect to various alternative specifications controlling for different compositions of control groups and competing explanations This stabilizing dimension should be incorporated in any comprehensive cost-benefit analysis of macroprudential policy Further research: Is there a stabilizing role of other macroprudential policy instruments? M. Buchholz Leverage caps & domestic credit Sep 28, 2015 29 / 29
  30. 30. Appendix – The effect on total asset growth and contributions by sectoral components (1) (2) (3) (4) (5) Total Assets Private Sector Non-residents Central bank Public sector DLEV x DPostCrisis 4.559* 3.205** 1.367 0.565 -0.076 (2.476) (1.570) (0.854) (0.752) (0.507) Real GDP growth (%) 1.016*** 0.690*** 0.118 0.111 -0.012 (0.272) (0.127) (0.074) (0.090) (0.009) Monetary policy rate (%) 0.244*** 0.075** 0.101*** 0.095*** 0.001 (0.032) (0.033) (0.009) (0.016) (0.003) Country FE y y y y y Year FE y y y y y Countries 66 66 66 66 65 Observations 761 761 761 739 748 R2 0.33 0.33 0.33 0.33 0.33 Dependent variable: Change in claims subcategory relative to total assets of previous period Column header indicates the sectoral component of total claims. Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 30 / 29
  31. 31. Appendix – Robustness: Excluding countries from treatment group (1) (2) (3) (4) (5) (6) (7) (8) Canada Chile Ecuador Jordan St. Kitts and Nevis Paraguay Saudi Arabia United States DLEV x DPostCrisis 6.030*** 6.026*** 6.339*** 5.420** 5.422** 5.012** 7.043*** 6.790*** (2.123) (2.123) (2.338) (2.271) (2.263) (2.084) (2.065) (2.200) Real GDP growth (%) 1.636*** 1.640*** 1.641*** 1.648*** 1.700*** 1.627*** 1.642*** 1.638*** (0.248) (0.248) (0.250) (0.252) (0.251) (0.261) (0.251) (0.248) Monetary policy rate (%) 0.198*** 0.198*** 0.198*** 0.197*** 0.198*** 0.207*** 0.197*** 0.197*** (0.0728) (0.0727) (0.0726) (0.0733) (0.0724) (0.0645) (0.0737) (0.0731) Country FE y y y y y y y y Year FE y y y y y y y y Countries 68 68 68 68 68 68 68 68 Observations 812 815 806 806 806 806 806 806 R2 0.38 0.38 0.38 0.38 0.39 0.39 0.38 0.38 Dependent variable: Real credit growth (%) Column header indicates country excluded from estimation. Robust standard errors clustered by country given in parantheses. ∗∗∗ ,∗∗ ,∗ denotes significance at the 1, 5, and 10 percent level, respectively. All variables were winsorized at the 1% and 99% quantiles. M. Buchholz Leverage caps & domestic credit Sep 28, 2015 31 / 29

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