The credibility of European banks risk-weighted capital: structural differences or national segmentations?

Transcription

1 CRC Credit Research Centre The credibility of European banks risk-weighted capital: structural differences or national segmentations? Brunella Bruno, Bocconi Giacomo Nocera, Audencia Nantes École de Management Andrea Resti, Bocconi

2 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

3 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

4 Background: Wide distrust of banks RWs Market participants have questioned the reliability and comparability of RWs, and focused on adjusted capital ratios Regulators have endorsed the use of backstops against the opportunistic use of risk-weighted measures Plain, un-weighted capital ratios Output floors (CP306) and Input floors (Sweden, CP362) on RWAs generated with internal models

5 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

6 Related literature Practitioners and supervisors e.g. BIS ; EBA : RWAs show considerable differences both across banks and across countries Academic literature Vallascas and Hagendorff (2013), show that riskweights are ill-calibrated to a market measure of bank portfolio risk (i.e., bank asset volatility) Mariathasan and Merrouche (2013) find that average risk-weights decrease once internal models gain regulatory approval, and this is especially true for capital-constrained banks

7 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

8 Sample and data sources 50 largest banks by total assets (and with pillar 3 information available) 48 listed 49 adopt IFRS 17 countries: EU, Norway and Switzerland Unbalanced panel over Source of data Pillar 3 reports (Banks websites) Compulsory, but not standardized Financial statements (Bankscope) Market-based risk measures (Bloomberg) Macroeconomic variables (World Bank and OECD reports) Belgium 2 Denmark 2 Finland 1 France 5 Germany 3 Greece 4 Hungary 1 Ireland 2 Italy 8 Netherlands 2 Norway 1 Poland 1 Portugal 2 Spain 5 Sweden 4 Switzerland 2 United Kingdom 5 All 50

9 Dependent variables, risk weights, have been decreasing over RWA_TA RWA_EAD Average risk weight (assetweighted) Average weight for credit risk (includes OBS) Lloyds TSB+ HBOS

10 Main explanatory variables Name Description SIZE Natural log of total assets DEPOSITS Customer deposits / total assets LOANS Loans / total assets CORPORATE Corporate loans / total loans RETAIL Retail loans / total loans ROA Return on assets (winsorised) GDP_GROWTH Annual real growth in national GDP BANKCONC Share of top 3 banks on total assets BANKONGDP Bank total assets on GDP STANDARD Loans under standardised approach / total loans IRB_LOANS Loans under internal ratings-based approach / total loans HIGH_RETCORP_IRB Dummy for heavy users of internal ratings-based models CDSSPREAD CDS spread WACC Weighted average cost of capital ASSETVOL Asset volatility ZSCORE Z-score (measure of credit risk) IMPAIRED_LOANS Impaired loans / total loans TIER1 Tier 1 capital / risk-weighted assets RATING_A Average rating (higher values mean higher quality)

11 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

12 Univariate statistics / 1 Business models Correlation with Mean Median Max Min Sigma Obs RWA_TA RWA_EAD SIZE %*** -50.4%*** DEPOSITS %*** 34.8%*** LOANS %*** 55.0%*** RETAIL %*** 15.6%*** CORPORATE % 2.7% Institutions / total loans (%) %*** -24.9%*** GOVERNMENT %*** -16.0%*** ROA % -3.0% Banks showing higher risk weights are smaller, more involved in the traditional loans/deposits business, more exposed to retail portfolios (as opposed to governments and financial institutions)

13 Univariate statistics / 2 Risk models Correlation with Mean Median Max Min Sigma Obs RWA_TA RWA_EAD STANDARD_LOANS %*** 71.0%*** IRB_LOANS %*** -71.0%*** FIRB_LOANS %* -11.9%** AIRB_LOANS %*** -61.6%*** Banks showing lower risk weights are heavy users of IRB models, especially advanced ones. This is consistent with the incentives created by the Basel II Accord.

14 RWA / TA Univariate statistics / 3 Capital and risk Correlation with Mean Median Max Min Sigma Obs RWA_TA RWA_EAD TIER1_RWA %*** -57.7%*** Stock return volatility (%) % 5.9% CDS spreads (bps) %*** 30.6%*** WACC (%) %*** 23.5%*** IMPAIRED_LOANS %*** 30.9%*** ASSET_VOL %*** 19.0%*** Z Score %*** -30.1%*** RATING_A *** *** Risk-weight based indicators are not inconsistent with most market-based risk measures (CDS, WACC, ASSETVOL, ZSCORE), ratings and the banks actual bad loans experience (impairment ratio). Tier 1 / TA = k A negative link with risk-weighted capital suggests that investors/supervisors look at un-weighted leverage, too. Tier 1 / RWA

15 Why do RWA differ across banks? odds ratio x_or = ln[x/(1-x)] RWA_TA_OR RWA_EAD_OR Coef Std. Coef. Coef Std. Coef. Constant Higher RWs for small SIZE *** banks, strongly geared DEPOSITS 0.009*** towards loans and LOANS 0.017*** traditional 0.008*** portfolios CORPORATE 0.013*** *** IRB_LOANS ** Low *** RWs for heavy IRB TIER1_RWA *** users *** and wellcapitalised banks Dummy France Dummy Germany National Dummy Greece segmentations, Dummy Italy especially Dummy Spain outside SSM Dummy Sweden *** ** Dummy UK 0.236* 0.275** Adj. R-square Joint F

16 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

17 What drives IRB adoption? Large, well capitalised banks use IRB more IRB_LOANS_OR IRB_LOANS_OR IRB_LOANS_OR Coef Std. Coef. Coef Std. Coef. Coef Std. Coef. Constant *** *** *** SIZE 0.536*** *** *** DEPOSITS ** ** LOANS * CORPORATE 0.019* * RETAIL 0.034*** Tier 1_RWA 0.141*** ** *** Dummy France * Dummy Germany ** but national Dummy Greece supervisory * Dummy Italy *** practices do Dummy Spain matter Dummy Sweden Dummy UK which in turn are driven by the banking industry s lobbying power BANKCONC 0.037*** BANKGDP 0.183*** Adj. R-square Joint F

18 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

19 Multivariate results: Do investors believe in Basel s RWs? WACC WACC CDSSpread CDSSpread Asset Vol Asset Vol RWs affect market-based measures of risk Constant *** RWATA 0.099** 0.051*** 4.646** 4.903*** 0.006** 0.006*** RWAEAD_O SIZE * ** DEPOSITS ** ** Traditional lending businesses are perceived as less risky LOANS * * CORPORATE *** *** * ** RETAIL * *** Asset profitability matters, write-downs are less clear EQUITY_RATIO 0.374** 0.349*** IMPAIRED_LOANS *** *** 0.020*** 0.022*** ROA *** *** *** *** GDP_GROWTH *** *** *** *** RATING_A ** *** *** *** 0.057** 0.049** Time and country dummies are significant Adj. R F on countries *** *** *** *** 2.340* 3.690** F on years *** *** *** *** 9.830*** ***

20 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Preliminary evidence from multilevel models Final remarks

21 Experimenting with multilevel models 2-level model: 3-level model: Level 3: countries Level 2: countries Level 2: banks RWA x x it i i1 i1 in in Level 1: observations RWA x x it i i1 i1 in in Level 1: observations

22 2-level models: what evidence of national segmentations? 2-level model: LR* Pvalue* Level 2: countries SIZE DEPOSITS LOANS CORPORATE IRB_LOANS *Base case: RE model RWA x x it i i1 i1 in in Level 1: observations Slopes for the main variables of the RWATA model differ significantly across countries

23 How did national segmentations evolve over time? LR P-value 2 levels (a) Level 2: countries* (b) Level 2: banks (c) 3 levels Level 2: banks Level 3: countries (c) vs. (b) *after accounting for changes in GDP

24 Outline of this talk Background Related literature Sample and key variables Results Why do risk weights differ across banks? Why do some banks use internal models more? Do investors believe in Basel s risk weights? Final remarks

25 Final remarks Risk weights are affected by the banks size, business model and asset mix The adoption of internal ratings based (IRB) approaches is (as expected) a powerful driver of bank risk-weighted assets Lower risk weights are positively linked to the banks capital cushion IRB adoption is more widespread in countries where supervisory capture is potentially stronger Regulatory risk weights are not disconnected from market-based measures of bank risk

26 CRC Credit Research Centre The credibility of European banks risk-weighted capital: structural differences or national segmentations? Brunella Bruno, Bocconi Giacomo Nocera, Audencia Nantes École de Management Andrea Resti, Bocconi