Capital &Credit

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Capital Requirements and Credit Growth:Binding or not?

IX Annual Seminar on Risk, Financial Stability and Bankingof the Banco Central do Brasil

Claire Labonne 1 Gildas Lame 2

1ACPR - Banque de France

2French Ministry of Finance

August 14-15th, 2014

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Capital &Credit

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Overview

1 Introduction

2 Data

3 Methodology

4 Empirical results

5 Conclusion

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Introduction

• European bank’s balance sheets under close scrutiny:Asset Quality Review

• French non-financial corporates highly depend on banksfor financing: 80% but only 33% in the US

• Basel III sets higher capital requirements for banks formicro- and macro-prudential purposes

• Debate over the impact of capital/capital requirements:Admati and Hellwig (2013), Miles et al. (2013)

• Trade-off between a sound banking system and easyfinancing that spurs growth

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More Capital, Less Credit ?

• Voluntarily-hold capital: new portfolio choice ?balance-sheet expansion?

• Mandatory capital: binding constraint ? balance-sheetcontraction?

• Miller (1995) : further distinction to be made :• having more capital (or a higher capital ratio) ex-ante with

full anticipations• raising capital

• On the first aspect of Miller (1995), do theModigliani-Miller propositions apply to banks ? ”Yes andno.”

• Proposition I: Irrelevance of capital structure• Proposition II: Equity costs rise with leverage

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Theory warns:

Many effects are at stake:

• Credit dynamics is affected by both demand and supplyeffects

• Supply effects include but are not restricted to capitalvariations

• Capital variations’ impact depends on their sources:market- or supervision- or business-induced?

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Literature

• Survey data to identify supply and demand: Lown andMorgan (2006), Del Giovane et al. (2011)

• Impact analysis of capital requirements: Aiyar et al.(2012), Francis and Osborne (2012), Carlson et al.(2013), Brun et al. (2013)

• Non-linearity of capital’s effects: Jimenez et al. (2012),Albertazzi et al. (2010), Bonaccorsi et al. (2012)

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What we do

• Evaluate the impact of bank capital on lending to NFCs inFrance

• Account for demand and credit standards shocks: BLSindividual answers

• Account for the potential non-linearity induced bysupervision (Pillar II discretionary capital surcharges)

• Account for the potential endogeneity of supervision withbanking supervisor’s risk-assessment ratings

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Results preview

• 1-ppt increase to the Tier1-capital-to-asset ratio → about1-ppt increase to credit growth to NFCs

• Effect of capital on credit depends on the intensity ofsupervisory constraint

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The data

Bank Lending Survey (Banque de France)

• 13 banks from 2003Q1 to 2011Q4 (solo basis)

• Individual credit standards and demand (NFCs)

Prudential reporting (ACPR)

• Balance-sheet data

• Discretionary individual capital requirements

• Risk-assessment ratings, similar to the CAMEL ratingsused in the US

Aggregate data

Macro variables: nominal investment by NFCs, Eonia

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Aggregate credit

Figure: Representativity of credit growth in our sample

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Credit supply (NFCs)

Figure: NFCs credit standards

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Credit demand (NFCs)

Figure: NFCs credit demand

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Discretionary capital requirements

• The supervisor can require the bank to hold a higher levelof capital than the minimum legal requirement of Pillar I

• The average total regulatory requirement (regarding Tier 1capital) between 2003 and 2012 is above the legalminimum of 4%.

• For the period 2003-2006, the additional requirement wasmore or less stable. It increased during the following yearsuntil reaching a peak in 2010.

• These supervisory requirements vary in the cross-sectionaldimension but vary only gradually through time for a givenbank.

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Reduced-form equation

∆yi ,t = α + β(L)CAT 1i ,t + γ(L)Xi ,t + µi + λt + εi ,t .

• ∆yi ,t : quarterly growth rate of credit to NFCs

• CAT 1i ,t : Tier 1 / asset ratio - this is not arisk-weighted ratio

• Xi ,t : bank-level and macro control variables such as BLSindividual answers

• µi , λt : bank and time effects

→ FE estimator with clustered variance-covariance matrix, atthe banking group level→ Lag selection using BIC criterion

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Supervisory constraints (1/2)

To measure the intensity of supervision, we define:

Bufferi ,t = bank ratioi ,t − supervisor requirementsi ,t .

We consider 3 groups to discriminate unconstrained,constrained and intermediary banks:

Group Ai ,t =

{10

if Bufferi ,t ≤ s1 = 50bpsotherwise

Group Bi ,t =

{10

if s1 < Bufferi ,t ≤ s2 = s1+Max2

otherwise

Group Ci ,t =

{10

if s2 < Bufferi ,totherwise

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Supervisory constraints (2/2)

We augment the equation to estimate (βj)j=0,1,2 in :

∆yit = α + β0(L)CAT 1i ,t+β1(L)[CAT 1i ,t · Group Ai ,t ]+β2(L)[CAT 1i ,t · Group Bi ,t ]+β3(L)Group Ai ,t + β5(L)Group Bi ,t

+γ(L)Xi ,t + µi + λt + qt + ε′i ,t

• We take Group C - unconstrained banks - as the referencegroup.

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Potential endogeneity ofsupervision

• There may be reverse causality from credit growth to therequired capital ratio

• Supervision and credit growth may depend on the sameenvironment’s characteristics (omitted variable bias)

2SLS regression

• Risk-assessment ratings of the French banking supervisoras instruments

• Ratings related to bank’s internal audit and governanceitems as well as Buffert−1

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Reduced Form EquationQoQ NFCloanst (1) (2) (3)

CAT 1t−1 0.912∗∗ 0.901∗∗ 1.116∗∗

(0.363) (0.318) (0.411)

BLS S NoChget−1 -0.044∗∗ -0.049∗∗ -0.045∗∗

(0.017) (0.018) (0.018)

BLS S Tightt−1 -0.049∗∗ -0.070∗∗∗ -0.056∗∗

(0.017) (0.019) (0.019)

BLS D NoChget−2 0.021∗∗ 0.012∗ 0.005(0.006) (0.006) (0.005)

BLS D Increat−2 0.032∗∗∗ 0.023∗∗ 0.015(0.008) (0.008) (0.009)

NPLt−1 -0.490 -0.301 0.294(0.322) (0.328) (0.450)

d Investmentt−2 0.413(0.228)

Eoniat−2 0.009∗∗∗

(0.002)

Constant 0.016 -0.011 -0.007(0.026) (0.024) (0.034)

Fixed effects YES YES YESCrisis Dummy YES YES YESData Quality Dummy YES YES YESTime Dummies NO NO YESSeasonal Dummies YES YES NOObservations 382 382 382Adjusted R2 0.066 0.094 0.115

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Economic and Supervisory Capital

QoQ NFCloanst (1) (2) (3)

CAT 1t−1 1.510∗∗∗ 1.293∗∗∗ 1.632∗∗

(0.357) (0.350) (0.496)

Group At−2 0.016 -0.015 -0.018(0.018) (0.013) (0.019)

Group Bt−2 0.029∗ 0.014 0.013(0.014) (0.013) (0.010)

CAT 1t−1 ∗ Group At−2 -0.459 -0.081 -0.103(0.303) (0.214) (0.278)

CAT 1t−1 ∗ Group Bt−2 -0.772∗∗∗ -0.579∗∗∗ -0.693∗∗∗

(0.169) (0.116) (0.175)

Interactions with capital - same control variables as before(BLS, constant, crisis dummy, data quality); quarterly dummiesin the first two columns, time dummies in the 3rd.

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IV regressions with therisk-assessment ratings

QoQ NFCloanst Strat. and Governance Internal Audit Both instruments

CAT 1t−1 1.226∗∗ 1.117∗ 1.127∗∗

(0.490) (0.485) (0.462)

Group A Instrut−2 -0.036 -0.028 -0.033(0.030) (0.035) (0.029)

Group B Instrut−2 0.006 0.019 0.014(0.014) (0.021) (0.020)

CAT 1t−1 ∗ Group A Instrut−2 0.110 0.151 0.159(0.333) (0.343) (0.341)

CAT 1t−1 ∗ Group B Instrut−2 -0.409 -0.582∗∗ -0.557∗

(0.254) (0.240) (0.253)

BLS S NoChget−1 -0.045∗∗ -0.046∗∗ -0.046∗∗

(0.014) (0.017) (0.016)

BLS S Tightt−1 -0.058∗∗ -0.061∗∗ -0.061∗∗

(0.018) (0.019) (0.019)

BLS D NoChget−2 0.008 0.009 0.009(0.007) (0.007) (0.007)

BLS D Increat−2 0.013∗ 0.016∗∗ 0.016∗∗

(0.006) (0.006) (0.006)

Buffer instrumented - same control variables as before (BLS,constant, crisis dummy, data quality, time dummies). Set ofinstruments relevant, valid and not weak

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Conclusion

• Evaluate the impact of bank capital on lending to NFCs inFrance and assess the non-linearities induced bysupervision

• Rich and unique database: BLS individual answers, PillarII discretionary capital surcharges, French CAMEL-likeratings

• 1-ppt increase to the Tier1-capital-to-asset ratio → about1-ppt increase to credit growth to NFCs

• Effect of capital depends on the intensity of supervisoryconstraint: U-shaped relationship

• Importance of the constraint being binding or not formacro-prudential purposes, as opposed to micro-prudentialones: proper impact, unintended credit distortions orsubstitutions

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Thank you for your attention !

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