Macroprudential Policy, Countercyclical Bank Capital Buffers & Credit Supply Cycles:

Evidence from the Spanish Dynamic Provisioning Experiments

Gabriel Jiménez (Banco de España) Steven Ongena (Tilburg & CEPR)

José-Luis Peydró (European Central Bank)Jesús Saurina (Banco de España)

CEPR – UoV – OENB Bank Supervision and Resolution – 4th October 2011

Caveats:1. Work in progress2. These are our views and do not necessarily reflect those of the Bank of Spain,

the European Central Bank and the Eurosystem

The National Bank of Belgium and its staffgenerously supports the writing of this paper

in the framework of the:

Colloquium of the National Bank of Belgium“Endogenous Financial Risk”

October 11/12th, 2012Brussels

The usual disclaimer is in effect: These are our views and do not necessarily reflect those of the European Central Bank, the Eurosystem, the Bank of Spain and/or the National Bank of Belgium.

3

Macroprudential policy and credit cycles

• Macroprudential policy aims at reducing the potentially strong negative externalities from the financial to the real sector

• A key channel is “excessive” bank pro-cyclicality / credit cycles due to financial frictions in:

– Banks (credit supply): Holmström and Tirole (QJE, 1997), Allen and Gale (2000 and 2007), Diamond and Rajan (JPE 2001 and AER 2006), Adrian and Shin (AER, 2010), Shleifer and Visnhy (JFE & AER, 2010), Kindleberger (1978), Tirole (2011), Gersbach and Rochet (2011), …

– Non-financial sector (credit demand): Bernanke and Gertler (AER, 1989), Kiyotaki and Moore (JPE, 1997), Lorenzoni (RES, 2008), Jeanne and Korinek (2011), …

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

4

Credit supply cycles

• “Excessive” bank pro-cyclicality /credit supply cycles due to bank frictions

– In good times:• Problem: seeds for the next crisis via too high credit supply

– In bad times:• Problem: credit crunch by banks with low capital buffers

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

5

One solution: countercyclical bank capital buffers?

• Higher bank capital standards in good times (and lower standards in bad times) can be beneficial both in good and bad times by reducing “excessive” bank pro-cyclicality in credit supply

– In good times:• Problem: seeds for next crisis via too high bank credit supply• Solution: banks should hold more capital (“skin in the game”) to

internalize potential loan costs/externalities

– In bad times:• Problem: credit crunch by banks with low capital buffers• Solution: higher bank capital buffers built in good times to

support credit in bad times (without government help)

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

6

Policy: the rationale of Basel III on higher capital and countercyclical buffers

• “The new [capital] standards will markedly reduce banks’ incentive to take excessive risks… lower the likelihood and severity of future crises, and enable banks to withstand - without extraordinary government support - stresses of a magnitude associated with the recent financial crisis.”

G-20 Seoul Official statement, November 2010

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

7

Question

• What are the effects of countercyclical bank capital buffers on credit supply? – More generally: Bank capital impact on credit supply?

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

8

Theory

• The two complementary rationales of bank capital (better incentives and buffers in crisis) highlighted by policymakers are also present in theoretical models: – e.g. Holmström and Tirole, QJE 1997; Morrison and White, AER 2005;

Diamond and Rajan, JF 2000-JPE 01-AER 06

• And even the countercyclical buffer, e.g. in models:– with agency problems (e.g. Tirole, 2011; Gersbach and Rochet, 2011) – without agency problems but with investor sentiment (e.g. Shleifer

and Vishny, JFE 2010 and AER 2010)

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

9

Empirical identification

• To identify the effects of countercyclical bank capital buffers on credit supply (in good and bad times) is needed both:

1. Shocks to countercyclical capital buffers

2. Comprehensive loan-level data

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

Experimental setting: Spain and dynamic provisioning• Spain 1999-2010 offers an almost ideal setting for identification:

1. Policy experiments with dynamic provisioning exogenously changed banks’ retained profits in good times to be used during crisis times Exploit policy shocks in good times: contractionnary introduction

in mid 2000 and expansionary change in mid 2005 Exploit provision buffers and a policy shock during the recent crisis

2. Comprehensive credit register (matched with bank and firm characteristics) to identify credit availability Difference-in-differences (banks more/less affected by shocks and

before/after shocks) controlling for time-varying observed and unobserved firm heterogeneity with firm*time fixed effects

10

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

11

Preview of the results

• Countercyclical capital buffers mitigate credit supply cycles

– They contracted credit availability (volume and cost) during good times (4%), but expanded it during the recent crisis (6%)

– While bank-level effects are always economically strong, firms are even more affected during crisis times when switching banks is difficult (1.5% in good vs. 5% in crisis times)

– In the first full draft of the paper we want to further exploit firm and bank heterogeneity

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

12

Our contributions• We exploit policy shocks to bank capital (countercyclical

buffers) both in good and bad times to identify the impact of bank capital on credit supply:

1. Unique (in the world) policy experiments on countercyclical capital buffers taking place before Basel III key contribution

2. In Jiménez, Ongena, Peydró and Saurina (AER, forthcoming) we find that credit supply is pro-cyclical in GDP and monetary conditions and stronger for banks with a lower capital ratio• We used lagged bank capital. But bank capital is a key

strategic variable and likely endogenous• Our innovation: to exploit the policy shocks affecting bank

capital: causality from bank capital to the supply of credit

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

13

Outline for the rest of the talk

• Policy shocks: dynamic provisioning experiments– How does it currently work?– Different policy shocks (2000 and 2005 and 2008) and the crisis shock

• Empirical strategy and data– Empirical strategy– Loan, firm and bank datasets

• Results– 2000 policy shock, the 2005 one and the 2007-2009 crisis– Loan- and firm-level results

• Conclusions– Implications for Basel III, bank bailouts, monetary policy and, in general, for macroprudential policy – Incomplete draft: things that we are doing

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

14

The introduction of dynamic provisions

• In July 2000, the Banco de España (Spain’s central bank, banking supervisor and responsible for bank accounting) put in place dynamic (statistical/ general) provisions due to:– Spain had the lowest ratio of loan loss provisions to total loans

among all OECD countries in 1999 – A period of sizeable credit growth and difficulty in immediately

recognizing problem loans following a credit expansion

(see Saurina et al (2000), Saurina (2009a) and Saurina (2009b) for all the details on dynamic provisioning)

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

15

Dynamic provisions: policy shocks and basic idea

• Introduced in mid-2000– modified in mid 2005 (for consistency with IFRS)– modified in 2008:Q4 (to allow banks to use more the provision funds

built in good times)

• Spanish LLP try to cover the increase in credit risk during lending expansions

• Forward-looking (provision before any loss arrives)

• Countercyclical: Build up a buffer in good times to be used in bad times

• Tier-2 Capital

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

16

Current provisions

• Specific provisions cover incurred losses already identified in a specific loan

• General (dynamic) provisions cover incurred losses not yet individually identified in a specific loan through a collective assessment for impairment

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

17

A simple countercyclical mechanism

• In periods of expanding credit, a buffer of provisions is being built up to cover the increase in credit risk (the incurred losses not yet materialized in specific loans)– We analyze the introduction in 2000 and a modification in 2005

• In periods when specific losses materialize in individual loans, the banks can draw down from the previously built-up buffer of provisions– We analyze the built-up buffers in the recent crisis

• The Spanish general provision also includes an upper and lower limit in the amount of the general fund being built– The lower limit was relaxed in the crisis and we also exploit this

• There is a simple formula governing the process

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

18

Empirical identification

• Using a difference-in-difference approach, we compare bank lending before and after the different shocks:– policy shocks in good times: introduction in mid-2000 (and change in 2005) of

the new regulation – crisis shock: provision funds at the start of the financial crisis in August 2007

and policy change of the lower floor of provision funds in 2008:Q4

• We differentiate across banks with varying susceptibility to the shocks and employ firm*time fixed effects to control for time-varying observed and unobserved firm heterogeneity (see Khwaja and Mian (AER, 2008) and Jiménez, Ongena, Peydró and Saurina (AER, forthcoming))– control also for other key bank characteristics– we analyze all margins of lending

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

19

Bank’s susceptibility to the shocks: Buffers

• For the policy shocks in good times: – new formula applied to the existing loan portfolio for each bank

yielding a bank-specific amount of new funds to be provisioned (over total assets)

– the 2005 policy shock changed the initial weights on different loans

• For the crisis shock: – how much each bank had built up as dynamic (general) provisions

just prior to the onset of the crisis (2006:IV) over total assets– policy change of lower floor of provision funds in 2008:Q4 affects

more the banks with lowest provision funds in 2008:Q3

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

20

Credit register

• Credit register from Spain matched with bank and firm relevant information (2007:Q2-> 100,000 firms, 175 banks, 600,000 loans)

• Exhaustive loan (bank-firm) level data on all outstanding business loan contracts at a quarterly frequency

• We calculate the total exposures by each bank to each firm in each quarter from 1999:III to 2009:IV– The sample period includes one year before the initial shock (to run placebo tests)

and we analyze 2.5 years of data on the crisis

• We analyze changes in (log) credit volume (commitment or drawn), maturity, collateral and the cost of lending (proxied by the percentage of drawing down to total committed loans)– Intensive and extensive margin of lending

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

Benchmark loan- and firm- level equations and hypotheses

• Shocks: – Good times: policy shocks of mid 2000 and mid 2005 – Bad times: crisis (from 2007:Q3) and policy shock in 2008:Q4

• LHS is change (after-before shock) in credit: log credit volume (commitment or drawn), short-term loans, collateralized loans and drawn to committed loans

• Buffers is our main variable on dynamic provisions (def. on previous pages)– Firm-time fixed effects to control also for time-varying unobserved heterogeneity– Bank controls are bank size, capital, NPL, ROA, liquidity, real estate exposure and bank

type (commercial, saving and coop banks) (and bank fixed effects on level of credit)

• Hypotheses under reduction of credit supply cycles due to capital buffers: β<0 in the 2000 and 2005 policy shock & β>0 in the crisis shock

• Estimate similar firm-level regression to check credit substitution & real effects:

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina 21

22

Policy shock of July-2000, loan level data &difference in log credit volume

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

Similar results for extensive margin and for credit cost and maturity

23

Policy shock of July-2000, loan level data & time-varying coefficients of buffers on credit volume

Also for credit drawn, extensive margin and cost and maturity with similar results

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

24

Policy shock of July-2000, both loan & firm level data time-varying coefficients of buffers on credit volume

Also for credit drawn, cost and maturity with similar results

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

25

Policy shock of July-2000, loan level data & time-varying coefficients of buffers on credit cost

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

26

Economic effects and summary

• An increase of one standard deviation in buffers in 2000:II reduces the committed volume of credit:– at the bank (loan) level: by 4 percent – at the firm level: by 1.5 percent

• Similar results for credit drawn, cost and maturity and for extensive and intensive margin and for the 2005 shock (in this case lower elasticities, probably due to an expansionary shock)

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

27

Summary of economic effects during crisis

• An increase of one standard deviation in buffers in 2006:IV increases the committed volume of credit:– at the bank (loan) level: by almost 7 percent – at the firm level: by at most 5 percent

• An increase of one standard deviation in buffers in 2000:II reduces the committed volume of credit:– at the bank (loan) level: by 4 percent – at the firm level: by 1.5 percent

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

28

Conclusions and policy implications• Identify countercyclical bank capital buffers effects on credit supply

• Experimental setting: Spain 1999-2010– Dynamic provisioning experiments and complete credit register

• Countercyclical capital buffers strongly mitigate credit supply cycles

• Firms are more affected during crisis times when switching from banks with low to high capital buffers is difficult

• Important policy implications for:– Basel III, bank bailouts, monetary policy and, in general, for

macro-prudential policy– Individual bank capital (not only aggregate) matters in crises!

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

29

Incomplete version: things we are doing

• Extensive margin of new lending missing

• Firm heterogeneity: change of credit supply could be stronger for riskier firms, smaller, with less tangible assets, with less relationship banking

• Bank heterogeneity: weaker banks should be probably more affected, e.g. banks with lower profits, lower capital buffers, smaller, non-listed, with higher NPLs

• Real effects: implications for employment, sales, profits, investment

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina

30

Thank you

31

Numbers

• The ratio of general provisions to total credit subject to the general provision at the end of 2007 for individual balance sheets was 1.22%. If we exclude exposures with a 0% weighting, the coverage ratio reaches 1.59%. For non-consolidated data in Spain, the general provisions were 78.9% of total provisions at the end of 2007.

• in 1999 the loan-loss provisions of Spanish banks were the lowest among OECD countries. In 2006, the Spanish banking system had by far the highest coverage ratio among Western European countries, at 255 percent

• Counter-cyclical provisions were included in Tier 2 capital i.e. up to 1.25 percent of risk- weighted assets

Total loan loss provisions at a consolidated level at the end of 2007 were 1.33% of total consolidated assets

The ratio of bank capital and those total assets was 5.78% At the end of 2007, Spanish banks at a consolidated level had 1.20% of general provisions

over total credit granted

32

Summary statistics of buffers

• In 2000:II:– Buffers has an average of 0.46 and the standard deviation is 0.09 – Only correlated to real estate exposure, bank-type and

collateralized loans. Not correlated to other bank, firm and loan characteristics

• In 2006:IV: – Average of pre-crisis buffers is 1.1 and the standard deviation is

0.21– Not correlated to firm and loan characteristics, but to some

bank characteristics (not to bank type)

Introduction – Policy shocks – Empirical Strategy – Results – Conclusions

Gabriel Jiménez, Steven Ongena, José-Luis Peydró and Jesús Saurina