macroprudential policy, countercyclical bank capital buffers and credit supply: evidence from the...

Macropruden+al Policy, Countercyclical Bank Capital Buffers and Credit Supply:

Evidence from the Spanish Dynamic Provisioning Experiments

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

José-‐Luis Peydró (UPF and Barcelona GSE) Jesús Saurina (Banco de España)

Barcelona GSE IX Trobada – IAE CSIC – 21st October 2011

Macropruden+al policy and credit cycles

•  Macropruden+al policy aims at reducing the poten+ally strong nega+ve externali+es from the financial to the macro-‐real sector

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

–  Banks (credit supply): Holsmtrom and Tirole (QJE, 1997), Allen and Gale (2000 and 2007), Diamond and Rajan (JPE 2001 and AER 2006), Adrian and Shin (AER, 2010 and Handbook of ME, 2011), 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)…

2

Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions

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

Credit supply cycles

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

fric+ons

–  In good +mes: •  Problem: too high credit supply/sof lending standards (seeds for the next crisis) since e.g. have lihle capital at stake

–  In bad +mes: •  Problem: credit crunch by banks due to e.g. low capital buffers

3



One solu+on: countercyclical bank capital buffers?

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

–  In good +mes: •  Problem: too high bank credit supply/sof lending standards •  Solu+on: banks should hold more capital (“skin in the game”) to internalize poten+al loan costs/externali+es. On the other hand, bank capital may be too costly

–  In bad +mes: •  Problem: credit crunch by banks due to low capital buffers •  Solu+on: higher bank capital buffers built in good +mes to support credit in bad +mes (without government help)

4



Policy: the ra+onale of Basel III on higher capital and countercyclical buffers

•  “The new [capital] standards will markedly reduce banks’ incenDve 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

5



The bankers think differently •  “More equity might increase the stability of banks. At the same Dme

however, it would restrict their ability to provide loans to the rest of the economy. This reduces growth and has negaDve effects for all”

Josef Ackermann, CEO of Deutsche Bank (Nov 20, 2009)

•  “The BriDsh Bankers' AssociaDon ... calculated that demands by internaDonal banking regulators in Basle that they bolster their capital will require the UK's banking industry to hold an extra £600bn of capital that might otherwise have been deployed as loans to businesses or households”

The Observer (July 11, 2010)

•  “Excess bank equity capital ... would consDtute a buffer that is not otherwise available to finance producDvity-‐enhancing capital investment”

Greenspan (FT, July 27, 2011) 6



Ques+on

•  What are the effects of countercyclical bank capital buffers on credit supply? – More generally: Bank capital impact on credit supply? –  Good vs. crisis +mes? –  Bank and firm heterogeneity? –  Real effects?

7



Theory

•  The two complementary ra+onales of bank capital (beher incen+ves and buffers in crisis) and the poten+al costs via a credit supply reduc+on highlighted by policy makers and by the bankers respec+vely are also present in theore+cal models: –  e.g. Holmström and Tirole, QJE 1997; Morrison and White, AER 2005;

Diamond and Rajan, JF 2000-‐JPE 01-‐AER 06; Gale and Özgür, JEEA 2005…

•  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 sen+ment (e.g. Shleifer and Vishny, JFE 2010 and AER 2010)

8



Empirical iden+fica+on

•  To iden+fy the effects of countercyclical bank capital buffers on credit supply (in good and bad +mes) is needed both:

1.  Shocks to countercyclical bank capital buffers

2.  Comprehensive loan-‐level data

9



Experimental seQng: Spain and dynamic provisioning

•  Spain 1999-‐2010 offers an almost ideal seung for iden+fica+on: 1.  Policy experiments with dynamic provisioning exogenously changed

banks’ retained profits in good +mes to be used during crisis +mes Ø  Exploit policy shocks in good +mes: contrac+onary introduc+on in mid

2000 and expansionary change in mid 2005

Ø  Exploit provision buffers and a policy shock with the recent crisis shock

Ø  Capital: over and above than provisions for specific and general losses

2.  Comprehensive credit register (matched with bank and firm characteris+cs) to iden+fy credit availability Ø  Difference-‐in-‐differences (banks more/less affected by shocks and

before/afer shocks) controlling for +me-‐varying observed and unobserved firm heterogeneity with firm*+me fixed effects

10



Preview of the results Countercyclical capital buffers 1.  Mi+gate bank credit supply cycles

–  They strongly contracted credit availability (volume and cost) during good +mes, but strongly expanded it during the recent crisis •  Weaker banks more affected

2.  Have posi+ve aggregate firm-‐level real effects –  Firm bank credit availability and real variables (almost) NOT affected in good +mes

–  But strong, POSITIVE impact in crisis +mes for firm credit availability, employment, total assets, and survival!

3.  Smaller firms more benefited in crisis +mes; no reduc+on of risk-‐taking in good +mes

11



Outline for the rest of the talk

•  Policy shocks: dynamic provisioning experiments –  How does it 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-‐2010 crisis –  Loan-‐ and firm-‐level results

•  Conclusions –  Implica+ons for Basel III, bank bailouts, monetary policy and, in general, for

macropruden+al policy

12



The introduc+on of dynamic provisions

•  In July 2000, the Banco de España (Spain’s central bank, banking supervisor and responsible for bank accoun+ng) put in place dynamic provisions due to: –  Spain had the lowest ra+o of loan loss provisions to total loans among all OECD countries in 1999

–  An empirical fact: afer strong credit growth in good +mes follows high NPLs and provisions are very low in good +mes and very high in bad +mes

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

13



Dynamic provisions: policy shocks and basic idea

•  Introduced in mid-‐2000 (contrac+onary shock) –  modified in mid-‐2005 (for consistency with IFRS, expansionary shock) –  modified in 2008:Q4 (to allow banks to use more the provision funds built in good +mes)

•  Forward-‐looking: provisions before any loss arrives

•  Countercyclical: Build up a buffer in good +mes to be used in bad +mes. The increase of provisioning in 2000 was over and above specific and general loan-‐loss provisions. In the crisis +mes, there is a regulatory reduc+on of this type of provisioning è neutral over the cycle

•  Tier-‐2 Capital

14



A simple countercyclical mechanism •  In periods of expanding credit, a buffer of provisions is being built up to be

used in crisis +mes –  We analyze the introduc+on in 2000 and a modifica+on in 2005

•  In periods when specific losses materialize in individual loans (crisis +mes), the banks can draw down from the previously built-‐up buffer of provisions –  We analyze the ex-‐ante pre-‐crisis built-‐up buffers in the recent crisis

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

•  Formula: Automa+c increase of dynamic provisions when current bank NPLs are lower than average value over the cycle (good +mes); a decrease when it is higher (crisis +mes). Same formula for everybody but affected differently to different banks depending on each bank’s loan poryolio

15



Empirical iden+fica+on

•  Using a difference-‐in-‐difference approach, we compare bank lending before and afer the different shocks: –  policy shocks in good +mes: introduc+on in mid-‐2000 (and change in

2005) of the new regula+on –  exploit 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:Q3

•  We differen+ate across banks with varying suscep+bility to the shocks and employ firm*+me fixed effects to control for +me-‐varying observed and unobserved firm heterogeneity fundamentals à iden+fy credit availability –  control also for other key bank characteris+cs –  all margins of lending, firm and bank heterogeneity, and real effects

16



Bank’s suscep+bility to the shocks: Buffers

•  For the policy shocks in good +mes: –  new formula applied to the exis+ng loan poryolio for each bank yielding a bank-‐specific amount of new funds to be provisioned (over total assets)

–  the 2005 policy shock changed the ini+al 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

17



Credit register

•  Credit register from Spain matched with bank and firm relevant informa+on (in e.g. 2007:Q2: 100,000 firms, 175 banks, 600,000 loans)

•  Exhaus+ve loan (bank-‐firm) level data on all outstanding business loan contracts (including loan applica+ons) at a quarterly frequency

•  We calculate the total exposures by each bank to each firm in each quarter from 1999:I to 2010:IV –  The sample period includes one year before the ini+al shock (to run placebo

tests) and we analyze 3 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 commihed loans) –  Intensive and extensive margin of lending (also loan applica+ons)

18



Benchmark loan-‐ and firm-‐ level equa+ons and hypotheses

•  Simple model: credit change due to a (i) secular trend, (ii) firm fundamentals/demand and (iii) bank capital shock (credit supply), (iv) idiosyncra+c shock

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

•  Bank capital buffer shocks (called buffers): –  Good +mes: policy shocks of mid 2000 and mid 2005 –  Bad +mes: crisis shock with pre-‐crisis provision buffers (from 2007:Q1) and policy

shock in 2008:Q4

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

bank type (commercial, saving and coop banks)

•  Es+mate similar firm-‐level regression to check credit subs+tu+on & real effects

!Creditb, f =! +" f +#b +$b, f


Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina 19

Results: summary Countercyclical capital buffers 1.  Mi+gate bank credit supply cycles

–  They strongly contracted credit availability (volume and cost) during good +mes, but strongly expanded it during the recent crisis •  Weaker banks more affected

2.  Have posi+ve aggregate firm-‐level real effects –  Firm bank credit availability and real variables (almost) NOT affected in good +mes

–  But strong, POSITIVE impact in crisis +mes for firm credit availability, employment, total assets, and survival!

3.  Smaller firms more benefited in crisis +mes; no reduc+on of risk-‐taking in good +mes

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


Our contribu+ons •  We exploit policy shocks to bank capital (countercyclical

buffers) both in good and bad +mes to iden+fy the impact of bank capital on credit supply:

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

2.  In Jiménez, Ongena, Peydró and Saurina (AER, 2012) we find that credit supply is pro-‐cyclical in GDP and monetary condi+ons and stronger for banks with a lower capital ra+o •  We used lagged bank capital. But bank capital is a key

strategic variable and à likely endogenous •  Our innova+on: to exploit the policy shocks affec+ng bank

capital: causality from bank capital to the supply of credit

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


Conclusions and policy implica+ons •  Iden+fy countercyclical bank capital buffers effects on credit supply

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

•  Results –  Countercyclical capital buffers mi+gate credit supply cycles!! –  Firms are more affected during crisis +mes when switching from banks

with low to high capital buffers is difficult. Posi+ve, strong real effects!! –  MM does not hold for banks and crucial for macro real effects

•  Important policy implica+ons for: –  Basel III, current debate in the EU, bank bailouts, monetary policy and, in

general, for macropruden+al policy –  Individual bank capital (not only aggregate) mahers in crises!

22



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

24



Similar results for extensive margin and for credit cost and maturity

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

25

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



!"#$%

!"%

!&#'%

!&#(%

!&#)%

!&#$%

&%

&#$%

&#)%

&#(%

&#'%

*'+)

%**+"

%**+$

%**+,

%**+)

%&&+"

%&&+$

%&&+,

%&&+)

%&"+"

%&"+$

%&"+,

%&"+)

%&$+"

%&$+$

%&$+,

%&$+)

%&,+"

%&,+$

%&,+,

%&,+)

%

!"#$%&#''()'*+)%-./01%2304451%

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

26

Also for credit drawn, cost and maturity with similar results



!"#$%

!"%

!&#'%

!&#(%

!&#)%

!&#$%

&%

&#$%

&#)%

&#(%

&#'%

*'+)

%**+"

%**+$

%**+,

%**+)

%&&+"

%&&+$

%&&+,

%&&+)

%&"+"

%&"+$

%&"+,

%&"+)

%&$+"

%&$+$

%&$+,

%&$+)

%&,+"

%&,+$

%&,+,

%&,+)

%

!"#$%&#''()'*+)%-./01%2304451% 677857095%2304451%

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

27



!"#$%!"#&'%!"#&%!"#('%!"#(%!"#)'%!"#)%

!"#"'%"%

"#"'%"#)%

*+,$%

**,)%

**,(%

**,&%

**,$%

"",)%

"",(%

"",&%

"",$%

"),)%

"),(%

"),&%

"),$%

"(,)%

"(,(%

"(,&%

"(,$%

"&,)%

"&,(%

"&,&%

"&,$%

!"#$%&'()'*)++,(-./0$1)/

Economic effects and summary

•  An increase of one standard devia+on in buffers in 2000:II reduces the commihed 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 elas+ci+es, probably due to an expansionary shock)

28



Crisis shock, pre-‐crisis buffers & low buffers when policy shock, loan level data & difference in log credit volume

29



Similar results for extensive margin and for credit cost; but shorter maturity

Crisis shock, pre-‐crisis buffers, loan level data & +me-‐varying coefficients of buffers on credit volume

30



-‐0.050

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450 07Q2

07Q3

07Q4

08Q1

08Q2

08Q3

08Q4

09Q1

09Q2

09Q3

09Q4

ΔLog Commitment Local Channel

Crisis shock, low buffers & policy shock, loan level data & +me-‐varying coefficients of buffers on credit volume

31



-‐0.020

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

07Q2

07Q3

07Q4

08Q1

08Q2

08Q3

08Q4

09Q1

09Q2

09Q3

09Q4

ΔLog Commitment Local Channel

Crisis shock, low buffers & policy shock, loan level data & +me-‐varying coefficients of buffers on credit dropped

32



-‐0.07

-‐0.06

-‐0.05

-‐0.04

-‐0.03

-‐0.02

-‐0.01

0

0.01

0.02

07Q2

07Q3

07Q4

08Q1

08Q2

08Q3

08Q4

09Q1

09Q2

09Q3

09Q4

Loan Dropped?

Crisis shock, low buffers & policy shock, loan level data & +me-‐varying coefficients of buffers on credit cost

33



-‐0.02 -‐0.015 -‐0.01 -‐0.005

0 0.005 0.01

0.015 0.02 0.025 0.03 0.035

07Q2

07Q3

07Q4

08Q1

08Q2

08Q3

08Q4

09Q1

09Q2

09Q3

09Q4

ΔDrawn-‐to-‐Commited Ra+o

Summary of economic effects

•  An increase of one standard devia+on in buffers in 2006:IV increases the commihed 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 devia+on in buffers in 2000:II reduces the commihed volume of credit: –  at the bank (loan) level: by 4 percent –  at the firm level: by 1.5 percent

34



Numbers

•  The ra+o 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% weigh+ng, the coverage ra+o 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 ra+o 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 ra+o 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

35

Summary sta+s+cs of buffers

•  In 2000:II: –  Buffers has an average of 0.46 and the standard devia+on is 0.09

–  Only correlated to real estate exposure, bank-‐type and collateralized loans. Not correlated to other bank, firm and loan characteris+cs

•  In 2006:IV: –  Average of pre-‐crisis buffers is 1.1 and the standard devia+on is 0.21

–  Not correlated to firm and loan characteris+cs, but to some bank characteris+cs (not to bank type)

36



Current provisions

•  Specific provisions cover incurred losses already iden+fied in a specific loan

•  General (dynamic) provisions cover incurred losses not yet individually iden+fied in a specific loan through a collec+ve assessment for impairment

37



Basic formula

•  Currently, Spain has specific provisions (dot.espe) and general provisions (dot.gen)

•  General provisions are set aside according to:

Ct is the stock of loans and ΔCt its varia+on α is the average es+mate of the credit loss β is the historical average specific provision

38

tt

ttt C

CespedotCgendot ).

(. −+Δ= βα



Specific mechanism

•  α & β based on 6 risk classes calibrated with historical credit data: –  (0%; 0.6%; 1.5%; 1.8%; 2%; 2.5%) for α –  (0%; 0.11%; 0.44%; 0.65%; 1.1% y 1.64%) for β

•  Six homogeneous groups: 1.  zero risk (cash, public sector debt) 2.  home mortgages with LTV below 80%, corporates with ra+ng A or above 3.  loans with real guarantees and home mortgages with LTV above 80% 4.  rest of loans, including corporates and SMEs 5.  consumer durables financing 6.  credit cards and overdrafs

•  Though the formula is the same for all banks, it affects banks differen+ally (since banks have different loan poryolios)

39

⎟⎠

⎞⎜⎝

⎛−+Δ=⎟⎟

⎠

⎞⎜⎜⎝

⎛−+Δ= ∑∑∑∑

====

6

1

6

1

6

1

6

1.

..

ititi

iitiit

i it

iti

iitit espedotCCC

CespedotCgendot βαβα



Amount of total net loan loss provisions (flow)

40

0

2.000

4.000

6.000

8.000

10.000

12.000

dic-‐96

jun-‐97

dic-‐97

jun-‐98

dic-‐98

jun-‐99

dic-‐99

jun-‐00

dic-‐00

jun-‐01

dic-‐01

jun-‐02

dic-‐02

jun-‐03

dic-‐03

jun-‐04

dic-‐04

jun-‐05

dic-‐05

jun-‐06

dic-‐06

jun-‐07

dic-‐07

jun-‐08

dic-‐08

jun-‐09

million €



Provision funds over total loans

41

0,0

0,5

1,0

1,5

2,0

2,5

Dec-‐00 Dec-‐01 Dec-‐02 Dec-‐03 Dec-‐04 Dec-‐05 Dec-‐06 Dec-‐07 Dec-‐08 Dec-‐09

%

Total provisions Specific provisions General provisions



macroprudential policy, countercyclical bank capital buffers and credit supply: evidence from the...

Education

bank capital impact

higher bank capital

higher bank capital

higher capital

banks credit supply

capital skin

lihle capital

bn of capital