macroprudential policy, countercyclical bank capital buffers and credit supply: evidence from the...
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Gabriel Jiménez (Banco de España) Steven Ongena (Tilburg) José‐Luis Peydró (UPF and Barcelona GSE) Jesús Saurina (Banco de España)TRANSCRIPT
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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?
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Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
23
Policy shock of July-‐2000, loan level data & difference in log credit volume
24
Introduc+on – 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
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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Policy shock of July-‐2000, loan level data & +me-‐varying coefficients of buffers on credit cost
27
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
Crisis shock, pre-‐crisis buffers & low buffers when policy shock, loan level data & difference in log credit volume
29
Introduc+on – 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; but shorter maturity
Crisis shock, pre-‐crisis buffers, loan level data & +me-‐varying coefficients of buffers on credit volume
30
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
-‐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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
-‐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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
-‐0.07
-‐0.06
-‐0.05
-‐0.04
-‐0.03
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07Q3
07Q4
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09Q2
09Q3
09Q4
Loan Dropped?
Crisis shock, low buffers & policy shock, loan level data & +me-‐varying coefficients of buffers on credit cost
33
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
-‐0.02 -‐0.015 -‐0.01 -‐0.005
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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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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 €
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina
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
Introduc+on – Policy shocks – Empirical Strategy – Results – Conclusions
Gabriel Jiménez, Steven Ongena, José-‐Luis Peydró and Jesús Saurina