Bank Leverage and Monetary Policy’s · Banking system 15 19 3 3 12 16 0.077 0.077 0.600 0.800 Condition of the banking system 2 2 2 2 0 0 0.051 0.051 0.000 0.000 Financial conditions

Bank Leverage and Monetary Policy’s

Risk-Taking Channel:

Evidence from the United States

by

Giovanni Dell’Ariccia (IMF and CEPR)

Luc Laeven (IMF and CEPR)

Gustavo Suarez (Federal Reserve Board)

CSEF – Unicredit Conference, Naples, March 21, 2014

The views in this presentation do not necessarily reflect those of the IMF, IMF Board, Federal Reserve System, or its Board of Governors

Paper summary

Ask whether bank extend riskier loans when monetary policy’s

stance is easier

Employs loan-level confidential Fed dataset

Allows for ex-ante measure of loan riskiness

First to use disaggregated commercial bank data for US

Grounded in basic theoretical model

Look at how bank capitalization affects bank risk taking incentives

when monetary policy changes

Motivation

Many see easy monetary policy conditions in the 2000s as

major factor behind the crisis (Borio and Zhu, Adrian and Shin,

2009, Taylor, 2009)

Renewed debate: are low interest rates setting the stage for

future crises? (e.g., Rajan (2010))

Interest rate policy affects the quality and not just the quantity

of credit

Risk-taking channel of monetary policy (Borio/BIS)

Before crisis … Macro looked OK

-3

-2

-1

0

1

2

2000 02 04 06 08:

Q4

Output Gap2Core CPI Inflation

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

2000 02 04 06 08:

Q4

Euro area United States Average of other economies1

1 Japan omitted.2 Estimate of output gap using rolling Hodrick-Prescott filter.

9

Figure 8. Credit Growth and Monetary Policy(Selected countries that had a boom in the run -up and a crisis in 2007-08)

Sources: IMF International Financial Statistics, World Economic Outlook; staff calculations.Notes: Credit is indexed with a base value of 100 five years prior to the crisis.

0

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T-5 T-4 T-3 T-2 T-1 T

United Kingdom 2007

Core inf lation

Credit (right axis)0

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T-5 T-4 T-3 T-2 T-1 T

Ireland 2008

Core inf lation

Credit (right axis)

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Spain 2008

Core inf lation

Credit (right axis)

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T-5 T-4 T-3 T-2 T-1 T

Greece 2008

Core inf lation

Credit (right axis)

Credit Growth and Core Inflation

Pre crisis: a theory and policy gap

Macro models often ignored credit

Models with financial accelerators explored primarily how changes in

monetary policy affected the riskiness of borrowers

IC constraints generally bind, focus on quantity rather than quality

Little focus on risk attitude of the banking system

Banking literature focused on excessive bank risk-taking who:

Operate under limited liability

Are subject to asymmetric information

But this literature largely ignored monetary policy

Similar gap in policy making

Monetary policy considered financial stability the real of regulators

But regulators were focused more on individual banks than the system

Many observers have argued that monetary policy had an important role in

the recent crisis by providing intermediaries with the wrong incentives

Borio et al. (2008)

Several papers relate low interest rate environment to crisis

Overly loose monetary policy (Taylor, 2009)

Abundant liquidity – search for yield (Rajan, 2005, Acharya/Naqvi, JFE 2012)

Lending standards (Dell’Ariccia/Marquez, JF 2006, Gorton/He, RES 2008)

Increase in leverage and lower screening (Adrian and Shin, 2008, AER 2009,

Dell’Ariccia et al. JET 2013)

Debate on whether ultra-low rates and the macro bailout are seeding the

ground for new crisis:

Rajan ( NY Times 2010)

Acharya /Yorulmazer (JFI 2007), Diamond/Rajan (JPE 2012), Farhi /Tirole

(AER 2012)

Post crisis

Existing empirical work

Several papers used non-U.S. data:

Ioannidou, Ongena, and Peydró (2009): Bolivia

Altunbas, Gambacorta, and Marques-Ibañez (2010), Maddaloni and

Peydró (RFS 2011): Lending standards euro area (and US)

Jimenez et al. (ECM forthcoming): Spain

Very few looked at U.S. data:

Lown and Morgan (JMCB 2006): lending standards (not significant)

Paligorova and Santos (2012), Delis et al. (2012): Differential spreads on

syndicated loans

Buch/Eickmeier/Prieto (2011): aggregate version of STBL

Preview of results

1. We use confidential loan-level data from the Fed’s Survey of

Terms of Business Lending to measure ex-ante bank risk-taking

for US banks

2. We find a negative relation between the level of short-term

interest rates and bank risk-taking

3. We find that the strength of this relationship depends on

banks’ capital structure

4. Results are statistically significant and robust. But economic

magnitudes are relatively small

Theoretical background

(At least) Two opposite forces link the policy rate with bank risk taking

Risk shifting: Higher deposit rates reduce profits in case of success (classic

effect in models with limited liability)

Portfolio rebalancing: Higher yields on safe assets reduce portfolio risk

(standard in asset allocation models)

Net effect theoretically ambiguous (although additional channels such as

leverage can help determine it; Dell’Ariccia, Laeven, Marquez JET 2014)

The first effect is the greater when limited liability more binding: the lower

the bank capitalization

So we should observe a differential effect of MP changes across banks

When rates are cut, less capitalized banks should increase risk taking more

Data: Survey of Terms of Business Lending

Loan-level data from the Fed’s Survey of Terms of

Business Lending (STBL)

Also Call Report data for individual banks

Macro variables at state level

STBL: All individual new loans extended on first

business week of middle month of quarter since 1977

400 banks / 60 percent of US banking system assets

Since 1997, the STBL has asked banks to report the

internal risk rating of each new loan ( σ )

Data: Survey of Terms of Business Lending

The internal risk rating for the loan is an increasing,

discrete index of loan riskiness:

1 = Minimal risk

2 = Low risk

3 = Moderate risk

4 = Acceptable risk

5 = Special mention or classified asset

Preliminary evidence

2.4

2.6

2.8

33.2

3.4

Ris

k o

f lo

an

s (

detr

en

ded

)

-4 -2 0 2 4Federal Funds Rate (detrended) (in %)

𝜎𝑘𝑗𝑖𝑡 = 𝛼𝑖 + 𝜆𝑗 + 𝛽𝑟𝑡 + 𝛾𝐾𝑖𝑡 + 𝛿𝐾𝑖𝑡𝑟𝑡 + 𝜃𝑋𝑘𝑡+𝜇𝑌𝑖𝑡+𝜌𝑍𝑗𝑡 + 휀𝑘𝑗𝑖𝑡

Measure of loan riskiness

Bank capital-asset ratio

Federal fund rate (start of quarter)

Loan-specific variables

Bank-specific variables (size)

Regional macro variables

•Tier 1 •Total CAR •Stock-mkt-cap-to assets

•Size •Maturity •Dummy secured

•Personal income •Change in CPI •Unemployment rate •Change in house prices

•Size (total assets)

Empirical model

Baseline regression 1

Dependent variable: Loan risk rating

(1) (2) (3) (4) (5)

Target federal funds rate

-0.016** (0.007)

-0.021*** (0.006)

-0.031*** (0.009)

-0.031*** (0.008)

-0.031*** (0.008)

Bank and region fixed effects?

No Yes Yes Yes Yes

Region controls? No No Yes Yes Yes

Bank controls? No No No Yes Yes

Loan controls? No No No No Yes

R2 0.001 0.169 0.170 0.170 0.183

Obs 994,287 994,287 994,287 994,287 994,287



(1) (2) (3) (4)

Target federal funds rate -0.004 (0.009)

-0.003 (0.015)

0.014 (0.016)

Time effects

Tier 1 capital ratio 0.267 (0.429)

1.109*** (0.372)

Tier 1 capital ratio × target federal funds

-0.317*** (0.082)

-0.389*** (0.081)

Total capital ratio 0.661 (0.395)

Total capital ratio × target federal funds

-0.239*** (0.077)

Market cap 0.575*** (0.199)

Market capitalization × target federal funds

-0.077** (0.034)

R2 0.183 0.183 0.197 0.188

Obs 994,287 994,287 994,287 994,287



(1) (2) (3) (4)

Target federal funds rate -0.004 (0.009)

-0.003 (0.015)

0.014 (0.016)

Time effects


1.109*** (0.372)


-0.317*** (0.082)

-0.389*** (0.081)

Total capital ratio 0.661 (0.395)

Total capital ratio × target federal funds

-0.239*** (0.077)

Market cap 0.575*** (0.199)

Market capitalization × target federal funds

-0.077** (0.034)

R2 0.183 0.183 0.197 0.188

Obs 994,287 994,287 994,287 994,287

Results so far

Monetary tightening:

Decreases bank risk taking

Less so for lowly capitalized banks (consistent with risk shifting)

Results statistically significant and robust

Various capitalization measures

Inclusion of several controls

Fixed effects specification

Economic effect relatively small

Average risk rating 3.43 with 0.85 s.d.

At sample mean Tier 1 capital ratio a 1 s.d. increase in rates reduce risk by

about 1/12 of the rating s.d. (100bp hike would reduce rating by about 0.04)

Effect increases to 1/10 of s.d. at one s.d. below Tier 1 mean

Loans under commitment

Firms may draw on pre-approved credit lines

Actual new loan quality may differ from a bank’s chosen mix,

when there are monetary policy and other macro surprises

Exclude loans made under commitment

About 25 percent of observations

Results roughly the same, but improve a bit in size and

significance

Main identification concerns

Monetary policy may react to risk taking

Financial stability considerations may lead FOMC to cut rates when risk is

high to shore up banks’ balance sheets

Simultaneous causality (although less of a concern for new loans than for

stock)

Bank risk rating endogenous to monetary policy

Loan officers more optimistic during expansions

Underestimate risk

GDP correlated with policy rate

Higher rates may correspond to periods of euphoria (low risk ratings)

Endogeneity of monetary policy

Examination of minutes

Search for keywords

Pre-2007, little evidence that bank risk taking played significant role in MP

decisions

Consistent with official line (“it is regulators’ job”)

Focus on sub-samples for which concern less serious

States not correlated with national cycle (also answers second concern)

State with little income volatility

Small/local banks less exposed to national trends (exclude top quintile)

Banks in states without large banks

Frequency of keywords in FOMC minutes

Keyword

# of times the keyword

was used in FOMC

meetings from

1997Q2—2011Q4


was used in FOMC

meetings from

1997Q2—2006Q4


was used in FOMC

meetings from

2007Q1—2011Q4

Frequency of times the

keyword was used in

FOMC meetings from

1997Q2—2006Q4

Frequency of times the

keyword was used in

FOMC meetings from

2007Q1—2011Q4

Conservative Liberal Conservative Liberal Conservative Liberal Conservative Liberal Conservative Liberal

Bank risk 0 0 0 0 0 0 0.000 0.000 0.000 0.000

Banking risk 0 0 0 0 0 0 0.000 0.000 0.000 0.000

Banking sector 10 14 1 1 9 13 0.026 0.026 0.450 0.650

Banking system 15 19 3 3 12 16 0.077 0.077 0.600 0.800

Condition of the banking system 2 2 2 2 0 0 0.051 0.051 0.000 0.000

Financial conditions 112 351 74 187 39 167 1.897 4.795 1.950 8.350

Financial stability 14 17 0 0 14 17 0.000 0.000 0.700 0.850

Financial system 11 19 1 2 10 17 0.026 0.051 0.500 0.850

Health of the banking system 0 0 0 0 0 0 0.000 0.000 0.000 0.000

Risks to the financial system 1 1 0 0 1 1 0.000 0.000 0.050 0.050

Stability of the financial system 2 3 0 0 2 3 0.000 0.000 0.100 0.150

Systemic 2 4 0 0 2 4 0.000 0.000 0.100 0.200

Systemic risk 0 0 0 0 0 0 0.000 0.000 0.000 0.000

Troubles of the banking system 1 1 0 0 1 1 0.000 0.000 0.050 0.050

Notes: Frequency is determined as the number of times a word has been used within a time period divided by the number of quarters in that time period. Conservative = the number

of reports the word appears in (if a word appears several times in a report, that's not counted). Liberal = the total number of times the word appears in the reports.

Source: FOMC Minutes

State/US income correlation


(1) States with

high correlation

with US GDP

(2) States with

low correlation

with US GDP

(3) States with

high correlation

with US GDP

(4) States with

low correlation

with US GDP

Target federal funds rate

-0.037** (0.014)

-0.021*** (0.004)

-0.018 (0.016)

0.009 (0.008)


-1.153* (0.637)

0.995 (0.930)

-0.736 (0.590)


-0.226* (0.131)

-0.349*** (0.095)

R2 0.212 0.147 0.212 0.147

Obs 561,642 432,645 561,642 432,645

Endogeneity of rating system

Harder to address. Do not have a fully satisfactory answer. Yet…

Results on states with low income correlation with US GDP

growth (and hence MP) comforting.

Control explicitly for GDP growth and recessions

Focus on deviations from “regional” conditions:

FF target minus state CPI

Deviations from “regional” Taylor rule

Again, results remain roughly the same

Aren’t crises different?

Monetary policy more likely to react to risk taking during distress

Also, banks may behave radically differently during crises

Split sample 1997-2007 and 2008-2010

Split sample in years with many/few bank failures

Results “die” in crisis years

Crisis and distress


(1) Crisis years

(2) Non-crisis

years

(3) Years with many bank

failures

(4) Years with few bank failures

Target federal funds rate -0.031* (0.016)

0.008 (0.011)

0.026 (0.024)

0.017* (0.008)


0.056 (0.605)

-0.712 (0.507)

0.148 (0.727)


0.263* (0.133)

-0.549*** (0.109)

0.063 (0.187)

-0.394*** (0.096)

R2 0.200 0.194 0.192 0.206

Obs 254,761 739,526 348,329 645,958

Additional Results

This are not in this version of the paper (apologies to Hans)

Multinomial logit estimations and other tests to take into account

non-linearities (such as the effect of the lowest rated loans)

Link our risk rating variable to future NPLs

Better controls for macro conditions (throughout the paper. We

already had some in this version). Taylor residuals etc.

Results remain qualitatively the same

Conclusions I

Evidence that risk-taking by banks is negatively correlated with the level of short-term interest rates

This negative relationship is stronger for banks with higher capital ratios

Results are statistically robust, but economically small

Conclusions II

If taken at face value, little reason to alter design of monetary policy

Yet, there are other ways for banks to take risk

Liability side: Leverage, Maturity mismatches

Off-balance-sheet activities

End

Thank you

Documents

Bank Leverage and Monetary Policy’s · Banking system 15 19 3 3 12 16 0.077 0.077 0.600 0.800 Condition of the banking system 2 2 2 2 0 0 0.051 0.051 0.000 0.000 Financial conditions