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)

May 2014

* Our views not necessarily reflect those of the IMF, IMF Board, Federal Reserve System, or its Board of Governors

Paper summary

Ask whether banks 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 Focus on riskiness of new loans

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 (Adrian/Shin, 2009, Taylor, 2009)

Renewed debate: are low interest rates setting the stage for future crises? (e.g., Rajan, 2010; Krishnamurthy/Vissing-Jorgenson, 2011; Farhi/Tirole, 2012, Chodorow-Reich, 2014)

Should monetary policy frameworks include financial stability considerations? (Woodford, 2012; Stein, 2014)

Interest rate policy affects the quality and not just the quantity of credit: Risk-taking channel of monetary policy (Borio/Zhu)

Before the crisis: a theory 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 risk-taking by banks who: Operate under limited liability Are subject to asymmetric information

But this literature largely ignored monetary policy

Similar gap in policy making

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, 2012) Lending standards (Dell’Ariccia/Marquez, 2006, Gorton/He, 2008) Increase in leverage and lower screening (Adrian and Shin, 2008, 2009, Dell’Ariccia

et al. 2013)

Debate on whether ultra-low rates and the macro bailout are seeding the ground for new crisis: Rajan, 2010, Acharya/Yorulmazer, 2007, Diamond/Rajan, 2012, Farhi/Tirole, 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 (2011): Lending standards euro area (and US) Jimenez et al. (forthcoming): Spain

Very few looked at U.S. data: Lown and Morgan (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

Different datasets/countries/measures of risk

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 small

Outline

1. Theoretical predictions on link between interest rates and bank risk taking

2. A simple model

3. Data: Survey of Terms of Business Lending

4. Empirical strategy

5. Results

6. Conclusions

Theoretical predictions

Portfolio allocation models: Exogenous decrease in yield on safe assets will lead to greater risk taking (e.g. Fishburn/Porter, 1976)

Corporate finance models with limited liability: decrease in interest rate banks have to pay on deposits will reduce risk taking (risk shifting effect) – This effect is strongest for the least capitalized banks that are more exposed to agency problems (Stiglitz/Weiss, 1981, Hellman et al., 2000)

In models that take both into account, these effects partly offset each other (Dell’Ariccia et al. 2014): for less capitalized banks the classical risk shifting effect is stronger and thus the overall relationship between interest rates and risk taking is weaker

A simple theoretical framework

Banks protected by limited liability fund themselves with deposits and equity

The interest rate on deposits is closely tied to the monetary policy rate, rD=r* (deposit insurance).

Banks control probability of loan repayment, q, at a cost q2/2

Free entry ensures zero profits

Expected profits:

A simple theoretical framework

Equilibrium monitoring effort

Two opposite forces: 1. Risk shifting: Higher deposit rates for a given loan rate reduce

monitoring incentives 2. Pass-through: Higher deposit rates increase loan rates and thus increase

monitoring incentives 3. Second effect prevails, but risk shifting greater for low k: ∂q /∂r* > 0 and ∂2q /∂r*∂k > 0

Dell’Ariccia et al (2013): Endogenous leverage/market power

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

Ex ante measure of risk taking

We construct an ex ante measure of bank risk taking at the loan level using the STBL Based on assessment of risk at the time the loan was made unlike existing literature which uses measures of ex post

loan performance (affected by subsequent events) or risk measures at the firm level using information on past doubtful loans (e.g. Jimenez et al. forthcoming)

Focus on the extension of new loans; two advantages: reduces concerns about endogeneity of monetary policy focuses on a margin that is fully under control of a bank (in

contrast to the overall riskiness of its portfolio)

From theory to empirics

Since σ and q are inversely related, theory predicts: 1. A negative relationship between σ and the short-term

interest rate: ∂σ/∂r* < 0 2. The strength of this relationship is more pronounced

for well-capitalized banks: ∂2σ/∂r*∂k < 0

Preliminary evidence

3.1

3.2

3.3

3.4

3.5

3.6

Ave

rage

loan

risk

ratin

g

0 2 4 6

Target federal funds rate, percent

𝜎𝜎𝑘𝑘𝑗𝑗𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝜆𝜆𝑗𝑗 + 𝛽𝛽𝑟𝑟𝑖𝑖 + 𝛾𝛾𝐾𝐾𝑖𝑖𝑖𝑖 + 𝛿𝛿𝐾𝐾𝑖𝑖𝑖𝑖𝑟𝑟𝑖𝑖 + 𝜃𝜃𝜃𝜃𝑘𝑘𝑖𝑖+𝜇𝜇𝜇𝜇𝑖𝑖𝑖𝑖+𝜌𝜌𝜌𝜌𝑗𝑗𝑖𝑖 + 𝜀𝜀𝑘𝑘𝑗𝑗𝑖𝑖𝑖𝑖

Measure of loan riskiness

Bank capital-asset ratio

Federal fund rate (start of quarter)

Loan-specific variables

Bank-specific variables

Regional macro variables

•Tier 1 •Total CAR •Common stock/assets •Market cap/assets

•Size •Maturity •Dummy secured

•Income growth •Change in CPI •Unemployment rate •Change in house prices

•Size (total assets) •Net income/assets •Liquid assets/assets •Deposits/assets •ST deposits/deposits •Non-retail deposits/deposits •Loans/assets •C&I loans/loans

Empirical model

Baseline regressions

Dependent variable: Loan risk rating

(1) (2) (3)

Target federal funds rate -0.035** (0.006)

0.004 (0.009)

Tier 1 capital ratio -0.869*** (0.311)

0.203 (0.352)

1.424*** (0.326)

Tier 1 capital ratio × target federal funds -0.447*** (0.070)

-0.527*** (0.077)

Bank and state fixed effects? Yes Yes Yes

Quarter-year fixed effects? No No Yes

Bank controls? Yes Yes Yes

Loan controls? Yes Yes Yes

R2 0.169 0.170 0.174

Obs 1,348,857 1,348,857 1,348,857

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 qualitatively similar

Becomes baseline from now on

Loans not under commitment

Dependent variable: Loan risk rating

(1) (2) (3)

Target federal funds rate -0.026*** (0.004)

-0.007 (0.008)

Tier 1 capital ratio -1.915*** (0.363)

-1.414*** (0.371)

-0.047 (0.366)

Tier 1 capital ratio × target federal funds -0.220*** (0.064)

-0.246*** (0.071)

Bank and state fixed effects? Yes Yes Yes

Quarter-year fixed effects? No No Yes

Bank controls? Yes Yes Yes

Loan controls? Yes Yes Yes

R2 0.169 0.170 0.174

Obs 789,194 789,194 789,194

Economic effect

Economic effect small but depends on bank capitalization Average risk rating 3.4 with 0.8 s.d. One-s.d. decrease in r of 2.2 increases risk ratings by about 0.06 to 0.08 Interquartile decrease in r increases risk ratings for well-capitalized banks (with Tier 1 at 75th p’tile) by 0.04 more than for weakly-capitalized banks (with Tier 1 at 25th p’tile)

Alternative measures of capital

Dependent variable: Loan risk rating

(1) (2) (3)

Target federal funds rate 0.014 (0.009)

-0.024*** (0.004)

0.028* (0.015)

Total capital ratio -0.829** (0.392)

Total capital ratio × target federal funds

-0.339*** (0.057)

Common stock / assets 6.096*** (1.746)

(Common stock / assets) × target federal funds

-0.731*** (0.250)

Market cap / assets 0.922*** (0.211)

(Market cap / assets) × target federal funds

-0.175*** (0.040)

R2 0.203 0.203 0.243

Obs 789,194 789,194 239,374

Alternative interest rates

Dependent variable: Loan risk rating

(1) (2) (3)

Taylor rule residual -0.027*** (0.004)

-0.008 (0.008)

Tier 1 capital ratio -1.920*** (0.363)

-2.081*** (0.372)

-1.341*** (0.380)

Tier 1 capital ratio × Taylor rule residual

-0.204*** (0.066)

Target federal funds rate 0.003 (0.008)

Tier 1 capital ratio × target federal funds rate

-0.213*** (0.065)

GDP growth × target federal funds rate

-0.004*** (0.001)

NBER recession × target federal funds rate

-0.013*** (0.011)

R2 0.203 0.203 0.203

Obs 789,194 789,194 789,194

Results so far

Monetary tightening: Decreases bank risk taking Less so for lowly capitalized banks (consistent with risk shifting)

Results statistically significant and robust Loans not under commitment Various capitalization measures and interest rates Inclusion of several controls Fixed effects specification

Economic effect small but depends on bank capitalization

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 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”)

We already limit analysis to new loans not under commitment

Focus on sub-samples for which concern less serious States not correlated with national cycle (also answers second concern) Small/local banks less exposed to national trends (exclude top quintile) Banks in states without large banks

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 in crisis (2008-2010) and non-crisis periods

Split sample in years with many/few bank failures

Frequency of keywords in FOMC minutes

Keyword

# of times the keyword was used in FOMC

meetings from 1997Q2—2011Q4

# of times the keyword was used in FOMC

meetings from 1997Q2—2006Q4

# of times the keyword 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

Size, state cyclicality, and financial stability

Dependent variable: Loan risk rating

(1) Small banks

(2) States without

large banks

(3) States with

low correlation

with US GDP

(4) Non-crisis

years

Target federal funds rate 0.024* (0.013)

-0.011 (0.011)

0.035*** (0.009)

0.007 (0.009)

Tier 1 capital ratio -0.552 (0.455)

-0.674** (0.317)

-1.873*** (0.477)

-0.867 (0.624)

Tier 1 capital ratio × target federal funds

-0.373*** (0.096)

-0.325*** (0.075)

-0.564*** (0.087)

-0.420*** (0.100)

R2 0.335 0.243 0. 149 0.224

Obs 180,227 474,011 357,846 546,749

Rating bias?

We already control explicitly for state personal income growth, GDP growth, and recessions

Results on states with low correlation with US GDP comforting

Ratings system periodically checked by Federal Reserve Ratings (ex ante measure of risk) positively correlated with future (four-period ahead) nonperforming loans (ex post measure of risk)

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

And depends on economic conditions (stronger during non-crisis periods)

Results are statistically robust, but economically small

Conclusions II If taken at face value, little reason to alter design of

monetary policy

Yet, effect on overall riskiness of banks could be different We focus on specific margin of risk taking: the riskiness of

new loans There are other ways for banks to take risk (leverage, maturity

mismatches, off-balance-sheet activities)

It might be easier to establish existence of risk taking channel than to quantify reliably its importance

End

Thank you

Alternative risk measures and nonlinearities

Dependent variable: Loan risk rating

(1) Ratings,

WLS

(2) Interest

(3) Future NPLs

(4) Logit of

very risky loan

Target federal funds rate -0.006 (0.007)

1.094*** (0.026)

0.149*** (0.034)

Tier 1 capital ratio -1.604*** (0.358)

4.635** (2.294)

-5.949*** (1.338)

Tier 1 capital ratio × target federal funds

-0.230*** (0.062)

-1.360*** (0.215)

-2.406*** (0.383)

Average loan risk rating 0.001*** (0.000)

(Pseudo-)R2 0.204 0.738 0.427 0.039

Obs 789,194 1,032,079 12,391 789,194