Bank Leverage and Monetary Policy´s Risk-Taking Channel ...€¦ · Motivation Many see easy...
Transcript of Bank Leverage and Monetary Policy´s Risk-Taking Channel ...€¦ · Motivation Many see easy...
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