Bank Leverage and Monetary Policy s Risk-Taking Channel: Evidence from the United States

Transcription

1 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

2 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

3 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)

4 Before crisis Macro looked OK Euro area United States Average of other economies Core CPI Inflation Output Gap : Q4 1 Japan omitted. 2 Estimate of output gap using rolling Hodrick-Prescott filter : Q4-3

5 Figure 8. Credit Growth and Monetary Policy Credit Growth and Core Inflation (Selected countries that had a boom in the run -up and a crisis in ) United Kingdom 2007 Ireland Core inf lation Credit (right axis) T-5 T-4 T-3 T-2 T-1 T Core inf lation Credit (right axis) T-5 T-4 T-3 T-2 T-1 T 50 0 Spain Greece Core inf lation Credit (right axis) T-5 T-4 T-3 T-2 T-1 T Core inf lation Credit (right axis) T-5 T-4 T-3 T-2 T-1 T 50 0 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. 9

6

7 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

8 Post crisis 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)

9 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

10 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

11 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

12 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 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 )

13 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

14 Preliminary evidence Federal Funds Rate (detrended) (in %)

15 Empirical model Federal fund rate (start of quarter) Size Maturity Dummy secured Loan-specific variables Personal income Change in CPI Unemployment rate Change in house prices Regional macro variables σ kjit = α i + λ j + βr t + γk it + δk it r t + θx kt +μy it +ρz jt + ε kjit Measure of loan riskiness Bank capitalasset ratio Tier 1 Total CAR Stock-mkt-cap-to assets Bank-specific variables (size) Size (total assets)

16 Baseline regression 1 Dependent variable: Loan risk rating (1) (2) (3) (4) (5) Target federal funds rate ** (0.007) *** (0.006) *** (0.009) *** (0.008) *** (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 R Obs 994, , , , ,287

17 Baseline regression 2 Dependent variable: Loan risk rating Target federal funds rate (0.009) Tier 1 capital ratio (0.429) Tier 1 capital ratio target federal funds (1) (2) (3) (4) *** (0.082) (0.015) Total capital ratio (0.395) Total capital ratio target federal funds *** (0.077) (0.016) Market cap 0.575*** (0.199) Market capitalization target federal funds ** (0.034) Time effects 1.109*** (0.372) *** (0.081) R Obs 994, , , ,287

18 Baseline regression 2 Dependent variable: Loan risk rating Target federal funds rate (0.009) Tier 1 capital ratio (0.429) Tier 1 capital ratio target federal funds (1) (2) (3) (4) *** (0.082) (0.015) Total capital ratio (0.395) Total capital ratio target federal funds *** (0.077) (0.016) Market cap 0.575*** (0.199) Market capitalization target federal funds ** (0.034) Time effects 1.109*** (0.372) *** (0.081) R Obs 994, , , ,287

19 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

20 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

21 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)

22 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

23 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 Banking risk Banking sector Banking system Condition of the banking system Financial conditions Financial stability Financial system Health of the banking system Risks to the financial system Stability of the financial system Systemic Systemic risk Troubles of the banking system 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

24 State/US income correlation Dependent variable: Loan risk rating (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 ** (0.014) *** (0.004) (0.016) (0.008) Tier 1 capital ratio (0.918) * (0.637) (0.930) (0.590) Tier 1 capital ratio target federal funds * (0.131) *** (0.095) R Obs 561, , , ,645

25 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

26 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 and Split sample in years with many/few bank failures Results die in crisis years

27 Crisis and distress Dependent variable: Loan risk rating (1) Crisis years (2) Non-crisis years (3) Years with many bank failures (4) Years with few bank failures Target federal funds rate * (0.016) (0.011) (0.024) 0.017* (0.008) Tier 1 capital ratio (0.703) (0.605) (0.507) (0.727) Tier 1 capital ratio target federal funds 0.263* (0.133) *** (0.109) (0.187) *** (0.096) R Obs 254, , , ,958

28 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

29 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

30 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

31 End Thank you