Banking on Deposits:

Transcription

1 Banking on Deposits: Maturity Transformation without Interest Rate Risk Itamar Drechsler 1 Alexi Savov 2 Philipp Schnabl 2 1 Wharton and NBER 2 NYU Stern and NBER BIS Research Network Meeting September 28, 2018

2 Textbook View of Banking and Maturity Transformation 1. Banks borrow short term (issue deposits), lend long term (make loans, buy securities) - maturity/duration mismatch - pay short-term (floating) rate, receive long-term (fixed) rate 2. Earns term premium but creates exposure to interest rates - a rise in short rate interest expenses go up profits fall assets fall relative to liabilities, equity capital depleted - important at all times, not just in financial crises - different from run risk, applies to whole balance sheet 3. Seen as an important channel for monetary policy - bank balance sheet channel - idea that Fed impacts banks through their interest rate exposure Drechsler, Savov, and Schnabl (2017) 2

3 Estimated duration Banks Duration Mismatch Assets Liabilities Aggregate duration mismatch is about 4 years Under textbook view, a 100-bps level shift in rates leads to - 4 years of 100-bps lower net income (as % of assets) - in PV terms: a 4% drop in assets a 40% drop in equity since banks are levered 10 to 1; stock price drops on impact - shocks cumulative over time, 100 bps small by historical standards Drechsler, Savov, and Schnabl (2017) 3

4 Precious metals Fabricated products Rubber and plastics Other Steel Mining Apparel Shipping containers Consumer goods Oil and natural gas Electronics Personal services Computers Business supplies Beer and liquor Electrical equipment Textiles Printing and publishing Communication Recreation Pharmaceuticals Transportation Banks Construction Software Market Machinery Utilities Retail Entertainment Defense Hospitality Wholesale Construction materials Cars and trucks Business services Chemicals Control equipment Trading and investments Insurance Medical equipment Tobacco Agriculture Aircraft Food products Healthcare Ships and railroads Real estate Candy and soda Coal How Exposed are Bank Stocks to Interest Rates? 1. Regress FF49 industry portfolios on 1-year rate around FOMC days 4% 2% 0% -2% -4% -6% -8% 2. Bank stocks drop by just 2.4% per 100-bps rate shock ( 40%) - no more exposed than average nonfinancial firm or overall market Drechsler, Savov, and Schnabl (2017) 4

5 0% 6% 12% 18% Bank Cash Flows and Interest Rates Fed funds rate 1. Interest rates have varied widely and persistently over past 60 years Drechsler, Savov, and Schnabl (2017) 5

6 0% 6% 12% 18% Bank Cash Flows and Interest Rates Fed funds rate Interest income rate 1. Interest rates have varied widely and persistently over past 60 years 2. Banks interest income much smoother, reflecting long-term assets would suffer frequent and sustained losses if funded at Fed funds rate Drechsler, Savov, and Schnabl (2017) 5

7 0% 6% 12% 18% Bank Cash Flows and Interest Rates Fed funds rate Interest income rate Interest expense rate 1. Interest rates have varied widely and persistently over past 60 years 2. Banks interest income much smoother, reflecting long-term assets would suffer frequent and sustained losses if funded at Fed funds rate 3. Instead, banks interest expense much lower and smoother than Fed funds rate, even though liabilities are short-term Drechsler, Savov, and Schnabl (2017) 5

8 Why Is Banks Interest Expense so Low and Smooth? In Drechsler, Savov, Schnabl (2017, QJE) we show that: 1. This is due to banks market power in retail deposit markets allows banks to keep deposit rates low even as the short rate rises 2. On average, deposit rates increase by just 40 bps per 100-bps Fed funds rate increase - exploit differences in competition across branches of the same bank 3. Deposits represent over 70% of aggregate bank liabilities banks overall interest expense has a low sensitivity to interest rates Drechsler, Savov, and Schnabl (2017) 6

9 0% 6% 12% 18% Banks Net Interest Margin (NIM) 1. NIM = (Interest income - Interest expense)/assets Fed funds rate Net interest margin 2. NIM is uncorrelated with short rate goes against textbook view - corr( NIM, FF rate) 0; σ( NIM) = 0.13% (annual) Drechsler, Savov, and Schnabl (2017) 7

10 Banks Net Interest Margin (NIM) 1. NIM = (Interest income - Interest expense)/assets 18% 12% 6% 0% % Fed funds rate Bank NIM Treasury portfolio NIM 2. Construct NIM for Treasury portfolio with same duration mismatch as banks (but no deposit market power) - Treasury portfolio NIM much more sensitive to rates than bank NIM Drechsler, Savov, and Schnabl (2017) 7

11 0% 6% 12% 18% Banks Net Interest Margin (NIM) and ROA 1. ROA = NIM + Fee income - Operating costs - Loan losses Fed funds rate Net interest margin ROA 2. ROA is also uncorrelated with short rate - well below NIM, reflecting substantial operating costs, 2-3% of assets Drechsler, Savov, and Schnabl (2017) 7

12 Related literature 1. Coexistence of deposit-taking and lending - Diamond and Dybvig (1983), Gorton and Pennacchi (1990), Calomiris and Kahn (1991), Diamond and Rajan (2001), Kashyap, Rajan, and Stein (2002), Hanson, Shleifer, Stein, and Vishny (2015) - these papers focus on liquidity transformation/runs. We provide an explanation for maturity transformation 2. Interest rate risk and the balance sheet channel of monetary policy - Bernanke and Gertler (1995), Brunnermeier and Sannikov (2014, 2017), Begenau, Piazzesi, and Schneider (2015), Gomez, Landier, Sraer, and Thesmar (2016), Brunnermeier and Koby (2017) - our results suggest interest rate exposure is very small 3. Deposit pricing and market power - Hannan and Berger (1991), Neumark and Sharpe (1992), Driscoll and Judson (2013), Drechsler, Savov, and Schnabl (2017) - we focus on asset-side implication for maturity transformation Drechsler, Savov, and Schnabl (2017) 8

13 Model 1. Time t 0, short rate process f t 2. An infinitely-lived bank runs a deposit franchise - per-dollar operating cost c (branches, salaries, marketing, etc.) - paying c gives the bank market power: deposit rate = β Exp f t, where β Exp < 1 - Drechsler, Savov, and Schnabl (2017) provide microfoundations 3. Bank invests deposit dollars to maximize PV of future profits - no equity or long-term debt (for simplicity) - asset markets are complete, stochastic discount factor m t Drechsler, Savov, and Schnabl (2017) 9

14 Setup Bank solves: Risks: V 0 = max INC t E 0 [ t=0 s.t. E 0 [ t=0 m t ( INCt β Exp f t c ) ] m 0 m t m 0 INC t ] = 1 and INC t β Exp f t + c 1. Need to cover interest expenses, sensitivity β Exp to f t income must be sensitive enough to f t in case f t is high - yet β Exp < 1 is low because of market power 2. Also need to cover insensitive operating cost c income must be insensitive enough in case f t is low - must hold sufficient long-term (fixed-rate) assets Drechsler, Savov, and Schnabl (2017) 10

15 Result Under ex-ante free entry (zero rents): 1. V 0 = 0, income is pinned down: INCt = β Exp f t + c 2. Sensitivity matching: Income beta β Inc = INC t f t = β Exp Expense beta - bank is fully hedged against any shock to current or expected rates, or term premium - aggregate time series shows tight sensitivity matching - test in cross section 3. Bank can implement optimal policy by investing: - β Exp share of assets in short-term assets - 1 β Exp in long-term (fixed-rate) assets Drechsler, Savov, and Schnabl (2017) 11

16 Empirical Analysis 1. Call reports, all U.S. commercial banks, 1984 to we ve posted cleaned data on our websites 2. For each bank i, estimate interest expense and income betas IntExp i,t = α i + IntInc i,t = α i + 3 τ=0 3 τ=0 β Exp i,τ FF t τ + ε it β Inc i,τ FF t τ + ε it - IntExp = Interest expense/assets - IntInc = Interest income/assets - 4 quarterly lags of FF capture adjustment over a full year 3. Plot β Exp i = 3 τ=0 β Exp i,τ versus β Inc i = 3 τ=0 β Inc i,τ Drechsler, Savov, and Schnabl (2017) 12

17 Income versus Expense betas (all banks) 1. Bin scatter plot of β Inc i versus β Exp ; 100 bins, 168 banks per bin i Coef. = 0.768, R-sq. = Interest income beta Interest expense beta 2. Strong matching: tight linear relationship between income and expense betas, slope is close to 1 Drechsler, Savov, and Schnabl (2017) 13

18 Income versus Expense betas (top 5% of banks) 1. Bin scatter plot of β Inc i versus β Exp i Coef. = 0.878, R-sq. = Interest income beta Interest expense beta 2. Strong matching: tight linear relationship between income and expense betas, slope is close to 1 Drechsler, Savov, and Schnabl (2017) 13

19 Sensitivity matching (panel regression) 3 Stage1 : IntExp i,t = α i + β Exp i,τ FedFundst τ + ɛ i,t τ=0 3 Stage2 : IntInc i,t = α i + γ τ FedFunds t τ + δ IntExp i,t + ε i,t. τ=0 All banks Top 5% Top 1% (1) (2) (3) (4) (5) (6) IntExp (0.033) (0.034) (0.099) (0.099) (0.068) (0.076) γτ (0.031) (0.050) (0.050) Bank FE Yes Yes Yes Yes Yes Yes Time FE No Yes No Yes No Yes N R-sq Matching coefficient δ close to 1, especially for large banks a bank with no market power (expense beta = 1) predicted to hold only short-term assets (income beta = 1) a money market fund Drechsler, Savov, and Schnabl (2017) 14

20 Time Series of Interest Income and Expense Rates 0% 5% 10% Interest expense Interest income Low expense beta High expense beta Fed funds rate Average interest income and interest expense rate by expense beta (top vs. bottom 5%) - a non-parametric way to see matching in the cross section Drechsler, Savov, and Schnabl (2017) 15

21 ROA Betas vs. Expense Betas Coef. = 0.061, R-sq. = ROA beta Interest expense beta 1. No relationship between expense beta and ROA beta matching unaffected by non-interest income (e.g., fees) and costs 2. Similar result for expense beta vs. NIM beta (by construction) Drechsler, Savov, and Schnabl (2017) 16

22 Cross Section of Bank Equity FOMC Betas FOMC beta vs. β Exp Coef. = (s.e. = 1.080), R-sq. = FOMC beta vs. β Inc Coef. = (s.e. = 0.711), R-sq. = FOMC beta Interest expense beta FOMC beta Interest income beta 1. No relationship with either expense or income betas explained by sensitivity matching Drechsler, Savov, and Schnabl (2017) 17

23 Expense Betas and Asset Duration Coef. = , R-sq. = Loans and securities duration Expense beta 1. Lower expense beta higher asset duration (repricing maturity) - slope coefficient = 3.66 years - large relative to aggregate asset duration of 4.4 years Drechsler, Savov, and Schnabl (2017) 18

24 Cross Section of Bank Equity FOMC Betas FOMC beta vs. Asset duration Coef. = (s.e. = 0.102), R-sq. = FOMC beta Duration Assets 1. No relationship with asset duration explained by matching of long-term assets with deposit market power Drechsler, Savov, and Schnabl (2017) 19

25 Is Matching Driven by Liquidity (Run) Risk? Coef. = , R-sq. = Securities share Expense beta 1. Perhaps high-β Exp banks hold more short-term assets to insure against liquidity risk? - does not predict matching coefficient of one 2. High-β Exp banks hold more loans and fewer securities - but loans are illiquid inconsistent with liquidity risk explanation - consistent with matching: securities have higher duration than loans Drechsler, Savov, and Schnabl (2017) 20

26 Matching within Securities portfolio 3 Stage1 : IntExp i,t = α i + β Exp i,τ FedFunds t τ + ɛ i,t τ=0 3 Stage2 : IntIncTreasuries i,t = α i + γ τ FedFunds t τ + δ IntExp i,t + ε i,t. τ=0 All banks Top 5% (1) (2) (3) (4) (5) (6) Total Treasuries MBS Total Treasuries MBS IntExpRate (0.045) (0.054) (0.082) (0.142) (0.218) (0.364) Bank FE Yes Yes Yes Yes Yes Yes Time FE Yes Yes Yes Yes Yes Yes N R-sq Banks match sensitivities even within Treasury and MBS portfolio - highly liquid/integrated markets not driven by segmentation 2. Implications for asset pricing Drechsler, Savov, and Schnabl (2017) 21

27 Expense Betas and Market Concentration β Exp and Bank HHI Coef. = , R-sq. = Interest expense beta Bank HHI 1. Bank HHI is the average Herfindahl of all zip codes where the bank has branches Banks that face less local competition for deposits (high Bank HHI) have lower expense betas, especially for retail (e.g. savings) deposits Drechsler, Savov, and Schnabl (2017) 22

28 Expense Betas and Market Concentration (HHI) 3 ( ) IntExp i,t =α i + β 0 τ + β1 τ HHI i,t FedFunds t,t τ + ɛ i,t [Stage 1] τ=0 IntInc i,t 3 =α i + γ τ FedFunds t,t τ + δ IntExp i,t + ɛ i,t. [Stage 2] τ=0 Stage 1: (1) (2) β 1 τ 0.047*** 0.059*** (0.021) (0.016) R Stage 2: Interest income (1) (2) IntExp 1.264*** 1.278*** (0.186) (0.154) Bank FE Yes Yes Time FE No Yes N 624, ,204 R Less competition less sensitive interest expense (Stage 1) 2. Matching coefficient δ close to 1 (Stage 2) Drechsler, Savov, and Schnabl (2017) 23

29 Retail Deposit Betas and Within-Bank Estimation 1. Use retail-deposit betas to hone in on market power mechanism 2. Within-bank retail β Exp : - compute county-level retail betas using differences in deposit rates across branches of same bank, average across each bank s counties gives us geographic variation in β Exp purged of bank characteristics Retail β Exp Within-bank retail β Exp Stage 1: (1) (2) (3) (4) β 1 τ 0.550*** 0.565*** 0.109*** 0.110** (0.057) (0.056) (0.013) (0.013) R Stage 2: Interest income Interest income (1) (2) (3) (4) IntExp 1.259*** 1.264*** 1.185** 1.186** (0.136) (0.136) (0.114) (0.119) Bank FE Yes Yes Yes Yes Time FE No Yes No Yes N R Strong first stage, matching coefficient again close to one Drechsler, Savov, and Schnabl (2017) 24

30 Takeaways 1. Despite a large duration mismatch, banks are largely unexposed to interest rate risk 2. This is due to market power over deposits, which lowers the interest rate sensitivity of banks expenses 3. Banks invest in long-term assets to hedge their deposit franchise Deposits are the foundation of banking, drive maturity transformation - explains why deposit taking and long-term lending coexist under one roof - implies that narrow banking could make banks unstable, reduce long-term lending - implies that banks are largely insulated from the balance sheet channel of monetary policy Drechsler, Savov, and Schnabl (2017) 25

31 APPENDIX Drechsler, Savov, and Schnabl (2017) 26

32 Textbook view in Freixas and Rochet (2008) 1. On interest rate risk - interest rate risks are generated by the activity of maturity transformation of short-term deposits into long-term loans 2. On the transmission of monetary policy 1. Monetary policy increases interest rates. 2. Because of maturity mismatch, this generates a loss. 3. This, in turn, produces a capital decrease. 4. The capital requirement leads to a reduction in lending. Drechsler, Savov, and Schnabl (2017) 27

33 Boivin, Kiley, and Mishkin (2010) Expansionary monetary policy can lead to improved bank balance sheets in two ways. First, lower short-term interest rates tend to increase net interest margins and so lead to higher bank profits which result in an improvement in bank balance sheets over time. Second, expansionary monetary policy can raise asset prices and lead to immediate increases in bank capital. In the bank capital channel, expansionary monetary policy boosts bank capital, lending, and hence aggregate demand by enabling bank-dependent borrowers to spend more. Drechsler, Savov, and Schnabl (2017) 28

34 Summary Stats All banks Top 5% Low beta High beta Mean St.Dev. Mean Mean Mean Interest expense beta Asset repricing maturity Liabilities repricing maturity Core deposits/assets Observations 18, ,276 9,276 - Expense beta does not correlate with liability repricing maturity - But correlates strongly with asset repricing maturity Drechsler, Savov, and Schnabl (2017) 29

35 Post-ZLB Evidence 1. Wall Street Journal, March 19, With Fed poised to raise interest rates a sixth time, savers so far have seen few rewards - In the last tightening cycle, the average yield on a one-year CD rose 1.15 percentage points during the Feds first five rate moves... In this cycle, CD rates have risen just 0.27 points. 2. Wall Street Journal, March 22, 2018 But the biggest chunk of deposits is held in the banks retail units... These deposits, which are considered sticky, or less likely to leave, have become even more important... Drechsler, Savov, and Schnabl (2017) 30

36 Distribution of Estimated Expense Betas β Exp i Density Interest expense beta 1. Avg. β Exp is 0.35 (for large banks: 0.44) with significant variation 2. Avg. banking sector size: $6.763 trillion, net income: $59.5 billion 1% increase in FF rate raises revenues from bank liabilities by ( ) $6, 763 = $38 billion per year 3. Banking sector size in 2015: $14.8 trillion Drechsler, Savov, and Schnabl (2017) 31

37 Expense Betas and Branch Operating Costs Interest expense beta β Exp and deposits per branch Coef. = 0.038, R-sq. = Log deposits per branch Savings deposits β Exp and deposits per branch Savings deposits beta Coef. = 0.077, R-sq. = Log deposits per branch 1. Fewer deposits per branch higher operating cost per deposit dollar, larger investment in acquiring retail deposits As in model, banks that pay higher operating costs have lower β Exp, especially true for savings deposits (largest type of retail deposits) Drechsler, Savov, and Schnabl (2017) 32