Microsoft Word Altavilla Boucinha Peydro ep word version docx

Financial variables
Short-term rate
0.60
1.15
-0.03
0.16
0.60
3,566
Country-specific slope
0.74
13.63
1.17
1.72
2.19
3,566
VIX
23.41
8.39
18.89
20.88
25.60
3,494
Expected default frequency
1.09
1.28
0.51
0.77
1.20
3,494
Macroeconomic variables
Real GDP growth
0.04
2.37
-1.31
0.62
1.55
3,294
Inflation
1.27
0.68
0.83
1.20
1.70
3,566
Expected real GDP growth
0.94
1.05
0.43
1.21
1.69
3,494
Expected inflation
1.55
0.51
1.21
1.56
1.87
3,494
Bank variables
Return on Assets (in basis points)
25
55
7
27
51
3,566
Net interest income (in basis points)
39
17
25
36
53
2,102
Non-interest income (in basis points)
23
13
13
23
32
2,087
Provisions (in basis points)
15
14
5
10
21
2,092
NPL ratio
5.75
4.50
2.54
4.48
7.46
2,297
Tier1 capital ratio
10.81
3.34
8.42
10.57
12.36
2,806
Cost-to-Income Ratio
61.51
15.37
50.78
61.04
71.41
3,143
Liquid asset ratio
30.49
16.26
18.24
26.84
38.03
2,402
Maturity gap
25.05
25.47
7.51
13.28
33.08
2,958

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22 
Table 6: Monetary policy and maturity transformation 
Note: The dependent variable is the return on assets (ROA). Data are at quarterly frequency covering an 
unbalanced sample of 234 banks for the period Q1 2007 – Q4 2016. Standard errors clustered at bank 
level in parentheses: * p<.1, ** p<.05, *** p<.01. 
(1)
(2)
(3)
(4)
(5)
(6)
(7)
ROA
i,j,t-1
0.165***
0.141***
0.112***
0.101***
0.0679*
-0.00141
0.0894**
(0.0327)
(0.0334)
(0.0319)
(0.0328)
(0.0400)
(0.0428)
(0.0403)
Short-term rate
t
0.0553***
0.0323**
0.0166
0.00634
0.0352
-0.0195
(0.0145)
(0.0157)
(0.0167)
(0.0169)
(0.0349)
(0.0254)
Slope
j,t
0.00448*** 0.00406***
0.00290**
0.000853
0.00123
0.000926
(0.00123)
(0.00127)
(0.00126)
(0.00153)
(0.00166)
(0.00148)
VIX
t
-0.00583***
0.000583
0.00164
0.00187
-0.00654***
(0.00161)
(0.00176)
(0.00176)
(0.00253)
(0.00236)
Real GDP growth
j,t
0.0217***
-0.00330
-0.00769
-0.00781
0.0119*
(0.00611)
(0.00615)
(0.00556)
(0.00645)
(0.00624)
Inflation
j,t
-0.0163
0.0425*
0.0365*
0.0500*
0.0467*
(0.0205)
(0.0219)
(0.0207)
(0.0258)
(0.0263)
Expected real GDP growth
j,t
0.158***
0.134***
0.115***
0.123***
(0.0191)
(0.0218)
(0.0302)
(0.0281)
Expected inflation
j,t
0.0617**
0.0735**
0.0402
-0.0370
(0.0306)
(0.0312)
(0.0553)
(0.0531)
Expected default frequency
j,t
-0.0671***
-0.0814**
-0.0691*
(0.0249)
(0.0397)
(0.0399)
NPL ratio
i,j,t-1
-0.0471***
-0.0264**
(0.0101)
(0.0103)
Regulatory capital ratio
i,j,t-1
-0.0135
0.00568
(0.00872)
(0.0120)
Cost-to-income ratio
i,j,t-1
-0.00332
-0.000971*
(0.00243)
(0.000451)
Liquid asset ratio
i,j,t-1
-0.00609
-0.00163
(0.00543)
(0.00507)
Maturity gap
i,j,t-1
0.00372**
0.00418*
(0.00143)
(0.00216)
(Short-term rate
t
) x (Maturity gap
i,j,t-1
)
0.000107
(0.00159)
(Slope
j,t
) x (Maturity gap
i,j,t-1
)
0.000696**
(0.000273)
Bank FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Country*time FE
No
No
No
No
No
No
Yes
Number of observations
2,271
2,271
2,271
2,271
845
845
845
R
2
0.418
0.429
0.468
0.472
0.432
0.467
0.646

---

23 
Estimation results are reported in Table 6. Similarly to Table 2, results show that, by influencing 
either the short-term rate or the slope of the term structure, monetary policy is found to have a 
significant impact on bank profitability if no additional controls are included in the specification 
(column 1).
Also in line with results shown in Subsection 3.1, current and future macroeconomic 
developments remain important drivers of bank profitability, and the coefficients for the short-term 
rate and the slope lose statistical significance when adding macroeconomic controls (column 2 to 
5). The impact on profitability of the cost-to-income ratio and the NPL ratio has similar sign and 
magnitude to the coefficients obtained using the longer sample: low cost efficiency and high non-
performing loans tend to compress bank profitability (column 6). 
The positive coefficient on the maturity gap reflects the idea that, all other things being equal, 
increased maturity transformation translates into higher profitability (see English et al., 2014). An 
average bank will see its ROA rise by about 10 basis points following an increase in its maturity gap 
by one standard deviation (i.e. 25 months). Moreover, we investigate whether the impact of changes 
in the level and the slope of the term structure depend on the maturity gap. The results in column 5 
show that the profitability of banks that engage more heavily in maturity transformation has a more 
positive reaction to a steepening of the yield curve in relative terms. A bank with a maturity gap that 
is one standard deviation above the sample average sees its profitability increase by two basis points 
in response to a 100 basis point steepening of the yield curve. 
In principle, the impact of monetary policy action on bank profitability through maturity 
transformation would be mitigated if banks used derivatives to hedge exposures to interest rate risk. 
Recent evidence by Begenau et al. (2015), however, suggests that the extent to which US banks use 
interest rate derivatives to hedge exposures to interest rate is limited. For the euro area, Hoffmann 
et al. (2017) find that banks use derivatives to reduce their banking book exposures to interest rate 
risk by 25%, on average. This evidence on US and European banks suggests that these financial 
intermediaries do not fully hedged for interest rate risk (which is also confirmed in the last section 
of the paper, as if banks were fully hedged, then there should not be an impact from monetary 
policy surprises on bank stocks and CDS).