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Interest rate risk and bank-specific characteristics

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1
Interest rate risk and bank-
specific characteristics
1.1 Introduction
Interest rate risk (IRR) represents one of the key forms of risk that banks face as
financial intermediaries. It can be defined as the risk that a bank’s income and/or
market value will be adversely affected by interest rate movements. This risk stems
from the peculiar nature of the banking business and can be basically attributed to two
principal reasons. First, banks hold primarily in their balance sheets financial assets
and liabilities fixed in nominal terms, hence especially sensitive to interest rate
changes. Second, banks traditionally perform a maturity transformation function using
short-term deposits to finance long-term loans. The resulting mismatch between the
maturity or time to repricing of their assets and liabilities exposes banks to repricing
risk, which is often seen as the major source of the interest rate exposure of the
banking system. Apart from repricing risk, banking firms are also subject to other
types of IRR such as basis risk, yield curve risk or optionality risk. As a result, the
banking sector is typically viewed as one of the industries with greater interest rate
sensitivity.
IRR management has gained prominence in the banking sector in recent years due
to several reasons. First, interest rates and financial market conditions have become
considerably more volatile. Second, net interest income, which directly depends on
interest rates, still remains as the most important source of bank revenue despite the
rising weight of fee-based income. Third, the concern about this topic has also
increased with the emphasis on the supervision and control of bank risks, IRR
included, under the new Basel Capital Accord (Basel II).
The exposure of banking institutions to changes in interest rates has been the
subject of an extensive body of research since the late 1970s. The most common
approach has consisted of estimating the sensitivity of banks’ stock returns to
movements in interest rates (e.g., Lynge and Zumwalt (1980); Elyasiani and Mansur
(1998); and Bartram (2002)). In contrast, there exists a substantially lower amount of
2
Capitolo 1
empirical evidence regarding the factors that explain the variation in interest rate
exposure across banks and over time (e.g., Flannery and James (1984); Hirtle (1997);
Fraser, Madura and Weigand (2002); Au Yong, Faff and Chalmers (2009)).
Studies that empirically investigate the determinants of bank IRR have
traditionally used asset-liability maturity or duration gap as the key factor explaining
banks’ interest rate exposure. However, this procedure presents serious drawbacks
given the well-known limitations of static gap indicators together with the difficulties
to obtain precise year-by-year gap measures for many banks. For this reason, an
interesting alternative, which however has received sparse attention in the literature, is
to examine the association between each bank’s interest rate exposure and a set of
potentially relevant financial variables.
This paper seeks to fill this gap in the Spanish case by empirically identifying the
main determinants of interest rate exposure of Spanish commercial banks. With this
aim, a series of bank-specific characteristics derived from information publicly
available in balance sheets and income statements have been used. To the best of the
authors’ knowledge this is the first study that specifically tackles this issue for the
Spanish banking industry.
The rest of the paper is organized as follows. Section 1.2 provides a brief review
of related literature. Section 1.3 describes the data and methodology employed. The
empirical results are presented in Section 1.4. Finally, Section 1.5 concludes.
1.2 Literature review
The incidence of IRR on bank stocks has been the focus of a considerable amount of
literature over the last three decades. The vast majority of the empirical studies have
adopted a capital market approach in the framework of the two-factor regression
model proposed by Stone (1974). This formulation is in essence an extended version
of the standard market model, where an interest rate change factor is added as an
additional explanatory variable to the market portfolio return in order to better explain
the variability of bank stock returns.
The bulk of this research, mostly based on US banks, has documented a
significant and negative effect of interest rate fluctuations on bank stock returns (e.g.,
Lynge and Zumwalt (1980); Elyasiani and Mansur (1998); and Saporoschenko
(2002)). This has been mainly attributed to the typical maturity mismatch between
banks’ assets and liabilities. In particular, banks have been generally exposed to a
positive duration gap on the balance sheet, i.e. the average duration of their assets
exceeds the average duration of their liabilities. In comparison, the attention paid to
the identification of the determinants of banks’ interest rate exposure has been much
less, although it is possible to distinguish two alternative groups of contributions.
The first approach investigates the relationship between the interest rate sensitivity
of bank stock returns and the maturity composition of banks’ assets and liabilities.
The one-year maturity gap (the difference between the assets and liabilities that roll
over or reprice within one year) is the variable most commonly used to measure the
Interest rate risk and bank-specific characteristics
3
maturity composition of the balance sheet. The seminal work of Flannery and James
(1984) provided evidence that maturity mismatch between assets and liabilities may
be used to explain cross-sectional variation in bank interest rate sensitivity (maturity
mismatch hypothesis). This finding has been supported later on by, among others,
Kwan (1991), and Akella and Greenbaun (1992). Subsequently, several empirical
papers extended the analysis of Flannery and James (1984) by incorporating the effect
of derivatives usage on banks’ IRR. The primary aim of this strand of research is to
examine the association between banks’ derivative activities and interest rate
exposure controlling for the influence of maturity composition (e.g., Hirtle (1997) and
Schrand (1997)).
The second approach focuses on the role played by a set of bank-specific
characteristics that are readily observable from basic financial statement information,
including both traditional on- and off-balance sheet activities. Thus, this methodology
overcomes the usual difficulties to obtain reliable and noise-free maturity gap
measures which prevent to accurately test the maturity mismatch hypothesis.
Following this approach, several empirical studies have documented a significant
effect of some bank characteristics such as bank size, equity capital ratio, or loan to
total assets ratio on bank IRR (e.g., Fraser, Madura and Weigand (2002);
Saporoschenko, (2002) and Au Yong, Faff and Chalmers (2009)). Additionally, this
line of research has been used in various papers that explore the determinants of IRR
of nonfinancial firms (e.g., Bartram (2002); Soto, Ferrer and Gonzalez (2005)).
With regard to the Spanish case, the available evidence concerning the sources of
bank interest rate exposure is very sparse. Jareño (2006 and 2008) examines the
differential effect of real interest rate changes and expected inflation rate changes on
the stock returns of Spanish companies, including both financial and nonfinancial
firms, working at the sector level. With that aim, different extensions of the two-
factor model of Stone (1974) and several potential explanatory factors of the real
interest and inflation rate sensitivity of Spanish firms have been used. However, it can
be noted that bank-specific characteristics are not taken into account to explore the
determinants of bank’s IRR in those papers.
1.3 Data and methodology
The sample consists of all commercial banks listed at the Spanish Stock Exchange
during the period January 1993-December 2006 with stock price data available for at
least three full years (23 banking firms in total). Daily bank stock returns have been
gathered from the Bolsa de Madrid Spanish database. The proxy for the market
portfolio used is the Indice General de la Bolsa de Madrid, the widest Spanish stock
market index. Table 1.1 shows the list of banks considered, the number of daily
observations for each bank, and the main descriptive statistics for the daily bank
returns.
With respect to the interest rate data, the daily average three-month rate of the
Spanish interbank market has been used. This choice obeys to the fact that during last
4
Capitolo 1
years the money market has become a key reference for Spanish banks due to two
major reasons. First, the great increase of adjustable-rate banking operations using
interbank rates as reference rates. Second, the interbank market has been largely used
by banks to finance the huge expansion of mortgage credit during the Spanish
housing boom. The interest rate data have been obtained from the Bank of Spain
historical database.
Finally, regarding the determinants of IRR, the end-year information from balance
sheets and income statements used to construct the bank-specific characteristics has
been drawn from Bankscope database of Bureau Van Dijk’s company, which is
currently the most comprehensive data set for European banks.
The methodology employed in this paper to investigate the determinants of banks’
interest rate exposure follows closely the second approach described in Section 1.2.
Thus, analogously to Fraser, Madura and Weigand (2002), Saporoschenko (2002) and
Au Yong, Faff and Chalmers (2009) a two-stage procedure has been applied. In the
first stage, the sensitivity of individual bank stock returns to movements in interest
rates has been estimated in the framework of the traditional two-factor model of Stone
(1974) by using a GARCH(1,1) process. The choice of this volatility model is
supported by many empirical studies which show that the GARCH(1,1) specification
is appropriate for modelling the variance generating process of financial time series.
The specific model can be expressed as:
ittimtiiit
IDRR
εβω
+++=
(1.1)
itititit
hh
εαεαα
+++=
12
2
110
(1.2)
)hN(0,~ |ε
it1-tit
(1.3)
where R
it
denotes the return on ith bank stock in period t, R
mt
the return on the market
portfolio in period t, I
t
the change in the interest rate in period t, ε
it
the error term
with zero mean and conditional variance h
it
, which is dependent on the information
set
t-1
. Finally, ω
i
, β
i
, D
i
, α
0
, α
1
,
and
α
2
are the parameters to be estimated. To
preserve the non negativity requirement for the conditional variance,
α
0
, α
1
, α
2
0
,
whereas
α
1
+ α
2
< 1 for stability to hold.
The coefficient on the market portfolio return,
β
i
,
describes the sensitivity of the
return on ith bank stock to general market fluctuations and, therefore, it can be
viewed as a measure of market risk (market beta). In turn, the coefficient on the
interest rate term,
D
i
, reflects the sensitivity of the return on ith bank stock to
movements in interest rates after controlling for changes in the return on the market
portfolio. Hence, it can be interpreted as a measure of ith bank interest rate exposure.
As Hirtle (1997), points out, this coefficient can be seen as an estimate of the
empirical duration of ith bank equity. Duration is a widely used measure of interest
rate sensitivity of fixed-income securities that can be extended to common stocks. In
Interest rate risk and bank-specific characteristics
5
particular, the empirical duration of a stock is an indicator of its IRR based upon the
historical relationship between equity returns and interest rate changes.
As specified in equation (1.1) above, the empirical duration is only a partial
measure of IRR, since changes in interest rates also affect the return on the market
and, through that channel, bank stock returns. In order to get a total measure of
banks’ interest rate exposure, and following Hirtle (1997) and Fraser, Madura and
Weigand (2002), among others, the market return variable has been orthogonalized.
Specifically, it has been carried out an auxiliary OLS regression of the market return
series on a constant and the interest rate fluctuation series and the residuals from that
regression have been used to replace the original market return in equation (1.1). The
empirical duration thus obtained measures both the direct effect of interest rate
movements on equity values and the indirect influences working through changes in
the return on the market.
Consistently with previous empirical research (e.g., Fraser, Madura and Weigand
(2002); Saporoschenko (2002); and Au Yong, Faff and Chalmers (2009)), the second
stage of the analysis has consisted of a cross-sectional regression of the empirical
durations for each individual bank generated in the first stage on a number of bank-
specific characteristics. Based on economic priors and early empirical literature, a set
of financial variables that are believed to help explain the bank IRR have been
considered. Thus, the cross-sectional regression model is as follows:
iiiiiii
OBSALOANSNONINTSIZECAPD
νγγγγγγ
++++++=
543210
ˆ
(1.4)
where
i
D
ˆ
is the absolute value of bank ith empirical duration estimated in stage one,
CAP denotes the ratio of equity capital to total assets, SIZE the natural logarithm of
bank total assets, NONINT the ratio of non-interest income to total revenue, LOANS
the ratio of loans to total assets, OBSA the ratio of off-balance sheet items to total
assets, and finally ν
i
is an Gaussian error term.
Since the empirical durations estimated in stage one overwhelmingly show a
negative sign, in order to facilitate the economic interpretation of the results the
absolute value of durations has been used as the dependent variable. The sample
average of each bank characteristic has been employed for each individual bank. The
estimation of (1.4) has been conducted using weighted least squares, where the
weights are the inverse of the standard errors of the durations obtained in the first
stage regression. This procedure gives more weight to the durations that have been
estimated more precisely.
The equity capital ratio (CAP) is a measure of capital strength frequently used as a
potential determinant of bank’s IRR (e.g., Fraser, Madura and Weigand (2002);
Saporoschenko (2002); and Au Yong, Faff and Chalmers (2009)). In general, banks
with higher capital ratios have lower needs of external funding and so less degree of
financial leverage. Thus, interest rate fluctuations will have a smaller impact on bank
revenues and, consequently, on bank stock returns. Therefore, a high level of capital
can be viewed as a great cushion against abnormal increases in interest rates and other
adverse shocks. Hence, a negative association between capital and IRR is predicted.
6
Capitolo 1
The bank size variable (SIZE) also constitutes a variable frequently considered as
a possible source of bank IRR (e.g., Fraser, Madura and Weigand (2002);
Saporoschenko (2002); and Au Yong, Faff and Chalmers (2009)). Bank size is
included to control for discrepancies in interest rate exposure between small and large
banks that might be caused by several factors such differences in the type of
businesses and customers, or the risk attitude among banks, or even moral hazard
behaviour in larger banks. Consequently, the sign of the relationship between size and
bank IRR is ambiguous and it becomes an empirical question.
The noninterest income ratio (NONINT) is an indicator of the degree of bank
diversification. It reflects the relative weight of the noninterest income arising from
both traditional service charges (fees and commissions) and non-traditional banking
activities (investment banking, market trading, insurance, or asset management).
Banks with a larger income share of noninterest activities are less dependent on
classical intermediation activities (deposits and loans) and, consequently, should be
less affected by interest rate changes. Thus, a negative association between this ratio
and the IRR is hypothesized.
The loan to total assets ratio (LOANS) measures the relative importance of loans in
the bank’s balance sheet and it can be interpreted as an indicator of IRR as well. On
average, the maturity of bank loans is greater than the corresponding one of the rest of
its assets and liabilities. Accordingly, an increase in the proportion of loans entails an
extension of the typical maturity mismatch between assets and liabilities. Hence, it
seems natural to expect a positive association between this ratio and the bank’s IRR.
Since banks are major users of derivative instruments both as end-users and as
dealers, and the fact that derivatives provide a relatively inexpensive means to alter
banks’ interest rate exposure, the impact of derivatives usage on bank’s IRR has
become an essential issue in recent literature concerning bank risk exposure (e.g.,
Hirtle (1997); Au Yong, Faff and Chalmers (2009)). Because derivative activities
carried out by banks are classified as off-balance sheet operations and there is not
more specific information about banks’ derivative positions in Bankscope database,
the ratio of off-balance sheet exposure to total assets (OBSA) has been used as a proxy
of derivative use. Concerning to the sign of the relationship between this indicator
and the degree of bank IRR, two opposite situations can be distinguished depending
on whether banks employ derivatives primarily to hedge their interest rate exposure
(negative coefficient expected) or for speculative purposes (positive coefficient
expected). As it is not clear a priori which of these two alternatives is more likely, the
net contribution of derivatives to banks’ IRR must be empirically established.
Finally, it must be pointed out that although the maturity gap ratio is an important
theoretical measure of bank’s IRR, unfortunately this indicator could not be used due
to the lack of any maturity buckets information in the Bankscope database.
1.4 Empirical results
Table 1.1 shows the descriptive statistics of bank stocks and market returns. The
summary statistics indicate that these series are skewed and leptokurtic relative to the
Interest rate risk and bank-specific characteristics
7
normal distribution. Their non-linear time dependence seems to suggest the suitability
of the GARCH approach to analyze the IRR of bank stocks.
Table 1.1 Descriptive statistics of bank stock and market returns
Banks Obs. Mean Variance Minimum Maximum Skewness Kurtosis JB
BANCO DE
ALICANTE
1344 -0.0001 0.0001 -0.119 0.148 4.363
***
105.508
***
627,659
***
B
ANCO
A
NDALUCÍA
3517 0.0006 0.0001 -0.149 0.150 1.054
***
30.719
***
138,944
***
A
RGENTARIA
1686 0.0009 0.0003 -0.109 0.120 0.256
***
4.905
***
1,708
***
B
ANCO
A
TLÁNTICO
2883 0.0006 0.0001 -0.127 0.149 2.209
***
62.385
***
469,859
***
BBVA 3517 0.0009 0.0003 -0.135 0.141 0.269
***
6.238
***
5,745
***
B
ANCO
C
ENTRAL
H
ISPANO
1578 0.0011 0.0003 -0.104 0.149 0.588
***
6.689
***
3,033
***
B
ANKINTER
3517 0.0008 0.0003 -0.116 0.149 0.493
***
6.992
***
7307
***
B
ANESTO
3517 0.0005 0.0003 -0.196 0.236 1.223
***
30.883
***
140,645
***
B
ANCO
V
ALENCIA
3517 0.0010 0.0001 -0.068 0.109 0.879
***
5.655
***
5,139
***
B
ANCO DE
C
ASTILLA
3517 0.0006 0.0001 -0.132 0.150 1.449
***
24.697
***
90,615
***
B
ANCO
C
RÉDITO
B
ALEAR
3517 0.0007 0.0002 -0.148 0.149 0.909
***
14.106
***
29,646
***
B
ANCO
E
XTERIOR
1080 -0.0001 0.00006 -0.053 0.141 4.868
***
91.888
***
384,221
***
B
ANCO
G
ALICIA
3517 0.0007 0.0002 -0.144 0.149 1.377
***
20.533
***
62,894
***
B
ANCO
G
UIPUZCOANO
3517 0.0006 0.0001 -0.064 0.146 1.172
***
10.405
***
16,673
***
B
ANCO
H
ERRERO
2003 0.0006 0.0004 -0.149 0.150 0.909
***
23.589
***
46,718
***
B
ANCO
P
ASTOR
3517 0.0008 0.0001 -0.084 0.097 0.572
***
7.012
***
7,398
***
B
ANCO
P
OPULAR
E
SPAÑOL
3517 0.0007 0.0002 -0.114 0.095 0.274
***
3.673
***
2,022
***
B
ANCO
S
ABADELL
1436 0.0005 0.0001 -0.096 0.071 -0.445 7.929
***
3,809
***
B
ANCO
S
ANTANDER
3517 0.0007 0.0003 -0.148 0.149 -0.013
***
6.395
***
5,994
***
B
ANCO
S
IMEÓN
1407 0.001 0.0012 -0.277 0.150 0.270
***
16.840
***
16,642
***
B
ANCO DE
V
ASCONIA
3517 0.0007 0.0003 -0.149 0.150 1.693*** 22.801
***
77,869
***
B
ANCO DE
V
ITORIA
1306 0.0005 0.0003 -0.119 0.149 3.445
***
29.228
***
49,072
***
B
ANCO
Z
ARAGOZANO
2732 0.0008 0.0002 -0.078 0.148 1.468
***
11.346
***
15,635
***
M
ARKET
P
ORTFOLIO
3517 0.0007 0.0001 -0.054 0.0600 -0.154
***
1.980
***
588
***
8
Capitolo 1
JB denotes the Jarque-Bera statistic for normality of returns. This statistic is distributed as chi-squared with two
degrees of freedom.
***
,
**
and
*
represent significance at the 1%, 5% and 10%, respectively.
Table 1.2 contains the results of the estimation of the interest rate sensitivity of bank
stock returns. As expected, the estimated empirical durations are predominantly
negative, suggesting that an increase in interest rates generally leads to a decrease in
bank stock values and a negative stock return. In fact, all the empirical durations
statistically significant at usual levels take negative values.
Table 1.2 Sensitivity of Spanish bank stock returns to interest rate changes
GARCH model: 1993-2006 Test LM
β D α
1
α
2
Χ
25
Χ
210
BANCO DE ALICANTE
0.0464 0.0498 0.75 0.19 0.1512 2.1660
B
ANCO
A
NDALUCÍA
0.1916
***
-0.6929
*
0.17
***
0.72
***
0.0938 0.3802
A
RGENTARIA
0.9509
***
-0.4662 0.11
***
0.85
***
6.3070 10.7314
B
ANCO
A
TLÁNTICO
0.1323
***
-0.0001 0.23
***
0.64
***
0.2123 0.2585
B
ANCO
B
ILBAO
V
IZCAYA
A
RGENTARIA
1.0158
***
-0.6751
**
0.09
***
0.89
***
10.1272
*
15.4962
B
ANCO
C
ENTRAL
H
ISPANO
0.9197
***
-0.7455
***
0.13
***
0.81
***
4.2866 14.6122
B
ANKINTER
0.8972
***
-1.4165
*
0.09
***
0.89
***
13.4917
**
14.8666
B
ANESTO
0.2682
***
-1.7918
***
0.12
**
0.88
***
1.0602 1.8719
B
ANCO
V
ALENCIA
0.1696
***
-0.1631 0.15 0.79
***
5.6600 14.9271
B
ANCO DE
C
ASTILLA
0.0432
***
-0.1968 0.06
***
0.93
***
55.0162
***
58.7345
***
B
ANCO
C
RÉDITO
B
ALEAR
0.0782
**
-0.5123 0.19
***
0.74
***
4.4397 5.7785
B
ANCO
E
XTERIOR
0.1821
***
-0.2177
**
0.27
**
0.08 0.3422 0.4154
B
ANCO
G
ALICIA
0.0823
***
-0.2945 0.10
***
0.90
***
112.4105
***
113.5056
***
B
ANCO
G
UIPUZCOANO
0.1623
***
0.1727 0.18
***
0.79
***
2.6929 6.7397
B
ANCO
H
ERRERO
0.1052
*
-0.5398 0.16
***
0.69
***
1.7421 5.3579
B
ANCO
P
ASTOR
0.1832
***
-0.1468 0.35
**
0.55
***
5.5760 10.5494
B
ANCO
P
OPULAR
E
SPAÑOL
0.7501
***
-0.1523 0.06 0.92
***
21.8752
***
29.9080
***
B
ANCO
S
ABADELL
0.3886
***
-0.5543 0.22
***
0.70
***
12.6610
**
18.1301
*
B
ANCO
S
ANTANDER
1.1453
***
-0.6993
*
0.08
***
0.90
***
4.7882 9.5331
B
ANCO
S
IMEÓN
0.2122
***
-0.5362 0.03
***
0.96
***
0.4292 1.2952
B
ANCO DE
V
ASCONIA
0.0990
**
-0.2364
***
0.11
**
0.87
***
15.1133
***
17.5647
*
B
ANCO DE
V
ITORIA
0.1859 -0.3271
***
0.22
**
0.79
***
2.8619 6.7564
B
ANCO
Z
ARAGOZANO
0.2277
***
-0.4458 0.37
***
0.42
***
0.6773 2.5614
***
,
**
and
*
represent significance at the 1%, 5% and 10%, respectively.
Prior to estimate the cross-sectional regression model (1.4), an orthogonalization
procedure has been applied in order to avoid multicolinearity problems among some
bank characteristics highly correlated. Thus, the orthogonalized financial variables
have been used in the second stage of the analysis.
Table 1.3 shows the results of the cross-sectional regression. Three out of five
bank characteristics (bank size, loan to total assets ratio, and the ratio of off-balance
sheet exposure to total assets) are statistically significant at usual levels.
Interest rate risk and bank-specific characteristics
9
Table 1.3
Determinants of interest rate exposure
Cross-sectional regression: Weighted Least Squares 1993-2006
γ
0
γ
1
γ
2
γ
3
γ
4
γ
5
0.7364 -0.2979 0.2586 0.0962 0.0472 0.5486
(0.55) (-1.05) (3.19)
**
(1.69) (3.45)
**
(9.67)
***
R
2
0.9158
F Statistic 6.5320
*
***
,
**
and
*
represent significance at the 1%, 5% and 10%, respectively.
The ratio of off-balance sheet exposure to total assets appears as the most significant
determinant of bank interest rate exposure in terms of both amount and significance.
This indicator is positively related to the level of IRR, suggesting that Spanish banks
use financial derivatives for speculation rather than for hedging purposes. This result
is in line with Hirtle (1997) and Au Yong, Faff and Chalmers (2007).
Bank size is highly significant and positively signed, indicating that there seems to
be a direct relationship between the size of banking firms and their level of interest
rate sensitivity. This finding is consistent with the results of Elyasiani and Mansur
(1998), Faff, Hodgson and Kremmer (2005), and Ballester, Ferrer and González
(2008) by using a different methodology, confirming that large banks bear higher IRR
than small banks. This pattern of behaviour could be a consequence of differences in
terms of type of business and customers, risk attitude, and aggressiveness in the
pricing policies between large and small banks in the Spanish case. Furthermore, the
inferior degree of diversification and the more difficult access to capital markets for
smaller banks, together with their stock performance highly driven by idiosyncratic
factors –e.g., rumours of possible mergers and acquisitions–, can also help to explain
their lower exposure to IRR.
The loans to total assets ratio is also significantly and positively linked with banks’
interest rate exposure, suggesting that banks that hold a greater portion of assets in the
form of loans have higher degree of IRR. One possible explanation for this result is
that the larger relative weight of loans into the bank balance sheet causes an increase
of traditional maturity mismatch between bank assets and liabilities, with the
subsequent positive impact on bank’s IRR.
Finally, neither the capital nor the noninterest income ratios seem to be significant
determinants of bank’s IRR. On the one hand, it can be argued that, since Spanish
banks are in general well capitalized and hold a large cushion of capital as a
protection against possible losses from negative economic shocks, the capital ratio is
not perceived by market forces as a key source of IRR. On the other hand, even
though the increasing importance of the noninterest income along last years, it does
not seem to have yet the necessary weight to be considered as a significant
determinant of the IRR borne by the Spanish banking industry.
10
Capitolo 1
1.5 Concluding remarks
This paper empirically investigates the main determinants of interest rate exposure of
Spanish commercial banks over the period 1993-2006. With that aim, a set of bank-
specific characteristics indicative of both traditional on- and off-balance sheet
activities have been considered.
The empirical analysis reveals several interesting findings. First, Spanish banks
show an important level of interest rate exposure during the period of study. In
particular, a significant negative relationship between bank stock returns and changes
in interest rates is predominantly found consistent with the classical view of banks
short-term borrowing and long-term lending. Second, it is documented that the
interest rate exposure is systematically related to some bank characteristics.
Specifically, a highly significant and positive association is found between off-
balance sheet activities and interest rate exposure, suggesting that the usage of
financial derivatives by Spanish banks is primarily driven by speculative purposes.
Moreover, bank size and the ratio of loans on total assets are also significantly and
positively linked with interest rate risk. This evidence seems to indicate that larger
banks adopt riskier strategies due to several operating advantages such as
diversification or better access to capital markets inherent to their size, or even to their
too big to fail status. Additionally, banks with a great portion of assets in the form of
loans present a higher interest rate exposure because of the effect of widening the
maturity mismatch between assets and liabilities induced by the larger relative weight
of loans. However, the capital and the noninterest income ratios do not exert a
significant influence on the degree of bank interest rate exposure.
The knowledge of the underlying factors explaining bank’s interest rate exposure
is particularly important for different economic agents. Good examples are bank
managers, who want to adequately manage their interest rate risk; investors,
concerned about the pricing of bank equities for hedging and asset allocation
purposes; and bank regulators, primarily interested about the assessment of systemic
interest rate risk and the stability and soundness of the banking system.
L. Ballester, R. Ferrer and C.Gonzalez are grateful for the financial support from
the Spanish Ministry of Education and Science and FEDER Funds, grant number
SEJ2005-08931-C02-02/ECON.
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