International Review of Financial Analysis 48 (2016) 376–387.docx

International Review of Financial Analysis 48 (2016) 376–387
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International Review of Financial Analysis
Banking industry performance in the wake of the global
financial crisis☆
Diptes C. Bhimjee a, Sofia B. Ramos b,⁎, José G. Dias c
a Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa 1649-
026, Portugal
b ESSEC Business School, Cergy-Pontoise 95000, France
c Instituto Universitário de Lisboa (ISCTE-IUL), BRU-IUL,
Lisboa 1649-026, Portugal
☆ The authors would like to thank the discussants and p
Conference on International Finance (Prato, Italy), the X
Spain), the Bank of Portugal's Conference on “Econome
Finance”, and two anonymous referees for their valuab
more specifically, we would like to thank INFINITI's Ed Kan
able insights on the future development of the HRSM's the
acknowledges support from BRU-UNIDE, Instituto Unive
Portugal.
⁎ Corresponding author.
E-mail address: [email protected] (S.B. Ramos).
1 A fundamental distinction between the ‘subprime’ cri
the ensuing global financial crisis (as a truly global financia
tional financial contagion processes) is thus respected thr
http://dx.doi.org/10.1016/j.irfa.2016.01.005
1057-5219/© 2016 Elsevier Inc. All rights reserved.
a b s t r a c t

a r t i c l e i n f o
Available online 27 January 2016
JEL classification:
G01
G15
F30
Keywords:
Global financial crisis
International financial contagion
‘Subprime’ crisis
Banking institutions
Heterogeneous regime-switching model
(HRSM)
This paper analyzes the performance of the banking industry
both prior to and during the global financial crisis
(GFC). Through the application of a panel regime-switching
model designed to capture heterogeneity, our find-
ings suggest that global banking performance can be grouped
into two distinctive clusters, each with its own spe-
cific regime dynamics. Before the crisis, a cluster of banking
institutions pertaining to advanced economies stood
out for its buoyant stock market performance, whereas a second
cluster, mainly composed of banking indexes
that belong to emerging economies, exhibited a more subdued
performance. Further, this differentiation was ac-
companied by low regime synchronization between the clusters.
During the crisis, banking institutions behaved
similarly, regime synchronization increased, and the differences
in the regime dynamics vanished. Finally, the
GFC constituted a highly synchronized and systemic extreme
financial event, as evidenced by our findings
depicting the onset of severe underlying international financial
contagion processes.

© 2016 Elsevier Inc. All rights reserved.
1. Introduction
The global financial crisis of 2007–2009 (hereafter, the GFC)
consti-
tuted a resounding systemic failure that had a profound effect
on finan-
cial markets and, more specifically, on international banking
institutions
that operate in increasingly borderless markets.1 The banking
industry
has been indelibly marked by this unprecedented credit event of
global
magnitude, and its worldwide implications continue to be felt.
As a result of severe international financial contagion
processes, the
GFC ultimately affected the valuation of outstanding securitized
assets
worldwide and the performance of the corresponding banking
institu-
tions holding assets of uncertain (or even ‘toxic’) value. This
global un-
certainty regarding the soundness of bank balance sheets
affected the
valuation of the financial industry worldwide, and the negative
outlook
for future profitability led to severe falls in stock returns.
Nevertheless,
articipants of the 12th INFINITI
XII Finance Forum (Zaragoza,
tric Methods for Banking and
le comments and suggestions;
e (Boston College) for his valu-
oretical framework. Sofia Ramos

rsitário de Lisboa (ISCTE-IUL),
sis (as a localized US event) and
l event associated with interna-
oughout this paper.
and despite the pervasiveness of international financial
contagion
throughout the GFC, banks were heterogeneously affected due
to their
distinct balance sheet exposure to the global systemic shock.
According-
ly, the stock market valuation of international banking
industries was
very heterogeneous throughout the GFC, in view of the differing
impacts
of the systemic crisis on the banking systems of each country.
This paper studies the main characteristics of this pivotal
element
of heterogeneity in the global banking industry. It addresses the
hetero-
geneous stock market performance of the banking industry and,
in
particular, considers the impact of the GFC. The paper uses a
novel
methodology that was designed to capture both heterogeneity
and in-
ternational financial contagion processes. The sample is
composed of
42 country banking indexes and encompasses a mix of emerging
and
advanced markets.
The paper has two main goals. First, it aims to characterize the
de-
gree of heterogeneity in banking industry performance in both

the up-
swing and downswing of the business cycle associated with the
GFC.
Banking industry indexes are used to capture the aggregate
perfor-
mance of banks because the market valuation reflects the
investors' as-
sessment of the future profitability of the banking industry.2
Second, given the global impact of this systemic breakdown,
this
study's objective is to assess the main attributes of international
finan-
cial contagion processes during the GFC.
2 A natural implicit assumption is that markets are
“informationally efficient”; that is,
investors are able to incorporate the new information coming
from the crisis into asset
prices.
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377D.C. Bhimjee et al. / International Review of Financial
Analysis 48 (2016) 376–387
Although some studies have addressed financial contagion in
this
period, they have focused either on stock market returns (refer
to

e.g., Dimitriou, Kenourgios, & Simos, 2013; Kotkatvuori-
Örnberg,
Nikkinen, & Äijö, 2013) or volatility spillovers between
banking indus-
tries (Choudhry & Jayasekera, 2014). Dimitriou et al. (2013)
investigate
the contagion effects of the global financial crisis in a
multivariate frac-
tionally integrated asymmetric power ARCH (FIAPARCH)
dynamic con-
ditional correlation (DCC) framework, using the stock market
returns of
the BRICS countries and the United States. Kotkatvuori-
Örnberg et al.
(2013) use data from 50 equity markets to examine both the
condition-
al and unconditional correlations around two major events
during the
financial crisis of 2007–2009. The researchers found that the
Lehman
Brothers' collapse resulted in a significant increase in
correlations.
Choudhry and Jayasekera (2014) investigate the influence of the
global
crisis on the spillover between the banking indexes of Europe
and the
United States by using a multivariate GARCH–GJR framework.
Their re-
sults indicate an increase in both means and volatility spillover
between
the major economies and the stressed EU economies from the
pre-crisis
to the crisis period, but minimal evidence of a significant
spillover from
the smaller economies to the major economies.

This paper addresses the heterogeneous performance of banking
in-
dustries at a global level, both before and during the 2007–2009
crisis.
Our paper captures the dynamics associated with international
financial
contagion processes among financial institutions during the
GFC. Ac-
cordingly, we propose an extension of the regime-switching
model,
the heterogeneous regime-switching model (HRSM), to achieve
this
dual purpose. This model extension allows us to distinguish
between
the likelihood of switching between regimes among the
heterogeneous
sub-sets of country banking indexes under analysis. The
approach also
expands on the existing methodologies that do not currently
allow mar-
ket regimes to be incorporated in the analysis of crises. The
proposed
methodology further accounts for the problem of non-normality
in
financial returns that often occurs in emerging markets.3 The
flexible
modeling of observed returns using a mixture of normal
distributions
enables nearly any departure from normality to be captured in a
straightforward manner.4 In addition, the inclusion of market
regimes
in the modeling of financial time series is suitable due to
structural
breaks, e.g., regime switching due to pro-cyclicity and the
asymmetry
of volatility (see, e.g., Baele (2005); Billio and Pelizzon (2003),

and
Kearney and Potì (2008)), is obtained endogenously within our
model
applications.
Our main findings are summarized as follows. Our results
suggest
that the banking industries depicted in our sample can be
disentangled
into two clusters (based on the best clustering identified by the
mini-
mum Bayesian information criterion (BIC)). The first
encompasses
advanced economies, whereas the second is chiefly composed of
emerg-
ing market economies. The groups are mainly distinguished
because the
emerging market group was not buoyant before the GFC but was
subse-
quently affected by it. The evidence suggests that co-
movements in-
crease during the GFC because we find a large synchronization
in these
two groups for all regimes. There are nearly no detectable
differences
between the groups in the regime dynamics during the crisis
period.
Furthermore, our results indicate differentiated or
heterogeneous
response dynamics emanating from the performance of the
banking in-
dustries. Shehzad and De Hann (2013) also observe this
heterogeneity,
as they find that stock prices of banks in emerging countries
were less

affected by the systemic shock than were the corresponding
prices
of their counterparts in developed economies. Our findings are
similar,
although they have been endogenously determined by applying
the
HRSM in a global macro-financial setting.
Moreover, the pattern found is in accordance with the stylized
fact that crises are typically associated with temporary changes
in
3 See, for instance, Harvey (1995) or Susmel (2001).
4 See, for example, Dias and Wedel (2004) and McLachlan and
Peel (2000) on the use of
mixture models to address unobserved heterogeneity.
fundamentals. Beltratti and Stulz (2012) argue that the
expectations as-
sociated with bank stock returns were significantly different
before and
after the GFC. Before the crisis, stock markets favored banking
business
strategies that involved financial innovation-related products.
Subse-
quently, the onset of the GFC shifted market expectations in
favor of
more conservative banking business strategies that promoted
staple
products.
The current work helps clarify the process of contagion during
the
global financial crisis. Our paper offers a complementary view
because
it focuses on the heterogeneity across cycles, which has not
been ad-

dressed in previous work.
The findings have important asset management implications, as
de-
veloped by Ahmad, Bhanumurthy, and Sehgal (2015), because
changes
in correlations imply changes in portfolio weights. The results
suggest
that the origin of the crisis was located in developed countries,
but
quickly spread to emerging countries, which highlights the
systemic na-
ture of the crisis.
These findings are important on two aspects: first, they are
essential
when assessing the performance of banking industries
throughout crisis
episodes. Second, they may be useful in the subsequent design,
by cen-
tral banking institutions, of coordinated support policies for the
banking
industry, particularly in the aftermath of systemic episodes that
severely
constrain the banking industry. The results are also of interest
for regula-
tors and demand measures to prevent contagion in such an
important
area.
Section 2 presents a short description of regime-switching
models
and fully depicts our methodology, the heterogeneous regime-
switching model (HRSM). Section 3 presents preliminary
consider-
ations regarding the data set. Section 4 describes the empirical

findings
pertaining to the model applications encompassing the GFC.
Finally,
Section 5 summarizes our main findings.
2. Methodology
This section introduces the statistical framework, the
heterogeneous
regime-switching model (HRSM), which is based on an
extension of
panel regime-switching models.
Hamilton (1989) was the first to show how regime-switching
models (RSM) can be useful in macroeconomic data modeling
by
allowing non-linear instead of linear stationary processes. RSM
has be-
come very popular because it captures the ‘turning points’ in a
given
economic time series as discrete regime shifts in the behavior of
the
time series. This behavior is naturally connected to the
existence of dra-
matic breaks (or discontinuities) in the economic time series
and is
often associated with the occurrence of financial crises and
economic
cycles (Bhar & Hamori, 2004). Therefore, these models are
suited to an-
alyzing and characterizing both the ‘turning points’ and abrupt
changes
(discontinuities) that occur in economic and financial time
series that
are affected by the occurrence of extreme, but reversible,
financial

events such as the GFC.
Heterogeneous regime-switching models are an extension of the
Markov-switching model and were initially developed by Dias,
Vermunt, and Ramos (2008) and Ramos, Vermunt, and Dias
(2011).
This model can also be viewed as an extension of the hybrid
model in-
troduced by Dias and Ramos (2014), which estimates a panel
regime
switching model and then, on the posterior probabilities, applies
heuris-
tic cluster analysis to identify the hierarchical structure of the
stock mar-
ket. The HRSM enables the statistical estimation of regime-
switching
models based on the similarity of the dynamics associated with
each ho-
mogeneous group (or cluster), i.e., clusters and regimes are
estimated
simultaneously. A model with S groups is denominated HRSM-
S. To
achieve this estimation, essentially, two types of clustering are
assumed.
Each underlying time series is both assigned to a specific
cluster and
modeled as a regime-switching model within each cluster.
Let yit represent the return, at time t, of each country banking
index
contemplated in our sample, where i∈ {1, …,n} and t∈ {1,
…,T}, with
Table 1

Characterization of the sample banking industries (year 2010).
Countries Bank deposits
to GDP (%)
Bank branches
per 100,000 adults
Bank credit
to bank deposits (%)
Foreign banks among
total banks (%)
GDP per
capita
Argentina (AR) 19 13 67 33
Australia (AU) 94 31 127 42 36,175
Austria (OE) 97 11 125 11 40,099
Belgium (BG) 104 45 88 43 37,745
Brazil (BR) 49 44 105 38 5,618
Canada (CN) 24 39 36,467
Chile (CL) 37 17 168 43 8,610
China 51 235 21 2,870
Czech Republic (CZ) 62 22 67 14,640
Denmark (DK) 55 41 9 47,792
Finland (FN) 62 15 149 22 39,698
France (FR) 80 42 140 5 35,216
Germany (BD) 114 16 92 14 37,146
Greece (GR) 103 40 123 22 21,894
Hong Kong (HK) 300 24 59 73 31,329
Hungary (HN) 47 17 82 11,109
India (IN) 60 10 76 12 1,032
Ireland (IR) 108 29 197 85 46,424
Israel (IS) 82 19 105 23,224

Italy (IT) 84 66 133 10 30,788
Japan (JP) 208 34 50 1 36,296
Luxembourg (LX) 339 89 55 96 81,565
Malaysia (MY) 119 11 89 40 6,319
Mexico (MX) 25 14 70 39 8,085
Netherlands (NL) 132 23 153 45 43,675
Norway (NW) 11 2 64,590
Pakistan (PK) 29 8 69 42 748
Peru (PE) 28 47 80 67 3,575
Philippines (PH) 51 8 55 11 1,403
Poland (PO) 46 32 74 10,038
Portugal (PT) 116 66 151 37 19,240
Russian (RS) 36 35 107 20 6,386
Singapore (SG) 114 10 81 50 34,758
South Africa (SA) 59 10 119 24 5,911
Spain (ES) 158 97 133 8 26,191
Sweden (SD) 53 23 1 44,878
Switzerland (SW) 133 52 116 21 58,140
Taiwan (TA) 24 18,572*
Thailand (TH) 94 11 99.5 24 3,164
Turkey (TK) 49 18 85 39 7,834
United Kingdom (UK) 25 58 39,472
United States (US) 80 35 66 32 43,961
Source: World Bank; *Chen and Liu (2013) for Taiwan. GDP
per capita is at constant prices in USD.
378 D.C. Bhimjee et al. / International Review of Financial
Analysis 48 (2016) 376–387
S and K being the number of clusters and regimes, respectively.
Let
f(yi;ψ) be the probability density function associated with the
banking
index return rate pertaining to country i. The HRSM-S is given
by

f yi; ψð Þ ¼ ∑S
wi¼1
∑
K
zi1¼1
∑
K
zi2¼1
… ∑
K
ziT ¼1
f wi; zi1; …; ziTð Þf yijwi; zi1; …; ziTð Þ ð1Þ
The right side of Eq. (1) indicates that the underlying model
struc-
ture is typical of a mixture model consisting of the time-
constant latent
variable wi and T realizations of the time-varying latent
variable zit. The
observed data density f(yi;ψ) is obtained by marginalizing over
the
latent variables and is provided by the total probability theorem
in
which the marginal probability is obtained by the sum of
conditional
probabilities over the partitions {wi, zi1, … , ziT}.
Furthermore, in view
of the Markov assumption for the sequence {zi1, … , ziT}, the
term (wi,
zi1, … , ziT) of Eq. (1) can be further transformed into.

f wi; zi1; …; ziTð Þ ¼ f wið Þf zi1jwið Þ∏Tt¼2 f zitjzi;t�1; wi
� �
ð2Þ
where f(wi) essentially represents the probability of a specific
country's
banking index belonging to a given cluster w, the multinomial
parame-
ter λw=P(Wi = w), f(zi1 | wi) represents the initial-regime
probability,
and f(zit | zi , t - 1, wi) represents the latent transition
probability of the
Markov process in cluster wi. Moreover, the observed index
return value
depends solely on the regime that is applicable at that specific
chrono-
logical point, i.e., response yit is independent of the returns at
other mo-
ments (this is known as the local independence assumption).
Simultaneously, the observed value is also independent of
regimes at
other times. These assumptions can be formulated as follows:
f yijwi; zi1; …; ziTð Þ ¼ ∏
T
t¼1
f yitjzitð Þ ð3Þ
where the probability density that a particular observed index
return
value at time t conditional on the regime in place at that
chronological
point, f(yit | zit), is assumed to follow a univariate Gaussian
density

function.
In addition, the parameters of the HRSM-S are estimated using
max-
imum likelihood (ML) estimation, where the log-likelihood
function is
↕ψ; yð Þ ¼ ∑ni¼1logf yi; ψð Þ: ð4Þ
The expectation–maximization algorithm can subsequently be
employed to solve this maximization problem. Nevertheless, it
should
be noted that the application of the expectation–maximization
algo-
rithm requires both a lengthy computational effort and a
cumbersome
computer storage capacity. To circumvent these computational
issues,
6
15
278
Argentina
694
2298
Australia

159
1226
Austria
132
2557
Belgium
44
765
Brazil
665
2653
Canada
0
5
Chile
1
10
Czech Rep.

127
856
Denmark
123
551
Finland
281
1538
France
131
918
Germany
680
4584
Greece
270
930
Hong Kong

3
37
Hungary
24
370
India
119
11225
Ireland
23
111
Israel
908
4284
Italy
0
4
Japan

492
1914
Luxembourg
221
754
Malaysia
1527
7854
Mexico
247
6782
Netherlands
19
128
Norway
2
69
Pakistan

19
191
Peru
4
20
Philippines
22
149
Poland
87
482
Portugal
6
329
Russia
180
553
Singapore

142
896
South Africa
205
767
Spain
105
499
Sweden
341
1404
Switzerland
Jan02 Mar04 May06 Jun08 Aug10
2
8
Taiwan
2
10
Thailand

77777
1234370
Turkey
2461
18859
United Kingdom
325
1885
United States
Fig. 1. Time series of country banking indexes (in USD). Source
of underlying data: Datastream; China is not included in this
figure due to unavailability of full data.
Analysis 48 (2016) 376–387
a special variant of the expectation–maximization algorithm, the
Baum–Welch algorithm, has been advanced in the literature and
allows
the abovementioned maximization problem to be more easily
solved
(Dias et al., 2008).
Furthermore, the choice of the appropriate number of clusters
(S)
and regimes (K) is traditionally based on the analysis of
statistical infor-
mation criteria. For example, to identify the number of clusters
(S), the

Bayesian information criterion (BIC) value was employed.
Thus, the
most appropriate pair of (S, K) values is selected for all of our
model ap-
plications until the optimized (i.e., minimized) value of the BIC
is
achieved.
3. Data
The sample is composed of countries' banking indexes5 and
includes
a diversified set of developed and emerging market banking
industries.
This sample diversity facilitates our analysis of the
heterogeneity of the
regime dynamics associated with the impact of the GFC on
different rep-
resentative banking systems and institutions worldwide.
5 Datastream uses the Industry Classification Benchmark (ICB)
to classify firms in indus-
tries; ICB was jointly created by FTSE and Dow Jones. The
indexes have several layers, de-
pending on the level of detail of the industry that the user
wants. To be more specific, the
ICB classifies the aggregated market into 10 industries, divides
them into 19 supersectors,
and then subdivides them into 41 sectors, for a total of 114
subsectors. We use the Banks
sector.
According to Datastream, indexes are calculated on a
representative list of stocks for each
market. The number of stocks for each market is determined by
the size of the market. The
sample covers a minimum of 75–80% of total market
capitalization. Sector and market ag-

gregations are weighted by market value. More details regarding
the indices can be found
in:
A Guide to the Industry Classification Benchmark, 2012 in
http://www.icbenchmark.com
Datastream Global Equity Indices User Guide:
http://extranet.datastream.com/data/
Equity%20indices/Index.htm
Table 1 presents the sample of countries6 and certain indicators
re-
lated to the size and development of the banking sector. The
economic
importance of banks is high in countries such as Hong Kong,
Japan,
and Luxembourg, where we find a large ratio of bank deposits
to gross
domestic product (GDP). If we examine their retail presence, we
con-
clude that the number of bank branches per 100,000 adults is
high in
Luxembourg, Italy, Portugal, and Spain.
Foreign banks are largely important in Luxembourg, Ireland and
Hungary, whereas in countries such as Japan, Norway or
Sweden, they
have small importance. Bank credit to bank deposits is high in
countries
such as Ireland or China. Finally, the last column presents GDP
per capita,
which ranges from 748 USD in Pakistan to 81,565 in
Luxembourg.
The collected indexes7 depict the stock market valuation of the
most
significant financial institutions of any given country (or
jurisdiction);

The country selection is contingent on data availability from our
data source. More-
over, the methodology requires a “balanced panel”. Further, we
excluded specific coun-
tries that present little liquidity, i.e., for which zero returns
consequently repeat. We
note also that these indexes are related to either countries or
special administrative re-
gions (Hong Kong). These disentangle neither cross-border
ownership of banks nor the di-
rect influence of the shadow banking system; we thank an
anonymous referee for noting
this. Due to data availability constraints, Chinese banking
institutions are only included in
our model applications for the 2007–2010 period; the
corresponding findings are included
in the Supplementary Appendix, which is available upon
request.
7 These indexes represent a proxy for the performance of each
country's banking indus-
try; the main advantage of using these indexes resides in the
comparability among the se-
ries; accordingly, the paper's framework involves the global
macro-financial architecture
and the propagation of international financial contagion
processes through the global
banking industry during the preceding decade. Regrettably, the
influence of the shadow
banking system cannot be properly addressed by our data
because the highly complex
‘vertical slicing’ of the credit intermediation process of
traditional banks into a highly spe-
cialized network of credit-related specialisms is performed by
non-bank financial inter-
mediaries, which are typically not publicly quoted (Pozsar,

Adrian, Ashcraft, & Boesky,
2012). However, the impact of the role of these specialized
financial intermediaries is
assessed through international financial contagion, the latter
being very observable in
our analysis of the GFC. We want to thank an anonymous
referee for highlighting this
issue.
http://www.icbenchmark.com
http://extranet.datastream.com/data/Equity%20indices/Index.ht
m
http://extranet.datastream.com/data/Equity%20indices/Index.ht
m
Image of Fig. 1
Table 2
Summary statistics of banking index returns.
This table reports descriptive statistics of banking indexes
(weekly), namely the mean, standard deviation (Std. Deviation),
skewness, and kurtosis. Returns are computed as the first dif-
ferences of the logarithm of prices and presented in percentage.
The last column presents the Jarque–Bera test of normality and
respective p-values. All returns are in USD. Sample period is
from January 2, 2002 to August 25, 2010.
Country Mean Median Std. deviation Skewness Kurtosis
Jarque–Bera test
[both adjusted for bias] statistics p-value
Argentina (AR) 0.145 0.455 5.695 −0.938 8.474 605.80 0.000
Australia (AU) 0.153 0.546 4.049 −1.000 10.382 1059.51 0.000
Austria (OE) 0.235 0.735 5.457 −0.784 7.122 351.07 0.000
Belgium (BG) −0.171 0.555 7.132 −1.185 11.455 1398.17 0.000

Brazil (BR) 0.408 0.770 5.919 −1.538 12.732 1890.95 0.000
Canada (CN) 0.218 0.385 3.510 −0.255 5.763 141.19 0.000
Chile (CL) 0.295 0.501 3.239 −1.169 12.716 1811.47 0.000
Czech Rep. (CZ) 0.383 0.393 5.714 −0.588 8.437 558.70 0.000
Denmark (DK) 0.086 0.455 4.798 −0.728 10.466 1047.40 0.000
Finland (FN) 0.217 0.460 4.723 −1.515 16.157 3310.61 0.000
France (FR) 0.018 0.524 5.583 −0.057 6.915 275.67 0.000
Germany (BD) −0.047 0.299 5.519 −0.896 9.300 776.30 0.000
Greece (GR) −0.112 0.428 5.383 −0.699 6.793 294.66 0.000
Hong Kong (HK) 0.028 0.132 3.348 −0.477 11.774 1410.62
0.000
Hungary (HN) 0.266 1.063 7.465 −1.320 11.541 1449.62 0.000
India (IN) 0.596 0.690 5.809 0.316 6.312 203.94 0.000
Ireland (IR) −0.438 0.134 11.672 −0.253 29.401 12,675.42
0.000
Israel (IS) 0.176 0.326 4.362 −0.109 5.780 138.94 0.000
Italy (IT) −0.024 0.247 4.475 −0.446 6.105 187.33 0.000
Japan (JP) −0.007 0.000 4.582 −0.049 4.423 35.67 0.000
Luxembourg (LX) 0.064 0.278 3.129 −0.414 8.534 565.11 0.000
Malaysia (MY) 0.255 0.284 2.656 −0.120 5.147 82.95 0.000
Mexico (MX) 0.257 0.554 3.986 −0.929 12.572 1723.72 0.000
Netherlands (NL) −0.487 0.283 8.462 −8.634 132.343
310,152.74 0.000
Norway (NW) 0.198 0.587 6.170 −0.908 11.909 1498.24 0.000
Pakistan (PK) 0.471 0.642 5.356 −0.894 6.583 289.65 0.000
Peru (PE) 0.457 0.191 3.175 0.705 10.457 1042.45 0.000
Philippines (PH) 0.182 0.150 3.527 −0.004 5.511 112.41 0.000
Poland (PO) 0.241 0.599 5.492 −1.349 10.166 1063.42 0.000
Portugal (PT) −0.139 0.142 3.892 −0.661 6.135 208.41 0.000
Russia (RS) 0.753 0.983 6.636 −0.625 13.104 1878.83 0.000
Singapore (SG) 0.155 0.082 3.684 0.453 10.366 996.53 0.000
South Africa (SA) 0.324 0.466 5.451 −0.644 7.348 370.92 0.000
Spain (ES 0.047 0.291 4.618 −0.398 6.032 176.32 0.000
Sweden (SD) 0.114 0.531 5.122 −0.599 7.356 368.01 0.000
Switzerland (SW) 0.012 0.139 4.925 −0.236 6.579 233.91 0.000
Taiwan (TA) 0.178 0.206 4.363 0.019 5.265 91.27 0.000

Thailand (TH) 0.308 0.252 4.525 −0.088 3.760 10.35 0.006
Turkey (TK) 0.398 0.912 7.048 −0.596 4.698 77.19 0.000
United Kingdom (UK) −0.146 0.105 5.129 −0.691 13.647
2089.84 0.000
United States (US) −0.159 0.017 4.903 −0.167 12.091 1498.63
0.000
Analysis 48 (2016) 376–387
they have been extracted from the Datastream database using a
weekly
frequency and are in United States dollars (USD) to facilitate
interna-
tional comparisons. The original source of data is the Industry
Classifica-
tion Benchmark (ICB), which collectively depicts more than
8000 banks
that provide a broad range of financial services (including retail
bank-
ing, loans and money transmissions).
Accordingly, the beginning point of our indexes is 2002 (more
spe-
cifically, January 2), and the end-point of our data is August 25,
2010.
The starting date was chosen for two reasons. First, this date is
the
year subsequent to the occurrence of the previous global
financial crisis,
the 2001 ‘dot-com’ crisis. Second, our choice concurs with the
beginning
of the upward trajectory of the ‘subprime’ crisis, which was a
related
business cycle that led directly to the present crisis.8 Further,
this
ample time frame allows us to have a broad overview of the

crisis.
That is, the time interval between 2002 and 2010 encompasses
not
only the upward phase of the ‘subprime’ global business cycle
prior to
the occurrence of the GFC but also the ensuing downward
phase.
8 For example, in the case of the United States, the epicenter of
the present crisis, the of-
ficial business cycle dating committee, the National Bureau of
Economic Research (NBER),
dated this upward phase associated with the ‘subprime’ cycle as
between November 2001
and December 2007; however, the ‘subprime’ crisis was dated
as between December 2007
and June 2009 (National Bureau of Economic Research, 2010).
Fig. 1 portrays the data used herein and describes the global
evolution
of the stock market valuation of each banking industry in the
countries
included in our sample, considering our adopted timeline.
Moreover, Table 2 provides summary statistics pertaining to the
country banking data collected. In addition to presenting the
standard
descriptive statistics associated with each country's banking
index, the
table presents the respective results for the Jarque–Bera
statistic. The re-
sults indicate that the null hypothesis of normality can be safely
rejected. The mean returns are negative for the Netherlands,
Ireland,
Belgium, the United Kingdom, and the United States and are
very high
in countries such as Russia, India, Pakistan, and Peru. The
standard devi-

ation is the lowest in the United States and is very high in
countries such
as Russia, Turkey, and Brazil.
4. Empirical results
4.1. Banking indexes in the 2002–2010 period
This section presents the results of the model. The model
selection
criterion (BIC) identifies the existence of heterogeneity (S) and
a
multi-regime (K) framework simultaneously. The minimization
of the
BIC criterion, which is equal to 104,031.14, yields an optimal
solution
of two clusters and three regimes (S=2,K =3). That is, our
findings
Table 3
Estimated prior probabilities, posterior probabilities and modal
classes for HRSM-2.
This table reports the classification of banking indexes by
clusters. Prior probabilities pro-
vide the size of each cluster or group and posterior probabilities
express the evidence that
a given stock market belongs to a given cluster. The maximum
posterior probability indi-
cates the assignment to the modal cluster.
Cluster 1 Cluster 2 Cluster
Prior probabilities 0.296 0.704

Posterior probabilities
Argentina (AR) 0.997 0.003 1
Australia (AU) 0 1 2
Austria (OE) 0.001 0.999 2
Belgium (BG) 0 1 2
Brazil (BR) 0.955 0.046 1
Canada (CN) 0 1 2
Chile (CL) 0 1 2
Czech Rep. (CZ) 0.996 0.004 1
Denmark (DK) 0 1 2
Finland (FN) 0 1 2
France (FR) 0 1 2
Germany (BD) 0 1 2
Greece (GR) 0.044 0.956 2
Hong Kong (HK) 0 1 2
Hungary (HN) 0.959 0.041 1
India (IN) 0.98 0.02 1
Ireland (IR) 0 1 2
Israel (IS) 0.976 0.025 1
Italy (IT) 0 1 2
Japan (JP) 0.03 0.97 2
Luxembourg (LX) 0 1 2
Malaysia (MY) 0 1 2
Mexico (MX) 0.005 0.995 2
Netherlands (NL) 0 1 2
Norway (NW) 0.002 0.998 2
Pakistan (PK) 1 0 1
Peru (PE) 0 1 2
Philippines (PH) 0 1 2
Poland (PO) 1 0.001 1
Portugal (PT) 0 1 2
Russia (RS) 0.993 0.008 1
Singapore (SG) 0 1 2
South Africa (SA) 0.998 0.002 1
Spain (ES) 0 1 2
Sweden (SD) 0 1 2

Switzerland (SW) 0 1 2
Taiwan (TA) 0.976 0.024 1
Thailand (TH) 0.95 0.05 1
Turkey (TK) 0.072 0.929 2
United Kingdom (UK) 0 1 2
United States (US) 0 1 2
Table 5
Estimated cluster-specific probabilities of regimes, regime
occupancy for each cluster, re-
gime transition and sojourn time.
P(Z | W) represents the proportion of banking indexes in each
regime for each cluster. Re-
maining rows report transition probabilities between regimes.
Standard errors are report-
ed in round brackets. Sojourn time represents the time expected
for banking indexes to
exit a given regime.
Cluster 1 Cluster 2
Regime
1
Regime
2
Regime
3
Regime
1
Regime
2

Regime
3
P(Z|W) 0.091 0.711 0.198 0.090 0.400 0.510
(0.021) (0.030) (0.029) (0.015) (0.028) (0.034)
Regime 1 0.931 0.069 0.001 0.939 0.061 0.000
(0.015) (0.016) (0.004) (0.010) (0.010) (0.000)
Regime 2 0.008 0.950 0.042 0.014 0.962 0.024
(0.002) (0.012) (0.011) (0.002) (0.004) (0.004)
Regime 3 0.001 0.148 0.851 0.000 0.019 0.981
(0.003) (0.030) (0.029) (0.000) (0.002) (0.002)
Sojourn time (weeks) 14.388 19.920 6.693 16.420 26.110
52.632
Analysis 48 (2016) 376–387
suggest that there are two distinct clusters of countries
operating under
the different dynamics of three very distinctive regimes (thus,
in addi-
tion to the two end-of-spectrum bull and bear market regimes,
there
is an intermediate regime). Both the composition of clusters and
the
characteristics of regime profiles have been endogenously
determined
within each model application. That is, the latter are determined
by
each of the models' empirical applications.
Table 3 summarizes the results pertaining to the estimated prior
class probabilities (the cluster dimension), the posterior

probabilities
associated with the distribution of the banking industries across
the
Table 4
Estimated marginal probabilities of regimes, mean returns and
variances – 2002–2010.
This table reports the estimated marginal probabilities of
regimes. P(Z) is the average proporti
regimes. The last three column is the variance of the returns in
each regime. Standard errors a
P(Z) Return (m
Regime 1 Regime 2 Regime 3 Regime 1 Regime
0.091 0.492 0.417 −1.850 0.250
(0.012) (0.031) (0.034) (0.349) (0.055)
two clusters (reflecting the degree of membership to each
cluster),
and the respective modal cluster. The estimated prior class
probabilities
are 0.296 (cluster 1) and 0.704 (cluster 2), which reflect the fact
that the
first cluster is significantly smaller than the second and that
banking in-
dustries are unevenly distributed across these two clusters for
the
2002–2010 period. The estimated posterior cluster probabilities
reflect
the degree of membership associated with each of the clusters,
and
these probabilities are conditional on the observed data. The
modal
cluster column ascribes each country to a specific cluster,
considering
these probabilities. Thus, cluster 1 is composed of the following

12
banking indexes: Argentina, Brazil, Czech Republic, Hungary,
India,
Israel, Pakistan, Poland, Russia, South Africa, Taiwan, and
Thailand,
i.e., the group is formed mainly by emerging markets.
Conversely, clus-
ter 2 is composed of the following 29 indexes: Australia,
Austria,
Belgium, Canada, Chile, Denmark, Finland, France, Germany,
Greece,
Hong Kong, Ireland, Italy, Japan, Luxembourg, Malaysia,
Mexico,
Netherlands, Norway, Peru, Philippines, Portugal, Singapore,
Spain,
Sweden, Switzerland, Turkey, the United Kingdom, and the
United
States.
The regime's profile and respective dynamics are described in
Table 4. There is a 0.091 (regime 1), 0.492 (regime 2) and 0.417
(regime
3) probability that the banking indexes may be in one of the
three re-
gimes. Regime 1 exhibits negative returns and a high degree of
volatility
(−1.85 and 177.17, respectively); regime 2 exhibits positive
returns as-
sociated with a much lower degree of volatility (0.25 and 22.64,
respec-
tively); and regime 3 exhibits the highest (positive) returns with
the
lowest volatility (0.43 and 4.94, respectively). That is, regime 1
is mark-
edly associated with bear market dynamics, regime 2 is
associated with

mild bull market dynamics, and regime 3 is associated with
buoyant bull
market dynamics. The results are in accordance with the
common ac-
knowledgement of the presence of asymmetric volatility in
financial
markets, i.e., volatility is very likely to be higher when the
performance
of financial markets is faltering and lower when the
performance is
buoyant. The regime heterogeneity described herein is in
accordance
with previous studies (such as Guidolin and Timmermann
(2007)),
on of markets in each regime over time. The next three columns
are the log returns of the
re reported in round brackets.
ean) Risk (variance)
2 Regime 3 Regime 1 Regime 2 Regime 3
0.434 177.170 22.641 4.943
(0.029) (8.169) (0.649) (0.146)
Analysis 48 (2016) 376–387
which incorporate and validate regime heterogeneity across
much lon-
ger time frames.
Let P(Z | W) represent the estimated probability that each
cluster's
set of country banking industries is in a given regime,

conditional on
the specificities of each individual cluster. The results in Table
5 suggest
that the banks associated with cluster 1 have a 0.091 probability
of
being in a bearish environment, a 0.711 probability of being in a
mild
bull environment, and a 0.198 probability of being in a bullish
environ-
ment. Similarly, the banking indexes associated with cluster 2
have a
0.09 probability of being in a bearish environment, a 0.40
probability
of being in a mild bull environment, and a 0.51 probability of
being in
a bullish environment. That is, banking industries in the latter
cluster
have a higher probability of operating under more bullish
financial con-
ditions during the adopted time frame. This is remarkable
because the
probability of operating in a recessionary regime is practically
the
same (approximately 0.09) for both clusters. The main
difference be-
tween these clusters concerns the incidence of heterogeneity in
the
dominant regimes for each cluster. The dominant regime for
cluster 1
countries is the mild bull regime 2, whereas that for cluster 2
countries
is the bull market regime 3. That is, banking institutions in
emerging
market economies typically operate in a mild bull environment
(0.711), but banking institutions in advanced economies
typically oper-

ate under a bullish environment (0.51). This distinction in the
dominant
regime for each cluster is properly ascertained through the
HRSM model
applications.
A potential explanation for the observed discrepancy in
dominant
regimes for each cluster may be attributed to the degree of
exposure
to the globally expansive twin bubbles in the real estate and
credit de-
rivative markets. This dual expansion specifically benefited the
banking
industries of advanced economies during the upswing of the
economic
cycle.9 This benefit is noticeable in the context of the financial
perfor-
mance of banks belonging to the most advanced economies in
our sam-
ple, as attested by the composition of cluster 2. Simultaneously,
the
same conclusion may be attained, given that cluster 2 banking
indus-
tries exhibit more integrated financial network structures than
do
their counterparts in cluster 1. For example, Allen and Carletti
(2009)
suggest that financial networks encompassing banking systems
with
more interconnected links typically shelter internationally
diversified
banks. This implies that banking industries in advanced
economies are
more interconnected, which may extend the duration of bull
markets

in these economies. This explanation for the discrepancy in the
domi-
nant regimes for each cluster warrants additional scrutiny in
subse-
quent research.
The transition probabilities between these three regimes for
each of
the clusters are also presented. Strong intra-cluster regime
persistence
continues to be observed during this period, with banking
indexes be-
longing to both clusters exhibiting very high probabilities of
remaining
in a given regime (with 0.931, 0.950 and 0.851 vs. 0.939, 0.962
and
0.981, respectively, for regimes 1, 2 and 3 in clusters 1 and 2).
Regarding
the mean sojourn time (which reflects the duration of the bear,
mild
bull and bull regimes, as measured in weeks), banking
industries associ-
ated with cluster 1 tend to take less time to emerge from any
given re-
gime than do their cluster 2 counterparts (14.39 vs. 16.42 weeks
for
regime 1, 19.92 vs. 26.11 weeks for regime 2, and 6.69 vs.
52.63 weeks
for regime 3). The difference is greatest in the mean sojourn
time asso-
ciated with regime 3 (52.63–6.69 = 45.94 weeks), which
suggests that
cluster 2 banking industries tend to remain in the bull regime
for more
weeks (a multiple of 52.63/6.69 = 7.866 times as much) before
transitioning to other regimes. This result may be explained by

the prof-
itability buoyancy exhibited by banks belonging to the countries
in
9 The GFC was a systemic event associated with the bursting of
the twin economic bub-
bles in the United States real estate and the credit markets.
Shiller (2008) identifies the
GFC as the “deflating of a speculative bubble in the housing
market that began in the United
States in 2006 and has now cascaded across many other
countries in the form of financial fail-
ures and a global credit crunch” (p. 9).
cluster 2. The latter banking industries operated under credit
and real
estate asset bubble environments throughout the business cycle
under
scrutiny, as the cases of the United Kingdom and the United
States clear-
ly demonstrate.10
The synchronization of regimes across our sample set of country
banking industries is also presented. The posterior probabilities
de-
scribed in Figs. 2 and 3 indicate a significant synchronized
impact that
is associated with the occurrence of the GFC across our sample.
Fig. 2 de-
picts the posterior probabilities of being in a given regime for
cluster 1
countries. Until mid-2008, the banking indexes comprised
therein
were primarily alternating between regimes 2 and 3, with the
former
being the dominant regime of the two, notwithstanding country-
specific idiosyncrasies. Therefore, during this time frame,
intermediate

bull and bull regimes appear to dominate over the bear regime.
In addi-
tion, Argentina, Brazil, and Russia experienced a crisis in
2001–2002.
However, the financial impact associated with the occurrence of
the
present GFC appears to have been widely felt in 2008. The
impact was
transversally persistent and synchronized across the entire
cluster. Ac-
cordingly, the summer of 2008 appears to have witnessed the
full
onset of the impact of the GFC for the entire sample of country
banking
indexes. The corresponding bear regime duration varied across
banking
indexes, with Hungary being the worst-hit country and
Argentina the
least affected. The crisis subsided in 2009, although the rebound
capac-
ity is very distinct across the cluster. Banks in Hungary, for
example,
were overwhelmed by a further bear episode in 2010. Fig. 3
depicts
the banking indexes of cluster 2. The results confirm that the
GFC indeed
constituted a systemic episode that had a persistent impact
throughout
the cluster's sample. For example, these attributes can be clearly
discerned by the fact that banks belonging to both the United
Kingdom and the United States, which were at the epicenter of
the sys-
temic episode under study, operated under a very bullish
environment
throughout the ‘subprime’ cycle. Once the systemic crisis took
root in

2008, these institutions were subjected to a severe downturn
that sub-
sided in mid-2009. Overall, these institutions experienced a
sustained
asset price boom that was followed by a severe downturn. The
main dif-
ference between the figures is that the overall propensity to
experience
a bull regime for the banking industries included in cluster 2 is
higher
than that associated with the cluster 1 banking industries.11 Our
find-
ings also confirm that a high degree of financial
interconnectedness is
positively correlated with the development of the
abovementioned
twin asset price booms. Under the influence of the latter
bubbles, the fi-
nancial institutions that reaped the benefits of financially
integrated
structures were subsequently compromised by its implosion
through
severe financial contagion processes. As observed in both Figs.
2 and 3,
the occurrence of the systemic event under study was truly
global and
highly synchronized.
Table 6 – Panel A shows the results for the synchronization of
re-
gimes. The results are aggregated by cluster for the sake of
simplicity.
In accordance with Dias and Ramos (2013), synchronization is
mea-
sured by the likelihood that the country set of banking
industries shares

the same regime and is quantified by their proposed logit-based
corre-
lation measure. This measure has the advantage of filtering out
the ex-
treme observations normally observed during crisis episodes.
The
measure is computed as
logititk ¼ log
α̂itk
1 � α̂itk
� �
ð5Þ
where α̂itk is the posterior probabilities of being in regime k in
country i
at time t.
10 A detailed analysis of the impact on international bank
lending and borrowing for
banks domiciled in the United Kingdom and the United States
was presented by Batten
and Szilagyi (2012).
11 The exception is Turkey, which exhibits high volatility
across the entirety of our
adopted timeline.
Fig. 2. Estimated posterior probability of the three regimes
within cluster 1 (2002–2010).
Analysis 48 (2016) 376–387
The synchronization is quantified using the product–moment

corre-
lation between the logits for two time series.
The banking indexes of cluster 2 are synchronized with each
other,
among all regimes and in regimes 1 and 3 in particular. As
expected,
the synchronization of banks of cluster 1 is larger in regime 1,
the bear
regime, and is substantially smaller in regimes 2 and 3, which
indicates
that the paths are very different. The synchronization of regimes
be-
tween clusters 1 and 2 ranges between 0.53 in regime 1, the
bear re-
gime, and 0.09 in regime 2, the mild bull regime.12
4.2. Banking indexes in the global financial crisis in the 2007–
2010 period
In this section, we present and discuss the findings for the
period
encompassing the GFC. Given space constraints, the results are
set out
in the Supplementary Appendix. The sample date starts on July
2007
and ends on August 2010. The start date reflects the month in
which
the first signs of financial distress occurred in the financial
markets13
and after which certain major financial systemic failures
occurred
(e.g., Bear Stearns, Lehman Brothers). Furthermore, our results
contem-
plate the specific case of the Chinese banking index (the

corresponding
time series data were available for the 2007–2010 period) in
addition to
the banking indexes pertaining to the countries already
encompassed
by our 2002–2010 analysis.
In general, 2007 and 2008 were very critical for the
performance of
the global banking industry. Indeed, four major and resounding
system-
ic failures disrupted the industry, thereby aggravating the
dynamics of
the international financial contagion (IFC) processes. These
four sys-
temic examples, Bear Sterns, Lehman Brothers, Northern Rock
and
IKB, were all connected to the implosion of the twin real estate
and
12 Country synchronization in the different regimes is presented
in the Supplementary
Appendix. The Supplementary Appendix is available online.
13 July 2007 witnessed a series of smaller defaults and loss
warnings by US financial in-
stitutions exposed to ‘subprime’ assets. As a premier US
financial player, Bear Stearns pub-
licly acknowledged on July 17, 2007 major losses (up to 90%)
on two of its hedge funds
specializing in ‘subprime’-related debt investments (Cox &
Glapa, 2009).
credit market bubbles. These examples illustrate both the
interconnec-
tedness among the country banking indexes operating in
globalized fi-
nancial markets and the effects associated with international
financial

contagion processes.14
The optimal choice of parameter values for clusters (S) and
regimes
(K) indicates that the value of S is equal to one and that the
value of K is
equal to four. That is, the optimal result yields a sole
undifferentiated
and non-heterogeneous cluster that contains all banking
indexes, oper-
ating under the framework of four distinct regimes.
In the Supplementary Appendix, we present the table with the
four
regimes. Regime 1 is associated with a strong bearish
framework (se-
vere negative returns of −4.671 coupled with a very high
volatility of
377.092); regime 2 is associated with a mild bearish
environment
(mild negative returns of −0.982 with a low volatility of
17.124); re-
gime 3 is associated with a subdued bearish framework (low
negative
returns of −0.068 associated with a medium volatility of
55.227); and
regime 4 is associated with a strong bull environment (high
returns of
0.936 coupled with a very low volatility of 10.46). Conversely,
P(Z) is
the average probability that banking indexes are in a specific
regime;
it is very high for intermediate regime 3 (0.348) and 2 (0.305),
followed
by the bullish regime 4 (0.284). The average probability of
operating

under the contractionary regime 1 is 0.063.
For a comparison with the results of the previous section, we
sepa-
rate banking indexes into the two previous clusters and analyze
their re-
gime synchronization. Panel B of Table 6 presents the
synchronization
during the crisis period. We find a larger synchronization
among the
countries of cluster 2 than among the countries of cluster 1
because
the latter group is composed primarily of emerging markets that
had
previously exhibited segmentation. The synchronization of
regimes
among the countries of cluster 2 is similar to that observed for
the
whole sample period. Overall, a large synchronization both
between
14 The strength of IFC is clearly observable in the work of Cho,
Hyde, and Nguyen (2015),
who use a large sample of approximately 31,000 firms across 31
markets to document the
GFC's global reach compared with other, less powerful financial
episodes.
Image of Fig. 2
within cluster 2 (2002–2010).
Analysis 48 (2016) 376–387
and within clusters is noticeable in all regimes. The absence of

distinct
synchronization is in accordance with the presence of a sole
cluster of
countries.
To obtain a better understanding of the dynamics of banking
indus-
tries during the crisis, Figs. 4 and 5 display the regime
dynamics, with
certain marked subperiods based on the official timelines
provided by
the Federal Reserve Board of St. Louis (2009) and the Bank for
Interna-
tional Settlements (Filardo et al., 2010). These studies separate
the time-
line of the GFC into four phases. Phase 1 spans from August 1,
2007 to
Table 6
Synchronization of regimes.
This table presents the association between banking indexes
based on the posterior probability
average that excludes a country's synchronization with itself.
Panel A: 2002–2010
Regime 1 Regime
Cluster 1 Cluster 2 Cluster
Cluster 1 0.52 0.23
Cluster 2 0.53 0.70 0.09
Panel B: 2007–2010
Regime 1 Regime 2

Cluster 1 Cluster 2 Cluster 1 Cluster
Cluster 1 0.65 0.58
Cluster 2 0.67 0.71 0.60 0.64
September 15, 2008, and is described as “initial financial
turmoil”.
Phase 2 is defined as “sharp financial market deterioration”
(September
16, 2008–December 31, 2008), phase 3 is described as
“macroeconomic
deterioration” (January 1, 2009 until March 31, 2009) and phase
4 is a
phase of “stabilization and tentative signs of recovery” (post-
crisis peri-
od, until the end of the sample period). In the figures, we again
separate
the clusters of banking indexes. We confirm that the regime
dynamics
have many resemblances. During phases 2 and 3, i.e., from
September
16, 2008 to March 31, 2009, most banking industries are in a
high
of being in the same regime (see Eq. 5). Average
synchronization is an equally weighted
2 Regime 3
1 Cluster 2 Cluster 1 Cluster 2
0.30
0.43 0.36 0.65
Regime 3 Regime 4
2 Cluster 1 Cluster 2 Cluster 1 Cluster 2

0.29 0.56
0.33 0.40 0.58 0.63
Image of Fig. 3
within cluster 1 (2007–2010). The lines mark the following
dates: September 15, 2008, December 31, 2008, and March 31,
2009,
which correspond to different phases of the global financial
crisis. The phases are initial financial turmoil – August 1,
2007–September 15, 2008; sharp financial market deterioration
–
September 16, 2008–December 31, 2008; macroeconomic
deterioration – January 1, 2009–March 31, 2009; and
stabilization and tentative signs of recovery – April 1, 2009 to
the end of
the sample period. Source: Federal Reserve Board of St. Louis
(2009) and the Bank for International Settlements (Filardo et
al., 2010).
Analysis 48 (2016) 376–387
volatility regime with negative means. In phase 4, despite signs
of re-
covery in the majority of banks, in some cases, e.g., Hungary,
Belgium,
Greece, France, and Spain, episodes of slumping back into the
crisis re-
gime remain; the extreme case is Ireland, which never leaves
the crisis
regime. In contrast, the banking industry indexes in Thailand,
Taiwan,
Japan, Malaysia, and Singapore appear to be the least affected
and re-

cover at a much faster pace.
5. Concluding remarks
The GFC of 2007–2009 was a systemic breakdown of
unprecedented
proportions affecting financial markets and institutions,
particularly
among banking institutions worldwide. The application of a
novel
panel regime-switching methodology, the HRSM, has unearthed
a
framework of heterogeneous banking industry performance that
is cap-
tured within a global macro-financial setting. Our findings are
summa-
rized in the following paragraphs.
First, heterogeneous global banking industry performance is
appro-
priately captured by the HRSM. Each of the clusters operated
under dis-
tinctive regime dynamics. These mutually exclusive regimes are
clearly
identifiable with bull, intermediate and bear financial regime
dynamics,
thus adding a deeper regime granularity to our findings.
Second, the inter- and intra-cluster synchronization patterns of
the performance of the banking industries comprising our
sample indi-
cate the severity of the underlying international financial
contagion pro-
cesses that are at work in a post-crisis environment. The results
further
reveal that the onset of the GFC may be associated with a loss

of hetero-
geneity due to the impact of a transversal common shock (the
GFC).
The heterogeneity in our findings concurs with the results of
Ehrmann, Fratzscher, and Mehl (2009) and Shehzad and De
Hann
(2013). The former confirms the existence of a set of
heterogeneous eq-
uity market responses to the GFC by focusing on both banking
and non-
banking segments rather than on macro-aggregates; the latter
finds
that stock prices of banks in emerging countries were less
affected by
the systemic shock than the corresponding prices of their
counterparts
in developed economies by focusing on individual banks rather
than
macro-aggregates.
According to Beltratti and Stulz (2012), a possible explanation
for
the differing performance of stock returns of the large banking
indexes
comprising our sample may reside in a combination of factors.
The latter
may involve the role of regulation, the quality of banking
governance,
and the specificities of the balance sheets of important banking
institu-
tions. Regime changes after the GFC appear to be in accordance
with the
over-hauling of the expectations associated with bank stock
returns be-
fore and after the crisis. Before the crisis, stock markets favored

banking
business strategies involving innovative financial related
products. The
onset of the crisis may have then shifted market expectations in
favor
of more conservative banking business strategies promoting
staple
products (Beltratti & Stulz, 2012).
Furthermore, the existence of large-scale banking operations
involv-
ing securitization lines may have strained the transmission
channels to
the real economy (for example, by constraining the availability
of credit,
once liquidity pressures set in). At a global macroeconomic
level, het-
erogeneous performance within the banking industry may have
caused
advanced economies overtly dependent on sophisticated credit
chan-
nels (i.e., of securitized extraction) to succumb more
perniciously to
the effects of the GFC than emerging market economies did, and
this
heterogeneity has been innovatively depicted in our model
applications.
These findings are fundamental to understand the significant
role
played by international financial contagion processes in the
aftermath
of a powerful systemic shock such as the GFC, as the paper
exposes
the high degree of cross-country transversality and the
simultaneity of

global banking contagion across the sample. These findings
contribute
to the proper understanding of how international financial
contagion
processes work and the corresponding implications thereof to
the for-
mulation of macro- and micro-prudential policies. Directions
for future
study should also address the following topics: first, the link
between
international financial contagion and trade interconnectedness
and
the corresponding impact on real economies throughout
systemic
Image of Fig. 4
within cluster 2 (2007–2010). The lines mark the following
dates: September 15, 2008, December 31, 2008, and March 31,
2009,
which correspond to different phases of the global financial
crisis. The phases are initial financial turmoil – August 1,
2007–September 15, 2008; sharp financial market deterioration
–
September 16, 2008–December 31, 2008; macroeconomic
deterioration – January 1, 2009–March 31, 2009; and
stabilization and tentative signs of recovery – April 1, 2009 to
the end of
the sample period. Source: Federal Reserve Board of St. Louis
(2009) and the Bank for International Settlements (Filardo et
al., 2010).
Analysis 48 (2016) 376–387

crises, and second, the use of more inclusive data (e.g.,
aggregate data
indexes comprising the shadow banking industries of the
countries in-
cluded in the sample) that is currently unavailable.
Appendix A. Supplementary Appendix
Supplementary data to this article can be found online at
http://dx.
doi.org/10.1016/j.irfa.2016.01.005.
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http://refhub.elsevier.com/S1057-5219(16)00006-
5/rf0155Banking industry performance in the wake of the global
financial crisis1. Introduction2. Methodology3. Data4.
Empirical results4.1. Banking indexes in the 2002–2010
period4.2. Banking indexes in the global financial crisis in the
2007–2010 period5. Concluding remarksAppendix A.
Supplementary AppendixReferences

Electronic copy available at: http://ssrn.com/abstract=1983884
www.ccsenet.org/ijef International Journal of
Economics and Finance Vol. 4, No. 2; February 2012
Published by Canadian Center of Science and Education 31
Capital Regulation and Italian Banking System:
Theory and Empirical Evidence
Enzo Scannella
Department of Economics, Business and Finance, University of
Palermo
Viale delle Scienze, Edificio n. 13, Cap. 90128 Palermo, Italy
Tel: 39-091-2389-5305 E-mail: [email protected]
Received: October 26, 2011 Accepted: November 10, 2011
Published: February 1, 2012
doi:10.5539/ijef.v4n2p31 URL:

http://dx.doi.org/10.5539/ijef.v4n2p31
Abstract
This paper aims to investigate the role of capital for banking
institutions and provide an empirical analysis on large
Italian banks’ capital adequacy. The paper is organized as
follows. The first section introduces to the issue of the
paper. The second section explains why the capital is important
in the economics of banking firm. The paper
reviews the theoretical literature on bank capital regulation.
Empirical results on large Italian banks are reported on
the third section. The final section contains summary and
concluding comments.
Keywords: Bank capital, Regulation, Basel Accord, Capital
adequacy, Financial stability
1. Introduction
In the finance literature, the theoretical debate on bank capital
started with the Modigliani and Miller’s “irrelevance
proposition” (1958), the idea that corporate financing decisions
do not affect firm value under certain conditions. In

a perfect market, financing and investment decisions are
separable and independent activities. Financing structures
do not matter. They are not value relevant and financing
decisions do not affect investment decisions. In a
frictionless world of full information and complete markets, a
firm’s capital structure can not affect its value. When
we take into account the imperfections of the market – taxes,
transaction costs, asymmetry of information, bounded
rationality, costs of financial distress, imperfect competition –
our conclusions are quite different. The value of the
banking firm is dependent from its capital structure. Contrary to
Modigliani and Miller’s “irrelevance proposition”
banking firms do business with a higher financial leverage than
non banking firms. In banking industry the
debt/equity ratio is higher than other industries, in sharp
contrast to the implications of the Modigliani and Miller’s
proposition. So the irrelevance hypothesis of financial structure
is not applicable to banking firms. Such difference is
due to the unique role played by banking firms as financial
intermediaries. They transfer financial resources across
time and space. They are “asset transformer” (Gurley and Shaw,
1960) because they transform securities issued by
firms (bonds and shares) into securities demanded by investors
(bank deposits). They connect surplus units to deficit
units through the creation of financial assets and liabilities.

Banks are financial intermediaries that take in deposits,
which they then use to make loans and to invest in securities
and other financial assets.
Modern theories of finance have removed the perfect market
conditions and have highlighted the different role of
bank capital adequacy for the existence of financial
intermediaries (Santos, 2001). The incompleteness of financial
markets raises doubts on bank’s ability to determine an optimal
level of capital adequacy, and an optimal amount of
financial leverage. It increases the role of banking authorities to
impose a bank capital regulation and minimum
capital requirements. The form of these regulatory requirements
is getting more and more standardized across
countries and industries. Banks are subject to internationally
coordinated capital regulation. Banks are different from
other firms by the very nature of their activities. Therefore,
some kind of regulation and supervision is justified.
2. A Theoretical Perspective on Bank Capital Regulation
The bank capital regulation is one of the most prominent
aspects of banking regulation. It recognizes the strategic
importance of capital in bank management, coherently to the
Italian banking literature. In this research field Italian

scholars have played a role of forerunners. Caprara (1954) and
Dell’Amore (1969) proposed the double function of
bank capital: to support investments and to insure deposits.
Dell’Amore (1969) and Saraceno (1949) highlighted the
importance of capital to enhance the safety and soundness of
banks, and the need to link the bank capital adequacy
to bank risks. Also Bianchi (1969, 1981, 1991) suggested a
micro-economic perspective (note 1) – based on an
Electronic copy available at: http://ssrn.com/abstract=1983884
ISSN 1916-971X E-
ISSN 1916-9728 32
integrated framework of analysis – to study the role of capital in
the economics of banking firms. Based on this
starting point, the determinants of a bank’s capital structure are
related to the functions performed by banks:
financial intermediation, managing payment system, monetary
policy transmission, managing risks. Along these

research trajectories numerous Italian theoretical studies have
been published during the last decades (note 2).
Bank capital has an investment function (covering long-term
investment) and an insurance function (stabilizing the
financial and economic results of banks). These two aspects are
intertwined. Consequently they have a considerable
impact on bank soundness and stability, bank liquidity creation
and size of financial intermediation. Few examples
can clarify these relations. If bank capital is not adequate to
cover bank risk exposure it will increase the risk
premium to be paid to bank liabilities subscribers. It will have a
negative impact on expected economic
performances. Another example: The bank capital inadequacy
may force banks to reduce the amount of assets or
their riskiness. The adequate amount of bank capital represents
a prerequisite for a sound and prudent bank
management. It impacts on many aspects of banking activities,
such as bank growth and competitive dynamics of
banking industry, strategic decision processes, market
positioning, risk profiles of investments, assets and liabilities
structure, expected profitability, etc.
The adoption of bank capital standards at international level
focuses on risk measurement and definition of capital

standards for different types of risk exposure. Since the original
Basel Accord on capital standards (1988) the bank
regulation has widely proposed risk-based capital ratios both as
measures of the strength of banks and as trigger
devices for supervisors’ intervention. The Basel Accord
requires that banks meet a minimum capital ratio that must
be equal to at least 8% of total risk-weighted assets. In this
prudential supervisory and regulatory framework banks
can choose the risk/performance profile of their investments,
taking into account that the bank capital is a buffer to
absorb risks. Bank capital serves as a buffer that protects bank
deposits in case of losses on the asset side. Bank
capital is recognized by the regulation as investment and
insurance function. It represents a stabilization tool for the
single banking firm and for the entire financial system. It has a
“residual claim” nature because it absorbs losses
arising from bank’s risk exposures and reduces the probability
of bank bankruptcy.
The bank financial leverage is increasingly regulated through
the definition of an “adequate” level of capital in order
to avoid any risk exposure without sufficient bank capital. The
definition of a bank capital adequacy is made by an
outsider stakeholder, the banking authority, which sets risk and
capital measurements. The 1988 Basel Accord was

established with two fundamental objectives: to strengthen the
soundness and stability of the international banking
system and to obtain “a high degree of consistency in its
application to banks in different countries with a view to
diminishing an existing source of competitive inequality among
international banks” (Basel Committee on Banking
Supervision, 1988).
The capital adequacy issue has generated an academic and
professional debate in the search of the best institution –
between market and banking authority – to design the optimal
capital requirements for banks. Regulatory
requirements differ substantially from market-generated capital
requirements. Two different views are prominent on
this subject. The first one suggests the market is in a better
position to define the adequate level of bank capital and
provide a good mix of incentives to banks to better evaluate
risks. The market can determine a bank’s capital
requirement as the capital ratio that maximizes the value of the
bank in the absence of regulatory capital
requirements. This market requirement may differ for each
bank. It is affected by the market’s perceptions of the
risks taken by banks. This view criticizes the Basel Capital
Accord and any regulatory capital requirements.
Abandoning the perfect world of Modigliani and Miller may

help explain market capital requirements for banks.
The opposite one, otherwise, supports the idea that the bank
regulation and the banking authority is in a better
position to set adequate bank capital requirements and provide
incentives to correctly evaluate bank risks. Negative
externalities, systemic risk, agency problems, bank bankruptcy,
and costs of financial distress are the main
arguments in defense of the second view. Regulators require
capital to protect the safety and soundness of banks,
and the stability of the entire financial system. Financial
instability and negative externalities associated with
financial intermediation may inflict heavy social costs.
Concerns about these social costs lead regulators to require
bank capital ratios. However, capital regulation involves a
tradeoff between the marginal social benefit of reducing
the risk of the negative externalities from bank failures and the
marginal social cost of diminishing the quantity of
financial intermediation (Gorton and Winton; 1995; Santomero
and Watson, 1977).
From the market capital requirement perspective, equity capital
is the residual claim on the bank and it depends on
how banks’ assets and liabilities are valued by the market. In
order to reflect a market value of assets and liabilities

this perspective suggests the adoption of a market value
accounting system. On the contrary, from the regulatory
perspective, banks’ assets and liabilities are not perfectly
tradeable. They lack of an efficient secondary market.
Their value is affected by asymmetric information problems. In
addition, this perspective does not require a market
value measure of the bank capital.
Since the 1988 Basel Capital Accord the debate has focused on
the effectiveness and opportunities for a capital
adequacy regulation and its impacts on bank’s investment
portfolios. Kahane (1977), Koehn and Santomero (1980),
Kim and Santomero (1988) show that an increase of equity-to-
asset ratios may lead to an increase of risky assets in
the portfolio chosen by a bank. Banks may respond to binding
regulatory constraints on equity capital by increasing
their risk exposure. They may choose a portfolio with higher

risk and higher expected return.
Possible justifications for this are that bankers are risk-averse,
the access to equity market is expensive, agency costs
affect the relations between shareholders and management. In a
perspective of maximization of an utility function,
the introduction of minimum capital ratios may lead bankers to
maximize the return on investments, choosing an
higher risk-expected return frontier. Koehn and Santomero
(1980, p. 1235) assume that “for the system as a whole,
the results of a higher required capital-asset ratio in terms of
the average probability of failure are ambiguous, while
the intra-industry dispersion of the probability of failure
unambiguously increases. This result leads us to question
the viability of regulating commercial banks in terms of a
capital requirement”. Capital ratios may be ineffective
instruments to induce bankers to limit risk exposures in their
portfolio allocation. Other instruments, such as asset
restrictions, may be more effective to reduce the probability of
bank bankruptcy. Crouhy and Galai (1986, p. 239)
also show that “in competitive markets capital adequacy
requirements may be of no economic significance”.
Gennotte and Pyle (1991) assume that an increase in capital
requirements has an ambiguous effect on the probability

of failure when banks invest in low return asset portfolios, like
those characterized by high information asymmetry,
lending relationships, non-marketable loans, and a high degree
of competitiveness in the market. The induced
decrease of return on such asset portfolios may be outweighed
by investing funds in assets with high return and high
risk. Besanko and Kanatas (1996) also show that in the presence
of moral hazard problems in the portfolio allocation
and monitoring, an increase of capital standards may not induce
bank to lower its risk.
In contrast with the previous literature, other theoretical and
empirical studies (Furlong and Keeley, 1989; Keeley
and Furlong, 1990; Avery and Berger, 1991) find that capital
requirements lower bank risks when banks have
diversified asset portfolios. Unfortunately this result has not a
predictive value because it is not true all the time.
Rochet (1992) shows that the relation between capital
requirements and bank’s risk-taking is ambiguous. Blum
(1999) also highlights that the effects of capital requirements in
the economics of banking firms could be
counterintuitive when we shift from a static perspective of
analysis to a dynamic one. If it is expensive for the bank
to raise equity to meet higher capital requirements in the future,
an alternative could be to increase risk today. In

addition, other studies (Jacques and Nigro, 1997; Shrieves and
Dahl, 1992; Thakor, 1996) point out that the
introduction of capital requirements and risk measurement
standards have adverse effects on asset allocation and
asset substitution.
Over time, a vast literature has used the regulatory arbitrage
framework to evaluate the complex relations between
capital adequacy and bank investment decisions. The bank
capital regulation seems a form of external constraints
that creates incentives and opportunities for financial
innovation and regulatory capital arbitrage, with prominent
effects on the asset allocation and the growth of off-balance
sheet banking (Jagtiani, Saunders e Udell, 1995). The
introduction of a minimum equity-ratio is a form of regulatory
taxation that stimulates banks to find innovative ways
to allocate resources and minimize borrowing costs. The
regulatory capital arbitrage reduces the effectiveness of
capital regulation. Concerns have emerged about the Capital
Accord because financial innovation and regulatory
arbitrage may exploit the differences between the real level of
asset riskiness and the rule-based risk measurements
(Mottura, 2008). Bank capital regulation has provided a way to
reduce capital requirements by shifting from asset
categories with high risk weights to assets category with low

risk weights. This has created incentives to substitute
from loans to government bonds which are theoretically risk-
free, so they require no capital or a lower risk weight.
Such limitations have contributed to the growth of asset
securitization, and the contraction in bank lending (credit
crunch). Regulatory capital requirements gave incentives for
some banks to shrink their loan portfolios.
Notwithstanding, there is not much agreement regarding this
conclusion, leaving wide room for additional research.
The adoption of a regulatory framework that is based on bank
risk charges associated with each bank asset category
and standard risk measurements, has boosted regulatory
arbitrage practices. Regulatory measures of risk exposure
may be subject to manipulation by bank management in order to
reduce capital requirements without reducing the
actual risk exposure (Merton, 1995). The most common
arbitrage practices are cherry-picking, securitization, remote
origination, and indirect credit enhancements. Jones (2000, p.
42) specifies that “cherry-picking is perhaps the most
common of all regulatory capital arbitrage techniques. It
involves unbundling and repackaging a loan pool’s cash

ISSN 1916-971X E-
ISSN 1916-9728 34
flows through securitization or other means so that the vast bulk
of the credit risk is concentrated within financial
instruments having a much smaller capital charge”.
Growing evidence on these limitations prompted the
development of a new capital adequacy framework with
alternative approaches to bank asset category for setting capital
standards. It has overcome the previous
“one-size-fits all” approach and has adopted a more “firm-
specific” approach, based on the idea that banks have
better information on their risky assets than banking authorities.
The original Basel Accord is based on the use of
arbitrary weights that bear no relation to default rates. It also
assumes that all assets within one category are equally
risky.
Under the new approach, risk charges are not associated with
bank asset buckets but with the rating of each
borrower. It is an internal rating-based approach where banks

are able to estimate the probability of default for each
borrower. The new Capital Accord should align regulatory
capital requirements more closely with underlying risks
and to provide banks and their supervisors with several options
for the assessment of capital adequacy. The new
approach gives banks the incentive to distinguish riskier assets
from less risky assets in order to save on capital. But
this incentive-compatible scheme is not always effective. It
depends on the existence of information asymmetry and
opportunistic behaviour at the bank-authority relationship level,
and on an effective coordination among capital
adequacy standards and other regulatory instruments, such as
the deposit insurance scheme.
In brief, the New Basel Capital Accord incorporates some
positive aspects that are highlighted by the optimal bank
regulation literature, such as the banking authority’s monitoring
function, the designing of incentive-compatible
capital standards, the definition of risk buckets based on a risk
measure rather than on the asset category, the
implementation of a standardized approach and an internal
rating-based approach to better measure bank risk
exposure. The new capital adequacy framework is based on
three mutually reinforcing pillars: minimum capital
standards, a supervisory review, and a market discipline. In

June 2006, the Basel Committee released the final
proposal of the International Convergence of Capital
Measurement and Capital Standards. It does not change the
definition of regulatory capital. It deeply changes the capital
charge scheme, aiming at making capital charges more
correlated with the credit, market and operational risks of the
banking activity (table 1). The denominator of the
regulatory risk-based capital ratio measures the bank’s risk
exposure using a building block approach. The greater
the risk exposure, the higher bank capital must be. The New
Capital Accord introduces three options to measure
credit risk: a standardized approach, and two internal rating-
based approaches (foundation and advanced). With the
standardized approach, risk weights are determined by the
rating of the borrower, as defined by a rating agency.
With the internal rating-based approaches, banks are able to
internally evaluate the rating of the borrower. Risk
components of the two approaches are: probability of default,
loss given default, maturity, exposure at default. A
function to convert these risk components into risk weighting
and required capital standards is defined by the Basel
Committee.
The New Capital Accord incorporated also other types of risk.
Basel 2 adds a new charge for operational risk. The

denominator of the regulatory risk-based capital ratio focuses
not only on credit risk, but also on market and
operational risks. The new regulatory framework develops a
measurement of bank’s risk exposure that can be
uniformly applied across banks and countries. Unfortunately, as
suggested by Berger, Herring and Szego (1995, p.
418) “imperfections in setting the level of required capital and
the relative risk weights may lead to allocative
inefficiencies if capital requirements distort relative prices both
among banks and between banks and non-bank
competitors, and divert financial resources from their most
productive uses”.
The New Basel Capital Accord contains most of the elements
that potentially aim to improve the effectiveness of the
risk-based capital regulation. It defines the concept of capital
adequacy, making it possible to specify an explicit
criterion by which regulators and markets can judge whether
bank capital is adequate or not. Basel 2 allows some
banks to use their internal risk-measurement models to
determine capital requirements. It recognises the
market-based innovation in risk management practices. The new
risk weights, which are assigned to the different
asset classes, should be strictly related to the risk of the
underlying assets. It appears as a more risk-sensitive

framework that incorporates the innovations in the risk
management field. This is a necessary condition, albeit not a
sufficient one, for the well-functioning of the risk-based capital
requirements. As we pointed out above, the quality
of the capital regulation cannot be evaluated only with regard to
the sophistication and complexity of risk
measurement methodologies, but also taking into account the
behaviour and the objective functions of different
stakeholders. This is an important area for future research. We
lack clear evidence about such connection.
The importance of the first condition (the availability and
implementation of statistically and mathematically
well-founded methodologies) has recently emerged with the
financial crisis. Shortcomings have emerged on risk
measurement and evaluation of the following items:
- Structured financial instruments, illiquid assets, and complex

credit securitization;
- Credit exposures to off-balance sheet vehicles;
- Systemic risk of asset portfolios;
- Liquidity risk, concentration risk, and reputation risk.
These shortcomings have produced some useful insight to
redesign them in the next International Capital Accord
(Basel 3). In order to fully evaluate the quality of the risk-based
capital regulation we should take into account many
other aspects, as follows:
- The bank management’s objective function;
- The effectiveness of incentive-compatible schemes to prompt
bankers to correctly measure risks;
- The bank governance system and its relations with risk control
and capital allocation;
- The management remuneration system and incentive schemes,
and the orientation of management to pursue
short-term or long-term performance;

- The ability of banking authorities to reduce information
asymmetry and agency costs in regulator-bank relationship.
It is due to the opaque nature of banks to outsiders;
- The ability of banking authorities to measure and evaluate the
entire bank risk exposure, control bank risk-taking,
evaluate the adequacy and reliability of internal risk
measurements. The risk exposure measurements may be subject
to significant manipulation by bank management. Regulators
may access to confidential bank information;
- The ability of banking authorities to apply sanctions to banks
for capital regulation violations;
- The ability of banking authorities to encourage banks to
disclose information in order to enhance the role of market
participants in monitoring banks.
Some of these issues are consistent with the supervisory review
process (pillar 2 of the New Basel Capital Accord)
that intendeds to ensure that a bank’s capital position is
consistent with its overall risk profile and to enable early
intervention. The quality of bank structures and processes, that
are in charge of risk measurement and management,

are as important as the capital adequacy.
The recent financial crisis has also highlighted some
shortcomings in the above mentioned qualitative aspects of
capital regulation. Also the recent Basel Committee’s
Enhancing Corporate Governance for Banking Organizations
(2006), and the Financial Stability Forum’s Enhancing Market
and Institutional Resilience (2008) have put under
pressure some shortcomings in bank risk management and
control. The Financial Stability Forum proposes concrete
actions in the following areas: strengthened prudential oversight
of capital, liquidity and risk management;
enhancing transparency and valuation; changes in the role and
uses of credit ratings; strengthening the authorities’
responsiveness to risks; robust arrangements for dealing with
stress in the financial system. It implies a wider
governance view in the risk management field and shows that
capital adequacy has a strategic relevance in bank
management (Sironi and Resti, 2008). Regulators and
supervisory authorities, at national and international level,
play an important role in pursuing a level playing field among
financial intermediaries.
The recent financial crisis brings a new banking regulation to
overcome the previous mentioned shortcomings

(Acharya and Richardson, 2009; Adrian and Shin, 2010). The
theoretical studies developed during the last decades
on capital regulation provide a useful methodological
framework to improve the current banking regulation
(Birindelli, Ferretti, and Tutino, 2011). Unfortunately the
pursuit of an optimal capital structure in the economics of
banking firms remains an open question. Whether the amount of
capital required by the regulators is greater than
that required by the financial market, and whether regulators are
very effective in controlling banks’ capital
adequacy, are fundamental theoretical and empirical questions.
The bank capital adequacy has been becoming a global issue in
relation to the rapid transformation of financial and
banking industries. The elimination of geographic restrictions,
the revolution in information technology, and the
huge number of mergers and acquisitions has created large and
complex financial institutions with a global character.
It has increasingly led to overcome a prudential regulation and
supervision based on national borders and national
supervisory authorities (Gualandri, 2008). An international
capital standard that prevents regulatory competition
among countries is the complex tool that many developed
countries have chosen to pursue the global financial
stability. Unfortunately, in some cases the Basel Accord has

moved banking systems in the opposite direction.
3. An Empirical Analysis of Large Italian Banks’ Capital
Adequacy
In this section on the paper I will conduct an empirical analysis
(using Bankscope data) on Italian banks’ capital
adequacy in comparison with large European banks. I will also
examine some of the implications of the theoretical
ISSN 1916-971X E-
ISSN 1916-9728 36
analysis. Changes in capital ratios are investigated because that
is what is examined in capital regulation studies.
First of all I will focus the analysis on the major Italian banks
with reference to the amount of total assets on their
balance sheet (Figure 1). This analysis focuses on large banks
because they are more likely to be subject to market
discipline. The total assets ratio may be a better measure of

scale.
In order to correctly evaluate the capital adequacy we need to
distinguish the different quality of each component of
bank regulatory capital. The regulatory capital is split into
different layers as follows:
- Tier 1 Capital
It is composed of common stock, retained earnings, and
innovative capital instruments that are subject to Basel
Committee’s stringent conditions. The core tier 1 capital is
composed of common stock and retained earnings only;
- Tier 2 Capital
It is composed of revaluation reserves, subordinated debt,
undisclosed reserves, and hybrid debt/capital financial
instruments. It is also called “supplementary capital”;
- Tier 3Capital
It is composed of short-term subordinated debt that is not
included in the tier 2 capital.

International Review of Financial Analysis 48 (2016) 376–387.docx

International Review of Financial Analysis 48 (2016) 376–387.docx

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International Review of Financial Analysis 48 (2016) 376–387.docx