Available via license: CC BY 3.0
Content may be subject to copyright.
Banks and Bank Systems, Volume 11, Issue 4, 2016
152
Cândida Ferreira (Portugal)
European and global financial integration: some panel evidence
of the convergence of interest rates
Abstract
This paper seeks to contribute to the literature on financial integration using panel estimates to test beta- and sigma-
convergence across the European Union countries’ interest rates and towards two specific benchmarks — the German
and US rates — covering the time interval between 1999 and 2014 and taking into account the recent international
financial crisis. The findings point to the existence of a process of convergence of interest rates and this process may be
considered as part of the global process of integration. Furthermore, there is evidence of convergence to the chosen
benchmarks, in particular of short-term real interest rates; the speed of this convergence towards the German rates
clearly increased in the EU as a response to the financial crisis.
Keywords: financial integration, banking market, European interest rates, beta-convergence, sigma-convergence, panel
data estimates.
JEL Classification: C2, E4, F3, G1, G2.
Introduction
Since the 1970s, and particularly after the collapse
of the Bretton Woods system, followed by the first
acute, deep oil crisis, there has been a global trend
of reducing the barriers to free international trade
and of establishing a clear process of international
financial liberalization (Obstfeld, 1998; Rose and
Van Wincoop, 2001; Baier and Bergstrand, 2007;
De Nicolò and Juvenal, 2012).
Simultaneously, in Europe, a remarkable process of
integration has taken place mostly with the aim of
guaranteeing the stability and security of the continent.
This process started with the common undertaking of
six countries and has evolved into the European Union
(EU), at present incorporating 28 member states. The
establishment of the Economic and Monetary Union
(EMU) in 1999 was supposed to accelerate the process
of economic consolidation and financial integration
both between the countries in the euro area and across
the EU as a whole (Cabral et al., 2002; Hartman et al.,
2003; Sørensen and Gutiérrez, 2006; Jappelli and Pa-
gano, 2008, Arghyrou et al., 2009).
Under these conditions, the EU countries have been
subject to different types of integration: an interna-
tional form, following the general process of globaliza-
tion, and the specific processes of European integration
not only into the EU, but also into the EMU.
However, there is no clear consensus on evidence of
the increasing consolidation and integration of Euro-
pean markets. Some empirical studies have even con-
cluded that European financial markets are far from
integrated (Centeno and Mello, 1999; Gardener et al.,
2002; Affinito and Farabullini, 2009; Gropp and Ka-
Cândida Ferreira, 2016.
Cândida Ferreira, Professor, ISEG, UL – Lisbon School of Economics
and Management of the Universidade de Lisboa and UECE - Research
Unit on Complexity and Economics, Portugal.
Financial support by FCT (Fundação para a Ciência e a Tecnologia),
Portugal is gratefully acknowledged. This article is part of the Strategic
Project (UID/ECO/00436/2013).
shyap, 2009). For different EMU members, Hartmann
et al. (2003) found no support for the argument that
financial integration leads to convergence in financial
structures. Baele et al. (2004) considered five key eu-
roarea markets (money, government bonds, corporate
bonds, banking/credit, and equity markets) and con-
cluded that these distinct market sectors have attained
different levels of integration.
With regard to interest rates in the EU countries, seve-
ral empirical studies, mainly using harmonized statis-
tics such as the ‘IMF interest rates’ available since
January 2003, have shown divergences in the level of
interest rates across member states (Martin-Oliver et
al., 2005; European Central Bank, 2007, 2008; Affinito
and Farabullini, 2009).
Kleimeier and Sander (2000), using cointegration
analysis, provided evidence of the degree of integra-
tion of the interest rates in six core EU countries, Ja-
pan, and the US for the 1985–1997 period, concluding
that there was a convergence process, particularly with
respect to spreads, but only at the regional level rather
than as a global phenomenon. They also concluded
that European lending rates were not yet fully inte-
grated and noted that the segmentation of European
financial markets posed an additional challenge to the
implementation of a unified monetary policy.
Arghyrou et al. (2009) tested the convergence of
real rates in the EU, using the EMU average as a
benchmark. Following the methodology proposed,
among others, by Ferreira and León-Ledesma
(2007), they applied an augmented Dickey–Fuller
test and, using monthly rates provided by Data-
Stream for the 1996–2005 period, obtained empiri-
cal evidence of convergence.
To our knowledge, not many papers have addressed
the controversial issue of financial integration across
the EU member states, comparing it with global finan-
cial integration and taking into account the possible
influence of the recent international crisis.
Banks and Bank Systems, Volume 11, Issue 4, 2016
153
This paper aims to contribute to the literature on finan-
cial integration, providing empirical evidence of the
convergence of interest rates across the EU member
states during the 1999–2014 period and comparing
these results with the convergence of the interest rates
of other developed European and non-European coun-
tries. By borrowing the concepts of beta- and sigma-
convergence from the economic growth literature and
adopting panel estimates and the methodology pro-
posed, among others, by Baele et al. (2004), we aim to
answer to three specific questions:
Is there a clear process of interest rate conver-
gence across the EU countries and is it more
evident than the global process of convergence
across some relevant European and
non-European developed countries?
Is it possible to identify different patterns and
speeds of approximation of EU and non-EU
countries’ rates towards the German and US in-
terest rates?
Did the recent international financial crisis pro-
voke the same kind of reactions in interest rate
convergence across EU and non-EU countries?
The remainder of this paper is structured as follows.
The next section presents the data used and the adopted
methodology. Section 2 reports the obtained empirical
results and final section summarizes and concludes.
1. Data and methodology
1.1. Data. The use of the available AMECO series is a
guarantee of the compatibility of all data. We select the
series of nominal and real (using both the private con-
sumption and GDP deflators) long-term and short-term
interest rates and yield curves. These allow us to com-
pare the evolution of the degree of integration between
all current EU member states and some developed
non-EU countries.
With these series, we calculate, for each of the selected
countries, the differences between the country’s rate
and the two chosen benchmarks: Germany’s rate and
US’s rate1. Thus, for all EU countries included in our
panels, we consider the following variables:
1. ILN = Nominal long-term interest rate
2. ILRC = Real long-term interest rates, using the
private consumption deflator
3. ILRV = Real long-term interest rates, using the
GDP deflator
4. ISN = Nominal short-term interest rates
5. ISRC = Real short-term interest rates, using the
private consumption deflator
1Except for the individual series of these two countries, where in all
situations, we must consider the differences in the other benchmark’s
rates. Thus, for Germany, we always take account of the difference
between German rates and US rates, and vice versa.
6. ISRV = Real short-term interest rates, using the
GDP deflator
7. IYN = Yield curve (= ILN − ISN)
8. ILNGermany = (ILN)i− (ILN)Germany
9. ILRCGermany =(ILRC)i− (ILRC)Germany
10. ILRVGermany =(ILRV)i− (ILRV)Germany
11. ISNGermany = (ISN)i− (ISN)Germany
12. ISRCGermany = (ISRC)i− (ISRC)Germany
13. ISRVGermany = (ISRV)i− (ISRV)Germany
14. IYNGermany = (IYN)i− (IYN)Germany
15. ILNUS= (ILN)i− (ILN)US
16. ILRCUS= (ILRC)i− (ILRC)US
17. ILRVUS= (ILRV)i− (ILRV)US
18. ISNUS= (ISN)i− (ISN)US
19. ISRCUS= (ISRC)i− (ISRC)US
20. ISRVUS= (ISRV)i− (ISRV)US
21. IYNUS= (IYN)i− (IYN)US
Using the available data, we consider the following
panels of countries:
Panel 1: including 35 developed countries,
namely all currently EU member states and
other European and non-European developed
countries considered in the AMECO database
(Iceland, Turkey, Norway, Switzerland, the
United States, Japan, and Canada). Panel 1-A
considers the years before the recent interna-
tional financial crisis (1999–2008) and Panel 1-
B enlarges the time period, considering the
1999–2014 period.
Panel 2: only including the 28 EU countries.
Again, Panel 2-A is only for the years before the
recent international financial crisis (1999–2008),
while Panel 2-C is for the 1999–2014 period.
1.2. Beta-convergence estimations. In the economic
growth literature, typical convergence studies consider
the regression of the average per capita GDP growth
rate of regions or countries with different income lev-
els and the initial level of their income. There is con-
vergence if the value of the regression coefficient (usu-
ally represented by β) is negative; this indicates that
poor regions or countries are growing faster than rich
ones, and, in turn, are becoming more homogeneous in
their per capita incomes (see, among others, Barro and
Sala-i-Martin, 1992; Mankiw et al., 1992;
Sala-i-Martin, 1996).
Beta-convergence models have been borrowed from
the literature on economic growth and adapted to
measure the progress of financial integration, among
others by Adam et al. (2002), Baele et al. (2004), and
the European Central Bank (2007, 2008).
Using panel data, here we also borrow from this litera-
ture and opt to estimate the following equation:
, )()( ,2,1,1,1,, titititititi rrrrr
(1)
Banks and Bank Systems, Volume 11, Issue 4, 2016
154
where r represents interest rate, i and t denote the
country and time indices, respectively, α is the inde-
pendent term, and εi,t is the error term, denoting the
exogenous shocks that force the interest rate differ-
entials between the considered countries.
The presence of negative betas signals convergence to
the averages of the series, and the magnitude of the
betas denotes the speed of this convergence.
1.3. Sigma-convergence estimations. From economic
growth literature, we can also borrow the widely used
concept of sigma-convergence, which typically meas-
ures the dispersion of the per capita income across a
group of regions or countries. Sala-i-Martin (1994,
1996) defends beta-convergence as a more interesting
measure of convergence and demonstrates that beta-
convergence is a necessary, but not a sufficient condi-
tion for sigma-convergence. Nevertheless, beta- and
sigma-convergence are usually considered as comple-
mentary measures and are used together in many em-
pirical convergence studies. The main idea behind
sigma-convergence is that all regions or countries con-
verge to the same level of economic output and sigma-
convergence can be defined as the lowering of vari-
ance of real GDP per capita among these regions or
countries, representing the catching-up effect during
the considered time period.
As in growth studies, financial integration literature
considers that sigma-convergence occurs when the
cross-sectional standard deviation of a variable, such as
an interest rate, is decreasing over time (see, for in-
stance, Adam et al., 2002; Baele et al., 2004; Casu and
Girardone, 2009).
In this paper, we want to test the convergence of
the interest rate series to the chosen benchmarks
(either German or US interest rates) to estimate
the following model:
(2)
,1,1,, titititi rrr
where ti
r,
represents the difference between the
country’s i interest rate and the correspondent
benchmark’s rate at moment t, 1,
ti
rdenotes the
difference between the country’s i interest rate and
the correspondent benchmark’s rate at the moment t−
1,
is the independent term, and
i,t is the error term.
The value of the sigma is supposed to be negative
and represents the rate of convergence towards the
correspondent benchmark; so, the larger the absolute
value of sigma is, the faster the cross-sectional con-
vergence to the chosen benchmark (either the Ger-
man or the US rates) will be.
1.4. Testing cointegration. Here cointegration is
tested with the implementation of the four panel
tests developed by Westerlund (2007) and Wester-
lund and Edgerton (2007), which test for the absence
of cointegration by determining whether individual
panel members are error correcting. These tests are
flexible, work well in unbalanced, heterogeneous,
and/or relatively small panels and allow for depen-
dence both between and within cross-panel units.
The application of these panel cointegration tests to
the i series included in one panel considers, for each
moment t (during the time interval t = 0,…,p), the
following errorcorrection model: Drit= ci+ai1* Drit −1 +
+…+aip *Drit −p+ bi0*Dxit+ bi1 *xit − 1 + …+ bip*Dxit −p +
+ai(rit − 1− bi *xit − 1) + uit.
These Westerlund cointegration tests provide four
test statistics: Gt, Ga, Pt, and Pa.
The Gt and Ga statistics test H0: ai = 0 for all i ver-
sus H1: ai< 0 for at least one of the series, i, starting
from a weighted average of the individually esti-
mated coefficients ai and their respective t-ratios.
The Pt and Pa test statistics consider the pooled
information of all panel cross-section units to test
H0: ai = 0 for all i versus H1: ai< 0 for all cross-
section units. Thus, the rejection of H0 must always
be taken as the rejection of cointegration for the
whole panel. Any single cross-unit can cause the
rejection of H0 and it is not possible to identify
which cross-unit is responsible for this rejection.
2. Empirical results
In this section, we present the results obtained for the
defined panels.
For Panel 1, which includes the 35 developed coun-
tries, we consider first Panel 1-A with 350 observa-
tions for the years before the recent crisis, 1999–
2008, and second Panel 1-B with 560 observations
for the entire 1999–2014 period.
For Panel 2, where we include only the 28 EU member
states, we also have Panel 2-A, with 280 observations,
for the 1999–2008 period, and Panel 2-B with 448
observations for the 1999–2014 period.
Here, we report first the results obtained for the
beta-convergence estimations with equation (1),
then those obtained for the sigma-convergence esti-
mations with equation (2), and, finally, we comple-
ment our analysis with the results obtained with the
Westerlund cointegration test.
2.1. Results obtained with the beta-convergence
estimations. Table 1 provides the results obtained for
beta-convergence with the estimation of equation (1)
using panel fixed effects estimates, which Hausman
tests revealed to be more adequate than random effects
estimates1. Panel fixed effects estimations assume that
the individual country-specific effects are random
1The results obtained with panel random effects estimates and the Hausman
tests are not presented in this paper, but can be provided on request.
Banks and Bank Systems, Volume 11, Issue 4, 2016
155
variables that are allowed to be correlated with the
explanatory variables; these estimates are clearly ade-
quate when we are only interested in analyzing the
impact of variables that vary over time, as with the
considered series of interest rates.
(Table 1 Аround here)
From the results reported in Table 1, we can see that
the statistical significance of the results for the coeffi-
cients and the independent term are, in general, ac-
ceptable, especially for the beta coefficients. More-
over, in all situations, the betas are negative and sta-
tistically very significant, indicating the clear pres-
ence of convergence in the considered interest rate
series across the countries included in the panels.
The comparison of the results obtained for the two
panels of countries allows us to conclude that not only
beta-convergence is a reality among the EU member
states’ interest rates included in Panel 2, but also, and
sometimes even with a higher speed of convergence,
among the interest rates of these countries and the
other developed countries included in Panel 1.
The results obtained for the long-term and the short-
term series reveal that, in general, and in both panels
of countries, the magnitude of the betas is higher in
the short term than in the long term. This tendency is
also confirmed by the statistically significant yet not
very strong speed of convergence of the ‘yield curve’,
which, according to the definition of the AMECO
database, is the difference between the nominal long-
term and the nominal short-term interest rate series.
In addition, for both panels, the comparison of the
results obtained for the time period before the recent
financial crisis (1999–2008) and those including the
years of the crisis (1999–2014) clearly show the
relevance of this financial crisis for the interest rates
series’ convergence in the considered panels of
countries. With the exception of the results obtained
for the nominal short-term interest rate series, par-
ticularly for Panel 1, all the other results reveal
higher betas in the panels including the years of the
crisis (Panel 1-B and Panel 2-B).
These results allow us to conclude that, in general,
for the universe of the considered EU and non-EU
developed countries, in spite of the recognized het-
erogeneities and different individual consequences
of the crisis for each country, after 2008, there was a
remarkable increase in the speed of convergence of
the countries’ interest rate series.
2.2. Results obtained with the sigma-convergence
estimations. In Table 2, we present the results ob-
tained with the estimation of equation (2), which
tests the sigma-convergence, that is, the cross-
section dispersion and the possible convergence of
the interest rate series to the chosen benchmarks
(here either the German or the US rates). We go on
following the indications of the Hausman test, which
recommends the results obtained with panel fixed
effects estimations, assuming that the individual
country-specific effect is correlated with the inde-
pendent variable.
(Table 2 Аround here)
The results provided in Table 2 reveal that there is
clear cross-sectional convergence towards the
benchmarks, as the obtained sigma coefficients are
always negative and statistically very significant.
With regard to the speed of convergence to the
benchmarks (the absolute values of the sigma), they
have similar patterns in both panels, but, in general,
they are slightly higher in Panel 1 than in Panel 2,
revealing that there is evidence of convergence to
the considered benchmarks not only among all the
EU member states, but also, and with more intensity,
among these and the other developed countries in-
cluded in Panel 1.
In all situations, and in line with the previous re-
sults obtained with the beta-convergence estima-
tions, the speed of the sigma-convergence is al-
ways higher for the short-term than for the long-
term interest rate series.
In both panels, the real interest rate series (both us-
ing the private consumption and the GDP deflators)
almost always converge faster to the benchmarks
than the nominal rates, and this is true both for the
long-term and the short-term rates.
Moreover, for the long-term interest rates in the two
panels and for the short-term interest rates in Panel 1
only, the speeds of convergence towards the bench-
marks are higher during the years before the recent
crisis (Panel 1-A for the 1999–2008 period) than
when we also include the years of the crisis (Panel
1-B for the 1999–2014 period).
The results obtained for the short-term interest rates
in Panel 2, which only includes the 28 EU member
states, reveal, on the one hand, that the nominal rate
(which is often considered to be a proxy for the
monetary policy rate) continues converging at simi-
lar speeds before and after the crisis, while, on the
other hand, the real interest rates series, both consid-
ering the private consumption and the GDP defla-
tors, clearly show in Panel 2-B the increasing con-
vergence of the EU real short-term interest rates,
particularly to the German rates, reflecting the ef-
forts of the EU economies to overcome the problems
provoked by the financial crisis.
In what concerns specifically to the convergence
towards the two benchmarks, there are clear differ-
ences in the speeds of adjustment across interest rate
series and time periods.
Banks and Bank Systems, Volume 11, Issue 4, 2016
156
For the years before the crisis (1999–2008), there is
clear evidence that in both panels, almost all interest
rate series converge with a higher speed of adjust-
ment towards the German rates; the exception is the
nominal short-term interest rate, which slightly con-
verges more towards the US rate.
The situation changes after the crisis, as for the time
period including the years of the crisis (1999–2014),
and again in both panels, only the real short-term rates
(both considering the private consumption and the
GDP deflators) continue to converge towards the Ger-
man rates with higher speed than towards the US rates.
However, for the real long-term rates the speed of
adjustment is clearly higher towards the US rates.
2.3. Results obtained with the Westerlund cointe-
gration test. In order to complement our previous
analysis, we present in Table 3 the p-values obtained
with the Westerlund cointegration test for each of
the considered panels of countries and time periods
(the values of the statistics and the Z-values are also
available and will be provided on request).
(Table 3 Аround here)
The cointegration results presented in Table 3 reveal
that there are no main differences across the considered
panels of countries. In all situations, the results ob-
tained for the considered universe of 35 developed
countries in Panel 1 are in line with the results of Panel
2, where we included only the EU member states.
According to these results, the cointegration of the
individual countries’ rates and the German rates cannot
be rejected for the majority of the series. The excep-
tions are to be found mostly for the nominal long-term
rates, but only for the time period including the years
of the crisis (1999–2014), and also for the yield rates
(which represent the difference between the long-term
and the short-term nominal rates), but only for the
years before the crisis (1999–2014).
In what concerns the cointegration with the US
rates, the results for the yield rates are completely in
line with those obtained for the cointegration with
the German rates. In both panels of countries, coin-
tegration cannot be rejected for all considered short-
term rates, but this is not so clear for the long-term
rates. A more detailed observation of the results
obtained for the long-term rates allows us to con-
clude that in almost all situations cointegration can
clearly be rejected for the nominal rates and for the
real rates using the private consumption deflator, but
only before the financial crisis (the 1999–2008 pe-
riod). In addition, for the other long-term real series
cointegration cannot be completely rejected, particu-
larly taking into account the results of the Pt and Pa
statistics, which consider the pooled information of
all panel cross-section units (here, the included de-
veloped EU and non-EU countries).
Summary and conclusions
This paper contributes to the literature by using pa-
nel data estimations to test beta- and sig-
ma-convergence and also cointegration across the
EU countries’ interest rates and the speeds of their
convergence towards two specific benchmarks:
German and US interest rates.
The data are sourced from AMECO and include the
available series of both nominal and real long-term
and short-term interest rates covering the 1961–2008
period. The analysis considers two panels of EU
countries: one including all the countries for which
AMECO provides the used interest rate series and
another with all the current EU member states. In
both situations, the possible influences of the recent
global financial and European debt crisis are taken
into account.
The empirical findings allow us to conclude that:
1. There is a clear process of European integration,
namely in what concerns the convergence of in-
terest rates, which may be considered as part of
the global process of integration. More precise-
ly, there is clear evidence of beta-convergence
not only among the interest rates of the 28 EU
member states, but also among the interest rates
of the EU and non-EU developed countries that
we considered in our analysis.
This conclusion is reinforced by the cointegration
test results, as in all reported situations, the values
obtained for Panel 1 with 35 developed countries
are completely in line with those obtained for Pan-
el 2, where we include only the 28 EU countries.
2. It is possible to identify different speeds of con-
vergence towards the benchmark rates of differ-
ent series of interest rates in the two panels of
countries. In general, in both panels, the sigma-
convergence is higher for the short-term than for
the long-term interest rates. However, it is not
evident that the interest rates of the EU countries
always converge more to Germany’s or to the
US’s rates. In addition, the cointegration tests do
not reveal clear differences in the possible exis-
tence of cointegration between each of the inter-
est rate series and the two considered bench-
marks (the German and the US rates).
Considering the years before the recent interna-
tional financial crisis (1999–2008), the results
reveal that in our two panels of developed coun-
tries (the first with EU and non-EU countries
and the second with only the 28 EU members-
tates), almost all interest rate series converge
with a higher speed of adjustment towards Ger-
many’s rates, with the exception of the nominal
short-term interest rate, which slightly con-
verges more towards the US’s rates.
Banks and Bank Systems, Volume 11, Issue 4, 2016
157
On the other hand, when we include the years after
the crisis, that is, considering the 1999–2014 pe-
riod, and again in both panels of countries, only
the real short-term rates continue to converge to-
wards Germany’ rates with a higher speed than
towards the US’ rates. However, with regard to the
real long-term rates, the speed of adjustment is
clearly stronger towards the US’s rates.
3. Furthermore, the results obtained not only allow us
to conclude about the importance of the recent in-
ternational crisis for the interest rates’ conver-
gence, but also reveal that the speed of this con-
vergence clearly increased after the crisis not only
in the EU, but also in the other non-EU developed
countries included in our analysis.
Further research is needed to analyze the com-
plex process of financial integration and to clari-
fy the factors that affect economic agents, both
on the supply and on the demand sides of finan-
cial markets. This is particularly relevant at a
time when the world is still undergoing a deep
economic and financial crisis that has affected
market structures and showed the heterogeneity
across EU and non-EU countries with remarka-
ble differences in their robustness in dealing
with financial turbulence.
References
1. Adam, K., Jappelli, T., Menichini, A.M., Padula, M. and Pagano, M. (2002). Analyse, Compare, and Apply Alter-
native Indicators and Monitoring Methodologies to Measure the Evolution of Capital Market Integration in the
European Union, Centre for Studies in Economics and Finance (CSEF), University of Salerno.
2. Affinito, M. and Farabullini, F. (2009). Does the law of one price hold in euro-area retail banking?An empirical analysis
of interest rate differentials across the monetary union, International Journal of Central Banking, Vol. 5 (1), pp. 5-37.
3. Arghyrou, M.G., Gregoriou, A., and Kontonikas, A. (2009). Do real interest rates converge? Evidence from the
European union, Journal of International Financial Markets, Institutions & Money, Vol. 19 (3), pp. 447-460.
4. Baele, L., Ferrando, A., Hördahl, P., Krylova, E. and Monnet, C. (2004). Measuring European financial integra-
tion, Oxford Review of Economic Policy, Vol. 20 (4), pp. 509-530.
5. Baier, S.L. and Bergstrand, J.H. (2007). Do free trade agreements currently increase members’ international trade?
Journal of International Economics, Vol. 71 (1), pp. 72-95.
6. Barro, R.J. and Sala-i-Martin, X. (1992). Convergence, Journal of Political Economy, Vol. 100 (2), pp. 223-251.
7. Cabral I., Dierick, F. and Vesala, J. (2002). Banking Integration in the Euro Area, ECB Occasional Paper Series No. 6.
8. Casu, B. and Girardone, C. (2009). Integration and efficiency in EU banking markets, in M. Balling, E.Gnan,
F.Lierman, and J.-P.Schoder (Eds.), Productivity in the Financial Services Sector, pp. 89-110. SUERF.
9. Centeno, M. and Mello, A.S. (1999). How integrated are the money market and bank loans market within the Eu-
ropean Union? Journal of International Money and Finance, Vol. 18 (1), pp. 75-106.
10. De Nicolò, G. and Juvenal, L. (2012). Financial Integration, Globalization, and Real Activity, CESifo Working
Paper No. 3737.
11. European Central Bank. (2007). Financial Integration in Europe.
12. European Central Bank. (2008). Financial Integration in Europe.
13. Ferreira, A.L. and León-Ledesma, M.A. (2007). Does the real interest parity hypothesis hold? Evidence from de-
veloped and emerging markets, Journal of International Money and Finance, Vol. 26 (3), pp. 364-382.
14. Gardener, E.P.M., Molyneux, P. and Moore, B. (Eds.). (2002). Banking in the New Europe – The Impact of the
Single Market Programme and EMU on the European Banking Sector. Palgrave Macmillan.
15. Gropp, R. and A. Kashyap. (2009). A New Metric for Banking Integration in Europe, NBER Working Paper No. 14735.
16. Hartmann P., Maddaloni, A. and Manganelli, S. (2003). The euro-area financial system: Structure, integration, and
policy initiatives, Oxford Review of Economic Policy, Vol. 19 (1), pp. 180-213.
17. Jappelli, T. and M. Pagano. (2008). Financial Market Integration under EMU, CSER Working Paper No. 197.
18. Kleimeier, S. and Sander, H. (2000). Regionalisation versus globalisation in European financial market integra-
tion: Evidence from co-integration analysis, Journal of Banking & Finance, Vol. 24 (6), pp. 1005-1043.
19. Mankiw, N.G., Romer, D. and Weil, D.N. (1992). A contribution to the empirics of economic growth, The Quar-
terly Journal of Economics, Vol. 107 (2), pp. 407-437.
20. Martin-Oliver, A., Salas-Fumás, V. and Saurina, J. (2005). A Test of the Law of One Price in Retail Banking, Ban-
co de EspañaWorking Paper, No. 0530.
21. Obstfeld, M. (1998). The global capital market: Benefactor or menace? The Journal of Economic Perspectives,
Vol. 12 (4), pp. 9-30.
22. Rose, A.K., and E. van Wincoop. (2001). National money as a barrier to international trade: The real case for cur-
rency union, American Economic Review, Vol. 91 (2), pp. 386-390.
23. Sala-i-Martin, X. (1994). Cross-sectional regressions and the empirics of economic growth, European Economic
Review, Vol. 38 (3-4), pp.739-747.
24. Sala-i-Martin, X.X. (1996). Regional cohesion: Evidence and theories of regional growth and convergence, Euro-
pean Economic Review, Vol. 40 (6), pp. 1325-1352.
25. Sørensen, C.K. and Gutiérrez, J.M.P. (2006). Euro Area Banking Sector Integration: Using Hierarchical Cluster
Analysis Techniques, ECB Working Paper No. 627.
26. Westerlund, J. (2007). Testing for error correction in panel data, Oxford Bulletin of Economics and Statistics, Vol.
69 (6), pp. 709-748.
27. Westerlund, J. and Edgerton, D.L. (2007). A panel bootstrap cointegration test, Economics Letters, Vol. 97 (3), pp. 185-190.
Banks and Bank Systems, Volume 11, Issue 4, 2016
158
Appendix
Table 1. Beta convergence results
PANEL 1- 35 developed countries (1) PANEL 2 – 28 EU countries.
Variables (2) P1-
A
: 1999
–
2008 P1-B: 1999
–
2014
P2-
A
: 1999
–
2008
P2-B: 1999
–
2014
Dep. Var.: ILN
Coef. 2.0058 -0.3868
0.0517 1.9011
-0.4052
0.1874
2.1134
-0.4232
0.1617 2.0088 -0.4352
0.2168
z-stat. 7.71 -8.14
1.45 9.42
-10.58
5.18
8.03
-8.56
3.45
8.70 -9.99 5.23
P > z 0.000 0.000
0.149 0.000
0.000
0.000
0.000
0.000
0.001 0.000 0.000 0.000
Dep. Var.: ILRC
Coef. 0.5508 -0.3512
-0.1087 1.2587
-0.5502
0.0531
0.3870
-0.3215
-0.0772 1.2500 -0.5567
0.0714
t-stat. 3.17 -5.30
-2.07 8.65
-11.73
1.21
2.30
-4.68
-1.33 7.69 -10.69
1.45
P > t 0.002 0.000
0.039 0.000
0.000
0.227
0.023
0.000
0.185 0.000 0.000 0.147
Dep. Var.: ILRV
Coef. 0.6993 -0.2824
-0.2953 1.2244
-0.5861
0.0922
0.3649
-0.3454
0.0173 1.1597 -0.5614
0.1617
t-stat. 3.96 -7.54
-10.21 7.95
-12.33
2.09
2.58
-5.71
0.22
7.05 -11.30
3.32
P > t 0.000 0.000
0.000 0.000
0.000
0.037
0.010
0.000
0.783 0.000 0.000 0.001
Dep. Var.: ISN
Coef. 1.1344 -0.7611
- 0.2045 0.6009
-0.2609
-0.2667
1.4986
-0.3564
-
0.0421 0.7477 -0.3162
-0.0336
t-stat. 6.03 -10.68
-4.48 3.60
-10.88
-12.71
10.15
-15.86
-2.22 6.86 -16.27
-1.88
P > t 0.000 0.000
0.000 0.000
0.000
0.000
0.000
0.000
0.027 0.000 0.000 0.061
Dep. Var.: ISRC
Coef. 0.3700 -0.1937
-0.1341 0.5858
-0.6715
-0.1569
0.4636
-0.5171
-0.1341 0.3472 -0.6080
0.0284
t-stat. 3.35 -6.05
-2.93 4.82
-13.57
-4.29
3.83
-10.09
-1.15 3.33 -13.26
0.72
P > t 0.001 0.000
0.003 0.000
0.000
0.000
0.000
0.000
0.251 0.001 0.000 0.470
Dep. Var.: ISRV
Coef. 0.7217 -0.6829
-0.6829 0.5784
-0.7695
-0.0373
0.2760
-0.4744
-0.0975 0.3564 -0.7118
0.0878
t-stat. 4.10 -10.45
-10.45 4.39
-14.88
-0.97
2.14
-9.15
-2.05 2.88 -13.94
2.07
P > t 0.000 0.000
0.000 0.000
0.000
0.334
0.034
0.000
0.041 0.004 0.000 0.039
Dep. Var.: IYN
Coef. 0.1563 -0.3645
0.1945 0.6003
-0.3878
0.1904
0.2057
-0.3827
0.1777 0.6765 -0.3843
0.2014
t-stat. 2.59 -6.98
4.20 8.16
-11.26
4.68
3.13
-7.00
3.78
7.69 -10.16
4.46
P > t 0.010 0.000
0.000 0.000
0.000
0.000
0.02
0.000
0.000 0.000 0.000 0.000
(1) This sample of 35 developed countries include all current 28 EU member states and also Iceland, Turkey, Norway, Switzerland,
US, Japan and Canada.
(2) ILN = Nominal long-term interest rates; ILRC = Real long-term interest rates, deflator private consumption; ILRV = Real long-
term interest rates, deflator GDP;
ISN = Nominal short-term interest rates; ISRC = Real short-term interest rates, deflator private consumption; ISRV = Real short-
term interest rates, deflator GDP;
IYN = Yield curve (= ILN – ISN).
Banks and Bank Systems, Volume 11, Issue 4, 2016
159
Table 2. Sigma convergence results
PANEL 1- 35 developed countries (1) PANEL 2 – 28 EU countries.
Variables (2) P1-
A
: 1999
–
2008 P1-B: 1999
–
2014
P2-
A
: 1999
–
2008
P2-B: 1999
–
2014
GermanyUS
Germany
US
Germany
US Germany
US
Dep. Var.: ILN
Coef. 0.4943 - 0.4801 0.4756 - 0.4685 0.5540 - 0.3379
0.4894
- 0.3690
0.4197
- 0.5107
0.3286
-0.4984 0.5230 - 0.3422 0.4102
- 0.3692
z-stat. 7.40 -9.44 -6.07 -8.34 7.28 -9.96 6.56 -10.64 6.68 -9.70 5.50 -8.29 6.12 -9.06 5.02 -9.51
P > z 0.034 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Dep. Var.: ILRC
Coef. -0.4790 - 0.6148 0.0073 - 0.5375 0.2255 -
0.3877
0.3145
-
0.4131
-0.5792
-
0.5490
-0.1303
-
0.4930 0.2143 - 0.3636 0.2772
-
0.3887
t-stat. -5.13 -9.92 0.09
-9.22 2.45 -10.52
3.64
-11.32
-5.23
-8.35
-1.43
-7.90 1.99 -9.03 2.78
-9.74
P > t 0.000 0.000 0.924 0.000 0.014 0.000 0.000 0.000 0.000 0.000 0.153 0.000 0.047 0.000 0.006 0.000
Dep. Var.: ILRV
Coef. -1.0578 - 0.6115 -0.2229 - 0.5340 0.0857 -
0.4571
0.2835
-
0.4950
-0.9153
-
0.4658
-0.2456
-
0.4127 0.1109 - 0.3911 0.2500
-
0.4369
t-stat. -7.21 -10.55 -2.08 -9.67 0.65 -11.71
2.47
-12.77
-5.90
-7.83
-2.20
-7.26 0.76 -9.45 1.95
-10.55
P > t 0.000 0.000 0.039 0.000 0.513 0.000 0.014 0.000 0.000 0.000 0.029 0.000 0.445 0.000 0.052 0.000
Dep. Var.: ISN
Coef. 0.8763 - 0.5434 1.0677 - 0.5496 0.6264 -
0.4823
0.7643
-
0.4859
0.2818
-
0.4047
0.5087
-
0.4169 0.2780 - 0.4012 0.3994
-
0.4058
t-stat. 3.72 -16.07 4.23
-14.94 4.40 -21.10
4.92
-19.51
3.30
-23.87
3.89
-15.61 4.23 -27.01 4.51
-20.20
P > t 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Dep. Var.: ISRC
Coef. -0.3497 - 1.0876 0.4444 - 0.9886 0.0356 -
0.9492
0.6299
-
0.8720
-0.5919
-
0.6276
0.0794
-
0.6059 -0.1789 - 0.6952 0.3056
-
0.6308
t-stat. -2.65 -21.76 2.92
-18.65 0.35 -24.36
5.66
-21.87
-5.94
-13.22
0.66
-11.56 -1.90 -16.88 3.08
-15.22
P > t 0.008 0.000 0.004 0.000 0.726 0.000 0.000 0.000 0.000 0.000 0.508 0.000 0.058 0.000 0.002 0.000
Dep. Var.: ISRV
Coef. -1.1959 - 0.9512 0.0954 - 0.8571 -0.2977 -
0.8916
0.4892
-
0.8246
-1.1189
-
0.6411
-0.0887
-
0.5933 -0.4032 - 0.7253 0.2737
-
0.6749
t-stat. -7.62 -18.80 0.58
-16.29 -2.27 -21.67
3.84
-20.30
-8.45
-13.43
-0.65
-11.49 -3.01 -16.18 2.19
-15.42
P > t 0.000 0.000 0.561 0.000 0.023 0.000 0.000 0.000 0.000 0.000 0.516 0.000 0.003 0.000 0.029 0.000
Dep. Var.: IYN
Coef. 0.0090 - 0.3269 -0.2478 - 0.4271 0.1898 -
0.3053
-0.0412
-
0.2776
0.0291
-
0.3636
-0.2291
-
0.4467 0.2511 - 0.2971 0.0133
-
0.3929
t-stat. 0.19 -7.09 -3.53 -8.60 3.54 -10.03
-0.64
-9.51
0.63
-7.74
-3.10
-8.27 3.98 -8.90 0.18
-10.23
P > t 0.849 0.000 0.128 0.000 0.000 0.000 0.521 0.000 0.526 0.000 0.002 0.000 0.000 0.000 0.855 0.000
(1) This sample of 35 developed countries include all current 28 EU member states and also Iceland, Turkey, Norway, Switzerland,
US, Japan and Canada.
(2) ILN = Nominal long-term interest rates; ILRC = Real long-term interest rates, deflator private consumption; ILRV = Real long-
term interest rates, deflator GDP;
ISN = Nominal short-term interest rates; ISRC = Real short-term interest rates, deflator private consumption; ISRV = Real short-
term interest rates, deflator GDP;
IYN = Yield curve (= ILN – ISN).
Banks and Bank Systems, Volume 11, Issue 4, 2016
160
Table 3. Westerlund panel cointegration test (p-values)
PANEL 1- 35 developed countries (1) PANEL 2 – 28 EU countries.
Cointegration
between the variables(2) P1-A: 1999–2008 P1-B: 1999–2014 P2-A: 1999–2008 P2-B: 1999–2014
Gt
Ga Pt Pa Gt
Ga
Pt
Pa
Gt
Ga
Pt
Pa Gt Ga
Pt
Pa
ILN and ILNGermany 0.000 0.000 0.246 0.000 1.000 1.000 1.000 1.000
0.000 0.000 0.132 0.000 1.000 1.000 1.000 1.000
ILRCand ILRCGermany 0.000 0.992 0.000 0.090 0.000 0.561 0.000 0.218
0.000 0.987
0.000 0.109 0.000 0.459 0.000 0.185
ILRVand ILRVGermany 0.000 0.585 0.000 0.001 0.000 1.000 0.039 0.974
0.000 0.520 0.000 0.000 0.000 0.997 0.013 0.930
ISNand ISNGermany 0.000 0.000 0.000 0.000 0.013 0.000 0.011 0.000
0.000 0.000 0.000 0.000 0.032 0.000 0.045 0.003
ISRCand ISRCGermany 0.000 0.000 0.000 0.046 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.047 0.000 0.000 0.000 0.000
ISRV and ISRVGermany 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
IYN and IYNGermany 0.968 0.951 0.987 0.700 0.000 0.000 0.000 0.000
0.962
0.858 0.976 0.795 0.000 0.000 0.000 0.000
ILN and ILNUS 0.992 1.000 0.104 0.963 0.073 1.000 0.148 0.967
1.000 1.000 0.413 0.972 0.139 1.000 0.276 0.969
ILRC and ILRCUS 0.937 1.000 0.999 0.947 0.000 0.019 0.000 0.000
0.957 1.000 0.998 0.932 0.000 0.008 0.000 0.000
ILRV and ILRVUS 0.000 0.730 0.000 0.198 0.000 0.643 0.000 0.001
0.000 0.963 0.000 0.185 0.000 0.359 0.000 0.001
ISNand ISNUS 0.000 0.000 0.000 0.000 0.000 0.028 0.000 0.036
0.000 0.000 0.000 0.000 0.000 0.055 0.000 0.058
ISRCand ISRCUS 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
ISRVand ISRVUS 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000
IYN and IYNUS 0.605 0.999 0.000 0.132 0.000
0.000 0.000 0.000
0.853 0.999 0.170 0.699 0.0 0.000 0.000 0.000
(1) This sample of 35 developed countries include all current 28 EU member states and also Iceland, Turkey, Norway, Switzerland,
US, Japan and Canada.
(2) ILN = Nominal long-term interest rates; ILRC = Real long-term interest rates, deflator private consumption; ILRV = Real long-
term interest rates, deflator GDP;
ISN = Nominal short-term interest rates; ISRC = Real short-term interest rates, deflator private consumption; ISRV = Real short-
term interest rates, deflator GDP;
IYN = Yield curve (= ILN – ISN).
