Financial integration and nancial development of the economies of Africa and the Mediterranean basin: a network approach
February 15, 2021
Preliminary version
Cécile Bastidon1 Bastien Bonijoly2
Abstract
The principle of Lucas paradox, and more generally the relationship between yield dierentials in favour of developing and emerging economies and interna- tional nancial integration, is the subject of a literature in full renewal. Our aim is to characterize this relationship in the case of a sample of 16 countries in the Middle East and Africa region. We apprehend the international nancial integration of the countries in the sample from the topological position of their equity markets within global equity market networks. On this basis, we study the empirical relationship between integration indicators and yield dierentials by controling the status of nancial sector reforms, proxied by the main nancial development indicators of the GFDD (World Bank) database. JEL codes: C38, F36, O16. Keywords : nancial integration, nancial development, Mediterranean and African developing countries, classi cation methods, Lucas paradox.
1Corresponding author. [email protected], LEAD, Université de Toulon, Campus Porte d'Italie, 70 Avenue Roger Devoucoux, 83000 Toulon, France ; and CAC, IXXI Institut Rhône-Alpin des Sys- tèmes Complexes, ENS Lyon, France. [email protected], LEAD, Université de Toulon, France.
1 Contents
1 Introduction 4
2 Survey 5
3 Database and network approach 6
3.1 Database ...... 6
3.2 Network methods ...... 7
3.3 Topological representations ...... 9
4 Empiral study of the relationship between stock markets networks structure, yield dierentials and nancial sector reforms 15
4.1 Main stylized facts from the Global Financial Developement Database . 15
4.2 Financial integration and stock markets yield dierentials ...... 25
4.3 Financial integration and the institutional and regulatory environment 25
5 Conclusions 25
References 25
Annexes 27
List of Figures
1 Heatmaps of distances and path length ...... 10
2 Minimal spanning tree, full period ...... 11
2 3 Minimal spanning tree, pre-Tapering period ...... 12
4 Minimal spanning tree, Tapering ...... 13
5 Minimal spanning tree, post-Tapering ...... 14
6 Network indicators, time series. Network indicators based on minimal spanning trees, log returns, full period of study...... 16
7 Network indicators, Box Whisker plots ...... 16
8 Depth, nancial institutions: Assets owned by nancial institutions . . 19
9 Depth, nancial markets: Stock market capitalization ...... 19
10 Depth, nancial institutions: Deposit money bank assets ...... 20
11 Depth, nancial institutions: Private credit by deposit money bank . . 20
12 Depth, nancial institutions: Credit to assets ...... 20
13 E ciency, nancial institutions: Bank interest margin ...... 21
14 Access, nancial markets: Value traded on stock market ...... 21
15 Stability, nancial markets: Stock indices volatility ...... 23
16 Stability, nancial institutions: Bank nonperforming loans to gross loans 23
17 Stability, nancial institutions: Bank Z-score ...... 23
List of Tables
1 Indices for the Middle East and Africa region ...... 28
2 Indices for Latin America ...... 28
3 Indices for Asia ...... 28
4 Indices for Eastern Europe ...... 28
5 Indices for Western Europe and advanced economies ...... 29
6 Countries of the .BRVMCI index ...... 29
3 1 Introduction
The principle of Lucas paradox, and more generally the relationship between positive yield dierentials of developing and emerging economies and international nancial integration, is the subject of a literature in perpetual renewal (for example, Gourinchas and Jeanne, 2013). The central idea of this recent literature is that the previously established frontier between advanced economies receiving the bulk of capital ows and developing economies subject to rationing phenomena is shifting. Since the second half of the decade 2000, emerging economies have been receiving capital ows at the level of the pro tability of the investments they oer, even if these ows remain characterized by high instability. Developing economies that have not yet fully attained the status of emerging economies, on the other hand, remain under the regime of the Lucas Paradox. On the other hand, the very measurement of the phenomenon of nancial globalization is also the subject of numerous recent studies (for example, Kose et al., 2006; Bekaert and Mehl, 2019).
Our aim is to characterize the Lucas paradox in the case of a sample of 16 countries in the Middle East and Africa zone, using an original measure of international nancial integration: the topological position of their equity markets within the global equity market networks. The choice of the study period (2013-2016) meets three criteria. On the one hand, it is a su ciently recent period for the results to allow the formulation of adequate recommendations for nancial reforms and development nancing. On the other hand, for more than ten years, it has been the only period in which international monetary policy has changed sequence in the direction of announcing and then im- plementing a tightening, and thus global tensions on long-term nancing costs. The unconventional policies and the interest rate oor practiced, respectively, in the con- text of the 2008 global nancial crisis and the COVID19 pandemic constitute a speci c environment that is not without interest, but which does not suit the characterization of developing and emerging economies' di culties in accessing private external nanc- ing. Finally, the Global Financial Development Database from which we characterize the status of nancial sector reforms is currently only available until 2016 or 2017, depending on indicators.
We apprehend the international nancial integration of the countries in the sample on the basis of the topological position of their equity markets within world markets, ac- cording to the methodology of Bastidon et al. (2019, 2020). On this basis, we study the empirical relationship between integration indicators and yield dierentials with respect to advanced economies by controling the state of nancial sector reforms, ap- proximated by the main nancial development indicators of the World Bank Global
4 Financial Development database.) This database, which is the benchmark for the in- ternational measurement of nancial development, has the advantage of being in the form of a matrix that separately measures the characteristics of banking systems and markets in terms of depth, accessibility, e ciency and stability (ihák et al., 2012). It is therefore a de facto approximation of the state of nancial sector reforms, allowing us to target the policy recommendations derived from our results.
The rest of the content of this article is as follows. Section 2 presents the literature review. Section 3 presents the empirical results. The nal section is devoted to the presentation of the conclusions.
2 Survey
Complex systems topology and nancial integration
The study of the topological characteristics of samples of asset prices allows us to appre- hend the degree of integration that characterizes them, by identifying and quantifying the propagation channels of price shocks. Like the relationships within ecosystems, of which they are a common application, their structure changes according to the eco- nomic environment, particularly in the case of phase transitions during the transition from one state to another (May et al., 2008; Vandewalle et al., 2001; Johnson et al., 2013). This tool for analyzing complex systems allows, within systems characterized by a high number of economic variables with bilateral correlation relationships, to isolate the relevant relationships and thus susceptible to economic interpretation (Soramäki et al., 2007).
The usual measures of capital market integration (Obstfeld and Taylor, 2004) are of several types: ratios of cross-border capital stocks and ows, investment ratio of the level of national investment to national savings (Feldstein and Horioka, 1979), co-evolution of domestic and foreign interest rates, etc. These measures generally show an increasing integration of capital markets since 1945, accelerated since 1980, but limited to the intensive margins, i.e. an increasing integration between advanced economies, whereas integration at the extensive margins, i.e. the integration of developing and emerging countries, would progress little (Williamson, 2007; Bastidon and Parent, 2016).
5 The Mediterranean Basin and the Lucas paradox
Without entering into the debate on the bene ts of integration for developing economies, this question of intensive and extensive margins is all the more interesting because the dynamics of foreign direct investment during the decade 2000 is very speci c. For the rst time, in fact, more than 50% of FDI ows are directed towards developing and emerging economies from 2012 onwards, with a dynamic that is clearly favorable to them (15% of the world total in 2000, and almost 60% in 2014). This favourable dynamic for developing and emerging economies constitutes both a historical precedent and a refutation of the Lucas paradox (Lucas, 1990), according to which post-colonial developing economies would generally be characterised by very low international capital ows in relation to the yield dierentials associated with them.
However, it is clear that the economies of the Mediterranean Basin do not bene t from this dynamic and remain constrained by the paradox (Bank, 2020). Our aim is to highlight the divergence of their integration dynamics from those of world capital markets, in relation to the preponderance of historical links or geographical proximity over the emergence dynamics of the last decade, associating within highly integrated sub-groups economies that only share the status of emerging markets.
In addition to the formulation of economic policy recommendations relating to the con- tinuation of the nancial reforms under way in the countries in the sample, the results obtained should provide an original approach to the problem of empirically testing the relationship between the topological status (as an original indicator of nancial inte- gration) of the countries in the sample, on the one hand; and growth, on the other hand. Although extensive, the empirical literature on the relationship between foreign investment and growth and between equity market liberalization and growth is not consensual, which may result alternately from an inappropriate speci cation of trans- mission channels, or an inappropriate speci cation of integration indicators (see, for example, Kose et al., 2006).
3 Database and network approach
3.1 Database
The rst step of our empirical approach consists in characterizing the nancial inte- gration of the countries in the sample from their position within the equity markets
6 apprehended as a network. This position is apprehended through the construction of "minimal spanning trees" Mantegna (1999); Bonanno et al. (2001); Tumminello et al. (2007), which are the most commonly used topological representation in the case of equity markets. Applied to long series of national indices with sliding windows, this methodology makes it possible in particular to highlight the increasing integration within subgroups exclusively composed of developing and emerging economies Bastidon et al. (2019).
Our sample of Middle East and African economies includes 16 equity market benchmark indices (see Table 1). All the indices are national indices except for the BRVMCI index, which is the index of the West African Regional Stock Exchange. Of the 16 indices, 6 correspond to African economies and 10 to Middle Eastern economies. The data, with daily frequency, are provided by Thomson Reuters. All indices are expressed in points.
For the development of representations of global equity market networks, our database also includes three groups respectively composed of emerging economies in Latin Amer- ica (6 indices, Table 2) and Asia (11 indices, Table 3); Eastern Europe (10 indices, Table for the development of representations of global equity market networks, our database also includes ); and advanced economies in Western Europe (16 indices, Table 5).
3.2 Network methods
We follow the Minimal spanning tree method of Mantegna (1999): The starting point of [the] investigation is to quantify the degree of similarity between the synchronous time evolution of a pair of stock price by the correlation coe cient:
hYiYji − hYii hYji (1) ρij = r 2 2 D 2ED 2E (hYi i hYi i) Yj Yj
where i and j are the numerical labels of stocks, Yi = ln (Pi (t))−ln (Pi (t=1)) and Pi (t) is the closure price of the stock i at the day t. The statistical average is a temporal average performed on all the trading days of the investigated time period. For both portfolios, [we] determine the n×n matrix of correlation coe cients for daily logarithm price dierences.
The matrix of correlation coe cients is a symmetric matrix with ρii = 1 in the main diagonal. Hence, in each portfolio, n(n − 1)/2 correlation coe cients characterize the
7 matrix completely. [We] investigate the correlation coe cient matrix to detect the hier- archical organization present inside the stock market. In the search for an appropriate topological arrangement of stocks of a given portfolio, [we] rst look for a metric. The correlation coe cient of a pair of stocks cannot be used as a distance between the two stocks because it does not ful ll the three axioms that de ne an Euclidean metric. However a generalized metric can be de ned using as distance an appropriate function of the correlation coe cient. The chosen function is
q d (i, j) = 2 (1 − ρij) (2)
With this choice d (i, j) ful lls the three axioms of an Euclidean metric (I) d (i, j) = 0 if and only if i = j; (II) d (i, j) = d (j, i)) and (III) d (i, j) ≤ d (i, k) + d (k, j) for all practical purposes.
Representations of the minimal spanning trees can be found in the section 3.3. We propose four representations: one representation for which the whole study period is taken into account, and three representations corresponding respectively to the period before the change in the sequence of monetary policy of the United States Central Bank ('pre-tapering'), a period corresponding to the tapering itself, and a 'post-tapering' period. From these representations, we calculate two types of indicators describing the structure of the networks, used as proxies for the nancial integration of the countries in the sample in the empirical study of section 4.
Based on the typology of Soramäki et al. (2007), we use the following network indicators. For distance measurements, the distance to nearest neighbors, which is the average distance to the nodes with which each of the nodes in the spanning tree is directly connected, is used as a local integration measure. Local integration is understood here not in the geographical sense but in the sense of neighbourhood in the network, both of which may or may not coincide depending on the existence of economic, nancial, historical, etc. relations without geographical proximity. The global distances used are the length of the paths between pairs of nodes; and eccentricity, that is, for each node, the length of the longest path. For connectivity measures, direct connectivity is measured by the degree of nodes, i.e., the number of nodes that are closest neighbors; and indirect connectivity is measured by the degree of closest neighbors.
High integration is characterized by short distances and high direct connectivity; inter- mediate integration by intermediate distances, low direct connectivity and high indirect connectivity; and low integration by high distances and low connectivity. Finally, com- paring local and global distance hierarchies provides information on the existence of
8 privileged integration relationships within subsets of countries: a higher rank in the local distance hierarchy for a given subset indicates the existence of such relationships; an equivalent or even lower rank indicates their absence.
3.3 Topological representations
The minimal spanning tree of the full period (Figure 2) shows 3 main sub-trees. The rst sub-tree consists of advanced economies (bottom right, with the Netherlands and France as hubs). The United Kingdom is the connection with a large sub-tree of emerging economies (mainly in the upper right, with Singapore as a hub). The third sub-tree consists of developing and emerging economies (in the upper left, with Dubai as a hub). The Middle Eastern economies in our sample are all in the third sub-tree, and African economies are in the second (West Africa, Egypt, Kuwait, Morocco), except for South Africa ( rst sub-tree) and Botswana (third sub-tree). The heatmaps of Figure 1, which represent distances and bilateral path lengths for all nodes of the network, allow a dierent visualization of the same information: the overall degree of integration of the countries studied is not atypical compared to the rest of the developing and emerging economies (high distance values outside the neighborhood of the diagonal). On the other hand, while the advanced and emerging Asian and Latin American economies (but not the transition countries) are highly integrated within their respective groups (low distances in the neighborhood of the diagonal), among the countries studied only the Middle Eastern economies (and not the African economies) share this characteristic.
9 (a) Distances matrix
(b) Paths length matrix Full distance matrix and path length matrix based on minimal spanning trees, log returns, full period of study. Country groups, from left to right: advanced, transition, Asia Paci c, Latin America, Middle East and Africa. Figure 1: Heatmaps of distances and path length
10 Philippines Shangai Slovaquia S_Korea Indonesia Ukraine China Thailand India West_Af Malaysia Taiwan Lebanon HongKong Pakistan Slovenia
Bahrein Singapore Hungary
Iceland Botswa Dubai Norway Irak Japan UK Abu_Dhabi Jordan Oman Tunisia Chile S_Af Italy NL Colombia Saudi_Ar USA Mexico Brazil Argenti Qatar Romania
Egypt Finland France Austria Germany Koweit Belgium Poland Spain Sweden Cz_Rep Morroco Israel Swiss Croatia Denmark Greece Turkey Lituania Portugal Lettonia
Figure 2: Minimal spanning tree, full period
11 Slovenia Tunisia Bahrein
Greece Ukraine Hungary Pakistan India S_Af Poland Italy Egypt Argenti Lituania Norway Saudi_Ar Slovaquia Colombia Cz_Rep Koweit Germany Lebanon France USA Dubai Mexico Abu_Dhabi Austria NL Brazil
Oman UK Denmark RomaniaCroatia Swiss Israel Philippines West_Af Spain Thailand Turkey Portugal Sweden Malaysia Belgium Indonesia Finland Jordan Lettonia Iceland Japan Qatar
Taiwan S_Korea
Morroco HongKong
Botswa Shangai
China
Figure 3: Minimal spanning tree, pre-Tapering period
12 Figure 4: Minimal spanning tree, Tapering
13 Egypt Pakistan Slovaquia Iceland
Romania Ukraine
Botswa Hungary Denmark Swiss Greece Sweden Spain Poland Cz_Rep Belgium West_Af France Austria Germany Colombia Croatia Israel Finland NL Mexico USA Brazil Argenti Lituania UK Saudi_Ar Qatar Portugal Norway Abu_Dhabi
Oman S_Af Koweit India Chile Jordan Turkey HongKong Tunisia Slovenia Italy Japan S_Korea Thailand Taiwan
Bahrein China Lettonia Morroco Indonesia Irak Lebanon Malaysia Shangai Philippines
Figure 5: Minimal spanning tree, post-Tapering
The organization of the minimal spanning trees by sub-period (Figures 3, 4 and 5) is dierent from that of the period considered as a whole. In general, we do not nd the structure consisting in advanced economies in a sub-tree vs. developing and emerging economies in another sub-tree, but a dierent pattern: advanced economies in the core of the network vs. developing and emerging economies in the branchings. This result suggests that the structure of equity market networks is not xed over time, but that its
14 characteristics of short-term structure (dierentiation of sub-trees) as well as medium- term structure (dierentiation core vs. peripheries) are both relevant to apprehend the nancial integration relationship between advanced economies on the one hand, and developing and emerging economies on the other hand.
This temporal dimension is apprehended by the time series of network indicators cal- culated by sliding windows (for the method, see Bastidon et al., 2020). The time series obtained are shown in Figures 6 and 7. It can be seen that the observation that the local distance measurements of the countries studied ( rst line, left, represented in cyan on the gures) are much more atypical than their global distance measurements ( rst line, middle and right) is true throughout the period. The same applies for direct connectivity (second line, left), which remains atypically low throughout the period. On the other hand, indirect connectivity (second line, middle) tends to increase at the end of the period. The countries studied, while remaining connected with atypically high distances to their neighborhood as seen above, therefore tend nevertheless to move towards areas that are less peripheral in the branchings in which they are located.
4 Empiral study of the relationship between stock markets networks structure, yield dierentials and nancial sector reforms
4.1 Main stylized facts from the Global Financial Developement Database
We use the World Bank's Global Financial Development database (ihák et al., 2012) as a source of proxies for the status of nancial reforms in the countries studied. The advantage of this database is to characterize nancial institutions and markets sepa- rately, and for each of the two, four dierent types of features: depth, access, e ciency, stability. Each of these characteristics is assessed by a set of dierent variables, which are generally widely available for advanced economies. For the countries under study, this availability is lower, some variables being not available at all, and others with recent dates exclusively.
There are missing data for the countries under study: some indicators have data for one or a low number of countries. This is explained in some cases by events that may
15 1.4 14 20 Middle East Africa Middle East Africa Middle East Africa
1.2 12 18
16 1 10
14 0.8 8 12 0.6 6 10
0.4 4 8
0.2 2 6
0 0 4 01/01/0113 01/01/0114 01/01/0115 01/01/0116 01/01/0117 01/01/0113 01/01/0114 01/01/0115 01/01/0116 01/01/0117 01/01/0113 01/01/0114 01/01/0115 01/01/0116 01/01/0117 average distance to the nearest neighbours average path eccentricity
12 12 Middle East Africa Middle East Africa
10 10
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0 0 01/01/0113 01/01/0114 01/01/0115 01/01/0116 01/01/0117 01/01/0113 01/01/0114 01/01/0115 01/01/0116 01/01/0117 degree nearest neighbours degree Figure 6: Network indicators, time series. Network indicators based on minimal span- ning trees, log returns, full period of study.
1.4 20
12 1.2 18
16 1 10 14 0.8 8 12
0.6 6 10
0.4 8 4 6 0.2
2 4
NL NL NL
UK US UK US UK US
Irak Irak Irak
Italy Italy Italy
India S_Af India S_Af India S_Af
Israel Israel Israel
Brazil Qatar Brazil Qatar Brazil Qatar
Spain Egypt Spain Egypt Spain Egypt
Swiss China Dubai Swiss China Dubai Swiss China Dubai
Japan Oman Japan Oman Japan Oman
Koweit Koweit Koweit
Turkey Jordan Turkey Jordan Turkey Jordan
Austria France Poland Austria France Poland Austria France Poland
Argenti Mexico Argenti Mexico Argenti Mexico
Iceland Croatia Taiwan Tunisia Iceland Croatia Taiwan Tunisia Iceland Croatia Taiwan Tunisia
Finland Greece Botswa Finland Greece Botswa Finland Greece Botswa
Norway Norway Norway
Ukraine Bahrein Ukraine Bahrein Ukraine Bahrein
Lituania Lituania Lituania
Lettonia Lettonia Lettonia
Belgium Sweden Cz_Rep Belgium Sweden Cz_Rep Belgium Sweden Cz_Rep
Shangai Morroco Shangai Morroco Shangai Morroco
Portugal Portugal Portugal
Hungary West_Af Hungary West_Af Hungary West_Af
Slovenia Pakistan Thailand Slovenia Pakistan Thailand Slovenia Pakistan Thailand
S_Korea Lebanon S_Korea Lebanon S_Korea Lebanon
Malaysia Malaysia Malaysia
Romania Romania Romania
Denmark Denmark Denmark
Germany Saudi_Ar Germany Saudi_Ar Germany Saudi_Ar
Colombia Colombia Colombia
Indonesia Indonesia Indonesia
Slovaquia Slovaquia Slovaquia
HongKong HongKong HongKong
Philippines Philippines Philippines
Abu_Dhabi Abu_Dhabi Abu_Dhabi average distance to the nearest neighbours average path eccentricity
12 12
10 10
8 8
6 6
4 4 2 2
0
NL NL
UK US UK US
Irak Irak
Italy Italy
India S_Af India S_Af
Israel Israel
Brazil Qatar Brazil Qatar
Spain Egypt Spain Egypt
Swiss China Dubai Swiss China Dubai
Japan Oman Japan Oman
Koweit Koweit
Turkey Jordan Turkey Jordan
Austria France Poland Austria France Poland
Argenti Mexico Argenti Mexico
Iceland Croatia Taiwan Tunisia Iceland Croatia Taiwan Tunisia
Finland Greece Botswa Finland Greece Botswa
Norway Norway
Ukraine Bahrein Ukraine Bahrein
Lituania Lituania
Lettonia Lettonia
Belgium Sweden Cz_Rep Belgium Sweden Cz_Rep
Shangai Morroco Shangai Morroco
Portugal Portugal
Hungary West_Af Hungary West_Af
Slovenia Pakistan Thailand Slovenia Pakistan Thailand
S_Korea Lebanon S_Korea Lebanon
Malaysia Malaysia
Romania Romania
Denmark Denmark
Germany Saudi_Ar Germany Saudi_Ar
Colombia Colombia
Indonesia Indonesia
Slovaquia Slovaquia
HongKong HongKong
Philippines Philippines
Abu_Dhabi Abu_Dhabi degree nearest neighbours degree Figure 7: Network indicators, Box Whisker plots
16 take place within these countries, such as geopolitical instability or even wars. Despite these missing data, we have satisfactory coverage of 4 depth variables (3 for nancial institutions, 1 for markets), 1 access variable (markets), 1 e ciency variable ( nancial institutions) and 3 stability variables (2 for nancial institutions, 1 for markets). In total, these 9 variables allow a detailed characterization of the state of nancial reforms in the countries under study. In what follows, we present the main stylized facts of this dataset.
Our sample of market indices of African and Middle Eastern countries consists of 16 indices. However, these indices do not necessarily correspond to a country. There is one set of variables in the indices database which correspond to a single country: for exemple, the indices for Dubai and Abu Dhabi both correspond to the United Arab Emirates in the Global Financial Development Database. On the other hand, there is one single index which corresponds to a group of countries: the BRVMCI index corresponds to a composite index of companies from 8 West African countries (see table 6 for country details). The total number of countries corresponding to the 16 variables in the indices database is 22 in the Global Financial Development Database.
In order to facilitate the reading of the graphs of each indicator, two sub-samples have been represented, that of the Middle-East and that of Africa. In case a large number of countries do not have data for an indicator, only one graph is provided. Finally, a global average and a sub-average, for each sub-sample, were calculated and added to their respective representations. The global average and sub-average are displayed only if there is more than 3 countries with data at a given date. When there is less than 3 countries, the average is not displayed.
Depth indicators
The depth of funding systems is the characteristic for which the most information is generally available. This is indeed the case for the countries in the study. It is also the most intuitive measure of nancial development in that it characterizes the size of the two segments of nancing systems. Figures 8 and 10 to 12 display the indicators of the depth of nancial institutions. The data represented in Figures 10 and 11 allow us to calculate the Credits over Deposits ratio represented in Figure 12. Figure 9 displays the indicator of markets depth.
For the countries studied, the depth of intermediated systems is much better known than the depth of nancial markets, which is in itself a sign of weak disintermediation
17 and a presumption of low nancial development. As regards the depth of nancial markets (Figure 9), we can see that their market capitalization reached on average, for both sub-samples as a whole, and more particularly for the Middle East countries, a high point before the Global nancial crisis. Nevertheless, with the exception of Lebanon, it can be seen that the medium term upward trend of the level of market capitalization is not aected by the crisis whatever the sub-sample considered. South Africa stands out particularly from the others. With this exception, the averages of the two sub-samples both remain fairly close to the total average, not showing any particular regional heterogeneity.
As regards the depth of the intermediated funding system, when looking at the assets held by nancial institutions (Figure 8), South Africa also stands out here with a much higher development and increasing trend than the rest of the countries in the sample. It is important to note that few countries have data available for this indicator, so this comparison should be considered with caution. As regards the other indicators of depth of the intermediated system, both deposits (Figures 10) and credits (Figure 11) are increasing steadily, in general.
However, the credit-to-deposit ratio (Figure 12) shows that the averages have a slightly downward trend for both sub-samples. In detail, we can see small countries that stand out such as Kuwait or Saudi Arabia which have an upward trend throughout the period since the mid-2000s, but they remain exceptions to the rule. The much larger balance sheet size of South African banks, previously highlighted, is clearly not attributable to the traditional intermediation activity through credit distribution, since neither the loans nor the credit/deposit ratio are signi cantly dierent from those of the rest of the countries represented here.
E ciency and access
Indicators of e ciency (mainly related to price formation processes and nancing costs, more generally) and access (mainly related to nancing ows, rather than stocks, which associated with the depth dimension) are scarcely available for the countries in the sample. They do not show a very high degree of heterogeneity either between the two subzones, or within them, with the exception of a small number of outliers for small periods of time (for example, Irak, South Africa and Togo in the case of the interest rate margin).
Generally speaking, as far as the e ciency of intermediated systems is concerned, this heterogeneity tends to decrease, and e ciency itself tends to improve, with a tendency
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Figure 8: Depth, nancial institutions: Assets owned by nancial institutions
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1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Bahrain Egypt Jordan 0
Kuwait Lebanon Oman
1991 1992 1996 1997 2002 2007 2008 2012 2013 1989 1990 1993 1994 1995 1998 1999 2000 2001 2003 2004 2005 2006 2009 2010 2011 2014 2015 2016 2017 Qatar Saudi Arabia United Arab Emirates Ivory Coast Botswana Morocco South Africa Mean Middle East Mean all Tunisia Mean Africa Mean all (a) Middle East (b) Africa Figure 9: Depth, nancial markets: Stock market capitalization
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1982 1986 1988 1992 1994 1998 2002 2004 2008 2010 2014 1980 1984 1990 1996 2000 2006 2012 2016
1980 1981 1982 1984 1985 1986 1988 1989 1990 1992 1993 1994 1996 1997 1998 2000 2001 2002 2004 2005 2006 2008 2009 2010 2012 2013 2014 2016 2017 1983 1987 1991 1995 1999 2003 2007 2011 2015 Bahrain Egypt Iraq Jordan Benin Burkina Faso Ivory Coast Guinea-Bissau Mali Kuwait Lebanon Oman Qatar Niger Senegal Togo Botswana Morocco Saudi Arabia United Arab Emirates Mean Middle East Mean all South Africa Tunisia Mean Africa Mean all (a) Middle East (b) Africa Figure 10: Depth, nancial institutions: Deposit money bank assets
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1983 1987 2002 2006 2010 1980 1981 1982 1984 1985 1986 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2003 2004 2005 2007 2008 2009 2011 2012 2013 2014 2015 2016 2017 Benin Togo Senegal Niger Burkina Faso Ivory Coast Guinea-Bissau Mali Botswana Morocco South Africa Tunisia Mean Africa Mean all
Figure 11: Depth, nancial institutions: Private credit by deposit money bank
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1980 1984 1989 1994 1999 2003 2008 2013 1982 1987 1992 1997 2002 2007 2012 2017 Bahrain Egypt Iraq Jordan Benin Burkina Faso Ivory Coast Guinea-Bissau Mali Kuwait Lebanon Oman Qatar Niger Senegal Togo Botswana Morocco Saudi Arabia United Arab Emirates Mean Middle East Mean all 20 South Africa Tunisia Mean Africa Mean all (a) Middle East (b) Africa Figure 12: Depth, nancial institutions: Credit to assets 25
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1999 2003 2007 2011 2015 1996 2000 2004 2008 2012 2016 Bahrain Egypt Jordan Kuwait Benin Burkina Faso Ivory Coast Guinea-Bissau Mali Lebanon Oman Qatar Saudi Arabia Niger Senegal Togo Botswana Morocco United Arab Emirates Mean Middle East Iraq Mean all South Africa Tunisia Mean Africa Mean all (a) Middle East (b) Africa Figure 13: E ciency, nancial institutions: Bank interest margin
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1990 1994 1997 2001 2004 2008 2011 2015 1991 1992 1993 1995 1996 1998 1999 2000 2002 2003 2005 2006 2007 2009 2010 2012 2013 2014 2016 2017 Bahrain Egypt Jordan 1989 Lebanon Kuwait Oman Ivory Coast Botswana Morocco South Africa Qatar United Arab Emirates Mean Middle East Tunisia Mean Africa Mean all Saudi Arabia Mean all (a) Middle East (b) Africa Figure 14: Access, nancial markets: Value traded on stock market
21 for the margin of intermediation to decrease slowly (Figure 13). From the point of view of access to markets (Figure 14), heterogeneity tends to increase, but the overall trend is also favorable, with the Global nancial crisis constituting a notable regression, but one that does not interrupt the medium-term trend for the Middle East region. For the Africa region, individual dynamics are less visibly connected to the global environment.
Stability indicators
Finally, the stability indicators have the particularity of being, in general, very weakly correlated with the three characteristics previously reviewed (for example, advanced economies have generally favorable levels on the rst three criteria, but not necessarily on the last one). The most straightforward indicator is market volatility (Figure 15), which characterizes the stability of nancial markets. There is already a notable amount of data for the Africa zone, and even for the Middle East zone, event if data do not start, for several countries, before the mid-2000s. However, several trends can already be identi ed. First of all, like the rest of the world, there was a very high volatility during the Global nancial crisis. But what we are most interested in is an increase coinciding with the Tapering at the end of the period, mostly visible for the Middle East zone.
As regards nancial institutions, Z-scores, which are a measure of the probability of default (the latter decreasing with the level of the Z-score) are the most di cult to interpret because there are disparities between the individual dynamics of countries, particularly in the Middle East sub-sample (Figure 17). It can be seen from the average that over most of the period, the Middle East zone has a higher Z-score than the Africa zone. For example, Jordan and Lebanon for the former and Tunisia and Morocco have high Z-score (above 30%) while Barhain, Qatar and a large part of the African countries have levels below 20% (or even 10%). The overall dynamics of non-performing loans (Figure 16) is more straightforward in the sense of a decrease and convergence throughout the period for the Middle East zone, and for Africa also a drop in the average.
Summary
These main stylized facts from the Global Financial Development Database show sev- eral interesting points. From a methodological point of view, a common feature is a recurrent and persistent lack of data. This justi es the choice of the Global Financial
22 50
45 45
40 40
35 35
30 30 25 25 20 20 15 15 10 10 5 5 0
0
2004 2009 1995 1996 1997 1998 1999 2000 2001 2002 2003 2005 2006 2007 2008 2010 2011 2012 2013 2014 2015 2016 2017
1995 1997 1999 2001 2003 2005 2014 2016 1998 2000 2002 2004 2006 2007 2008 2009 2010 2011 2012 2013 2015 2017 Bahrain Jordan Kuwait Lebanon 1996 Oman Qatar Saudi Arabia United Arab Emirates Botswana Egypt Morocco South Africa Mean all Mean Middle East Tunisia Moyenne Afrique Mean all (a) Middle East (b) Africa Figure 15: Stability, nancial markets: Stock indices volatility
30
30 25 25 20 20
15 15
10 10 5 5
0
1999 2000 2003 2006 2007 2010 2011 2014 2017 2001 2002 2004 2005 2008 2009 2012 2013 2015 2016 1998 0
Bahrain Egypt Jordan
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Kuwait Lebanon Oman 1998 Qatar Saudi Arabia United Arab Emirates Senegal Botswana Morocco South Africa Moyenne Moyen Orient Mean all Tunisia Moyenne Afrique Mean all (a) Middle East (b) Africa Figure 16: Stability, nancial institutions: Bank nonperforming loans to gross loans
120 70 100 60
50 80
40 60
30 40
20 20 10
0
0
1996 1997 1998 1999 2000 2001 2003 2004 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2002 2005
1996 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2010 2011 2012 2013 2014 2015 2016 2017 1998 2009 Bahrain Egypt Iraq Jordan Benin Burkina Faso Ivory Coast Guinea-Bissau Mali Kuwait Lebanon Oman Qatar Niger Senegal Togo Botswana Morocco Saudi Arabia United Arab Emirates Mean Middle East Mean all South Africa Tunisia Mean Africa Mean all (a) Middle East (b) Africa Figure 17: Stability, nancial institutions: Bank Z-score
23 Database, which brings together the broadest possible information on nancial devel- opment and thus makes it possible to nd alternatives when certain variables relating to the countries under study are not available. As regards heterogeneity, both within and between the two subgroups, for most of the indicators, it mainly results from the existence of outliers, often for short periods of time. Overall it seems that the Mid- dle East would be more characterized by a lower level of nancial development and thus progress of nancial reforms than Africa, with the exception of South Africa, but this nding may partly result from the fact that a single index alone groups 8 African countries when a country-speci c index is used for all the others. Generally speaking, for both nancial institutions and markets, the medium-term trend in all indicators is favorable, with one exception, which is the depth of intermediated systems as measured by the credit/deposit ratio. This point is important because it generates dependency on external nancing and therefore a speci c sensitivity to nancing conditions in global capital markets.
Another common feature is that most countries do not seem to have been lastingly impacted by the Global nancial crisis, especially in terms of market capitalization. This seemingly paradoxical point is in fact due to the increased attractiveness of devel- oping and emerging economies in periods of low growth and zero lower bound of policy rates in advanced economies. This interpretation is, moreover, fully consistent with the observation of the eects of the announcements of exit from unconventional monetary policies. Besides the common feature of a lowly persistent eect of the Global nancial crisis, one of the most striking facts is the presence of a tapering eect on the volatility of the countries under study, which justi es our interest in this issue and our choice to present the network representations of Section 3.3 by sub-periods with reference to this particular episode. This is true even for countries that are less extensively studied by the literature as regards the eects of the tapering.
24 4.2 Financial integration and stock markets yield dierentials
4.3 Financial integration and the institutional and regulatory environment
5 Conclusions
References
Bank, T.W., 2020. How Transparency Can Help the Middle East and North Africa.
Bastidon, C., Bordo, M., Parent, A., Weidenmier, M., 2019. Towards an unstable hook: The evolution of stock market integration since 1913. NBER Working Paper Series No. 26166, 28 p.
Bastidon, C., Parent, A., 2016. What Form did Global Financial Integra- tion Take from 1960 to 2015? A Topological Analysis. SSRN Scholarly Paper ID 2902547. Social Science Research Network. Rochester, NY. URL: https://papers.ssrn.com/abstract=2902547.
Bastidon, C., Parent, A., Jensen, P., Abry, P., Borgnat, P., 2020. Graph-based era segmentation of international nancial integration. Physica A: Statistical Mechanics and its Applications 539, 122877.
Bekaert, G., Mehl, A., 2019. On the global nancial market integration swoosh and the trilemma. Journal of International Money and Finance 94, 227245.
Bonanno, G., Lillo, F., Mantegna, R.N., 2001. High-frequency cross- correlation in a set of stocks. Quantitative Finance 1, 96104. URL: http://dx.doi.org/10.1080/713665554, doi:10.1080/713665554.
ihák, M., Demirgüç-Kunt, A., Feyen, E., Levine, R., 2012. Benchmarking nancial systems around the world .
Feldstein, M.S., Horioka, C.Y., 1979. Domestic savings and international capital ows. National Bureau of Economic Research Cambridge, Mass., USA.
Gourinchas, P.O., Jeanne, O., 2013. Capital ows to developing countries: The alloca- tion puzzle. Review of Economic Studies 80, 14841515.
25 Johnson, N., Zhao, G., Hunsader, E., Qi, H., Johnson, N., Meng, J., Tivnan, B., 2013. Abrupt rise of new machine ecology beyond human response time. Nature Scienti c reports 3.
Kose, M.A., Prasad, E., Rogo, K.S., Wei, S.J., 2006. Financial globalization: a reap- praisal. Technical Report. National Bureau of Economic Research.
Lucas, R.E., 1990. Why doesn't capital ow from rich to poor countries? The American Economic Review 80, 9296.
Mantegna, R.N., 1999. Hierarchical structure in nancial markets. The European Physical Journal B-Condensed Matter and Complex Systems 11, 193197.
May, R.M., Levin, S.A., Sugihara, G., 2008. Complex systems: Ecology for bankers. Nature 451, 893895.
Obstfeld, M., Taylor, A.M., 2004. Global capital markets: integration, crisis, and growth. Cambridge University Press.
Soramäki, K., Bech, M.L., Arnold, J., Glass, R.J., Beyeler, W.E., 2007. The topology of interbank payment ows. Physica A: Statistical Mechanics and its Applications 379, 317333.
Tumminello, M., Coronnello, C., Lillo, F., Micciche, S., Mantegna, R.N., 2007. Span- ning trees and bootstrap reliability estimation in correlation-based networks. Inter- national Journal of Bifurcation and Chaos 17, 23192329.
Vandewalle, N., Brisbois, F., Tordoir, X., others, 2001. Non-random topology of stock markets. Quantitative Finance 1, 372374.
Williamson, J.G., 2007. Global Capital Markets in the Long Run: A Review of Maurice Obstfeld and Alan Taylor's" Global Capital Markets". Journal of economic literature , 400409.
26 27 Appendix
Appendix 1 : List of equity market indices
RIC ticker Index Country RIC ticker Index Country
.BRVMCI COMPOSITE INDEX Western .SSEC SSE COMPOSITE Shangai Africa .HSI HANG SENG INDEX Hong Kong .JTOPI TOP40 -TRADEAB South .KS11 KOSPI South Korea Africa .STI STRAITS TIMES Singapour .DCIBT DC INDEX Botswana .TWII TAIWAN WEIGHTED Taiwan .TUNINDEX20 TUNINDEX 20 Tunisia .NSEI NIFTY 50 India .BAX BB ALL SHARE I Bahrein .JKSE IDX COMPOSITE Indonesia .EGX30 EGX 30 IDX Egypt .SETI SET Index Thailand .ISX60 ISX MAIN IDX Irak .MXX MXSE IPC GRAL IN Mexico .AMGNRLX AMM FR FLT IDX Jordania .PSI PHILIPPINE-PSEi Philippines .KW15 KW 15 INDEX Kuweit .KSE KARACHI 100 INDX Pakistan .BLSI BLOM STK IDX Lebanon .VNI VN Index/d Viet Nam .MASI CASA ALL SHARES Morrocco Total 11 indices .MSI MSM MAIN 30 ID Oman Table 3: Indices for Asia .QSI QE MAIN 20 IDX Qatar .TASI TDW MAIN IDX Saudi Arabia RIC ticker Index Country .ADI ADX MAIN INDEX Abu Dhabi .WIG20 WIG20 Poland .DFMGI DFM MAIN INDEX Dubai .BUX BUDAPEST SE INDX Hungary Total 16 indices .BETI BUCHAREST BETI Romania .PFTSI PFTS Index Ukraine Table 1: Indices for the Middle East and .SAX SAX INDEX Slovaquia Africa region .CRBEX CROBEX INDEX Croatia .SBITOP SBITOP Sloveni .OMXVGI OMXV GENERAL Lituania RIC ticker Index Country .OMXRGI OMXR GENERAL Lettonia .BVSP BVSP BOVESPA IND Brazil .PX PX-PRAGUE SE IND Czech Rep. .MXX MXSE IPC GRAL IN Mexico Total 10 indices .IGBC Colombia SE Colombia .MERV BUSE MERVAL IN/d Argentina Table 4: Indices for Eastern Europe .ADRIAN BEC ADRIAN Chili .IBC IBC INDEX Venezuela 28 Total 6 indices
Table 2: Indices for Latin America Source: Thomson Reuters RIC ticker Index Country
.GDAXI XETRA DAX PF Germany .FCHI CAC 40 INDEX France .FTSE FTSE United-Kingdom .IBEX IBEX 35 INDEX Sppain .MCX MICEX INDEX Italy .AEX AEX-Index The Netherlands .SSMI SMI PR Switzerland .OMXS30 OMXS30 INDEX Sweden .BFX BEL20 Belgium .OBX OSLO OBX INDEX Norway .ATX ATX-INDEX VIENNA Austria .OMXC20 OMXC 20 Denmark .ATG AT COM SHR PR ID Greece .OMXHPI OMXH GEN PI Finland .PSI20 PSI 20 INDEX Portugal .OMXIPI OMX ICX PI Island .SPX S&P 500 INDEX United States .N225 NIKKEI 225 INDEX Japan Total 16 indices
Table 5: Indices for Western Europe and advanced economies
Source: Thomson Reuters
Appendix 2 : List of equity market indices: countries of the BRVMCI index
Benin Burkina Faso Guinea-Bissau Yvory Coast (Côte d'Ivoire)
Mali Niger Senegal Togo
Table 6: Countries of the .BRVMCI index
29