The Interaction between Financial Development and Financial Globalization in CEE Countries: Evidence from Time Series Analysis

by Karolis Bielskis 700432 Msc. University of Tilburg

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Econometrics and Mathematical Economics

Tilburg School of Economics and Management Tilburg University

Supervisor: dr. Otilia Boldea

Tilburg, 2016 INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION

Bielskis Karolis. The interaction between financial development and financial globalization in CEE countries: Evidence from time series analysis:

[Manuscript]:

Master Thesis: Econometrics and Mathematical Economics

Tilburg, Tilburg University

Abstract

This paper investigates the interaction between financial globalization and financial development in Central and Eastern Europe. By using 12 countries from particular region and a time period of 1991 – 2014, this article presents the findings from a different time series techniques (panel VAR, Granger causality, Common Correlated Effects estimator and others) applied for a specific data set. The results seem to support theoretical idea that financial globalization positively affects dynamics of financial development. It clearly indicates that financial openness to global markets is seen as a crucial factor stimulating financial development during the transition period from the developing to highly developed economy.

Keywords: Financial globalization, Financial development, Panel VAR estimation, CCE mean-group estimator

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Total words: 10 106

Table of Contents

Introduction ...... 6

1. Motivation...... 8

2. Empirical setup ...... 11

3. Econometric methods ...... 17

3.1. IPS panel unit root test ...... 18 3.2. Phillips-Perron unit root test ...... 19 3.3. Principal component analysis (PCA) ...... 20 3.4. Panel vector autoregression (panel VAR) ...... 20 3.5. Granger causality ...... 22 3.6. Common correlated effect (CCE) mean group estimator ...... 23 3.7. Descriptive statistics ...... 24 4. Results ...... 25

4.1. Panel IPS and Phillips-Perron unit root tests ...... 25 4.2. Principal Component Analysis (PCA) ...... 27 4.3. Panel vector autoregression (VAR) estimation ...... 29 4.4. Granger causality test and common correlated effects (CCE) estimation ...... 30 5. Conclusion ...... 32

Bibliography ...... 35

Appendix A ...... 38

Appendix B ...... 41

Appendix C ...... 43

Appendix D ...... 44

Appendix E ...... 45

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List of Figures

Figure 1. Flow chart of following chapters ...... 8

Figure 2. Average private sector credit of 12 CEE countries (1991-2014) ...... 13

Figure 3. Average domestic credit from banking sector in 12 CEE countries (1991-2014) ...... 13

Figure 4. Average liquid liabilities of 12 CEE countries (1991-2013) ...... 13

Figure 5. Average Stock market capitalization of 12 CEE countries (1992-2012) ...... 14

Figure 6. Average Stock market turnover ratio of 12 CEE countries (1992-2013) ...... 14

Figure 7. Average stock traded of 12 CEE countries (1992-2013) ...... 15

Figure 8. Average interconnectedness to GDP ratio of 12 CEE countries (1991-2014) ...... 16

Figure 9. Average Chinn/Ito index of 12 CEE countries (1991-2014) ...... 17

Figure 10. Average foreign banking ownership of 12 CEE countries (2004-2013) ...... 17

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List of Tables

Table 1. Descriptive statistics of variables ...... 24

Table 2. Results from IPS panel unit root test ...... 26

Table 3. Phillips-Perron unit root test for individual countries in levels, (p-values) ...... 27

Table 4. Phillips-Perron unit root test for individual countries in first differences, (p-values) ...... 27

Table 5. Principal component analysis (PCA) for FD variables ...... 28

Table 6. Principal component analysis (PCA) for FG variables ...... 29

Table 7. PVAR(1) estimation based on pc1_d, pc1_g, pc2_g variables ...... 29

Table 8. PVAR(1) estimation based on pc2_d, pc1_g, pc2_g variables ...... 30

Table 9. Common Correlated Effects Mean Group estimator (pc1_d, pc1_g, pc2_g) ...... 32

Table 10. Common Correlated Effects Mean Group estimator (pc2_d, pc1_g, pc2_g) ...... 32

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Introduction Financial sector can be explained as the set of institutions, instruments and markets (OECD Statistics Portal, 2013). Levine (1997) identifies the main functions of financial system as following: 1) production of ex ante information about the possible investments and capital optimization, 2) monitoring investments after they are provided, 3) trade alleviation and better risk management by diversifying it, and 4) promotion of the exchange of goods and services. Moreover, the paper written by Financial sector team of DFID (Department for International Development) (2004) claims that financial sector can be developed in different ways: 1) by improving efficiency and competitiveness of the sector, 2) by broadening the range of possible financial services, 3) by diversifying institutions acting in the sector, 4) by boosting the amount of money intermediated through the financial sector, and 5) by regulating and increasing security of the sector.

From theoretical point of view, financial development (indicated as “FD” in the following parts of the thesis) was deeply analyzed by Greenwood and Jovanovic (1990), and Bencivenga and Smith (1991) which created models explaining the effect of it. Both models were concentrating on analysis of financial intermediaries and how they affect FD. Greenwood and Jovanovic (1990) highlighted the conclusion saying that as the key part of FD financial intermediaries lead to idea that distribution of income across society stabilizes, saving rates fall down and the growth rate converges to a higher level than it was before (Greenwood & Jovanovic, 1990). Bencivenga and Smith (1991) created another model by analyzing effects of financial institutions and liquidity of assets inside the country. In that case banks reduce investment in liquid assets by insuring businesses against unpredictable liquidity needs in case of shocks (Bencivenga & Smith, 1991). Moreover, better allocation of savings in terms that less of them are needed for liquid assets also leads to a better capital accumulation as it is one of the main factors of growing economy (Romer (1986), and Prescott & Boyd (1987)).

At the same time financial globalization (indicated as “FG” in the following parts of the thesis) can be defined as the global linkages in terms of cross-border financial flows which become more and more relevant for developing countries as they are trying to integrate financially into the rest of the world (Yeyati & Williams, 2011). Moreover, the effect of FG is ambiguous as it produces higher growth rates in most of developing countries (Gurgul & Lach, 2014) while it also hurts them with a transmission of crises coming from a highly developed states (Prasad, Rogoff, Wei & Kose, 2003). One of the ways to look at the effects of FG is through their influence on the domestic interest rates as they can be affected by increased competition between the local and foreign banks. Rate of foreign banking ownership is used for this purpose while increasing in its value leads to more

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION competitive local banks and reduced investment costs (Baldwin & Forslid, 2000). Another way to look at FG is via interconnectedness of financial markets. It is widely use measurement for FG and it is important because the share of foreign assets in domestic market can be seen as an additional investment into country which leads to a better capital accumulation and higher rates of economic growth (OECD Publishing, 2010). FG can also stimulate the development of financial sector by allowing for better macroeconomic stability which leads to more stable policies inside the country (Prasad, Rogoff, Wei & Kose, 2003).

At this point, consumption volatility can also be treated as an alternative proxy of FD which can be affected by FG. Economic logic leads to the idea that lower volatility indicates an economy to be more stable. Moreover, developing countries can have a possibility to manage their consumption volatility and output better if they are trying to do it by shifting some of their income risk into the global market (Prasad, Rogoff, Wei & Kose, 2003). It is an important reason because many consumers, as well as countries, are risk-averse and according to consumption theory they are trying to diversify their income risk in terms of using international financial markets. At the same time, all the literature talking about effects of FG can be divided into two groups. First of them claims that FG affects GDP growth and consumption volatility by issuing more efficient international allocation of capital, by stimulating capital deepening and providing international risk-sharing (Prasad, Rogoff, Wei & Kose, 2003). The second group states that traditional channels are no more important and potential collateral benefits (such as financial market development, institutional development, better governance or macroeconomic discipline) play a central role in explaining relationship between FG and GDP growth or consumption volatility. However, the second way of thinking also means that FG leads to better macroeconomic outcomes when some thresholds of potential collateral benefits are met (Prasad, Rogoff, Wei & Kose, 2003).

Overall, in this paper I am going to investigate the effects coming from the interactions between FD and FG as plenty of theoretical arguments were mentioned about it. For empirical purposes I will use similar division of FD into development of banking sector and stock market as it was mentioned by Kandil, Shahbaz and Nasreen (2015). Moreover, I will also apply different types of FG indicators in order to expand the scope of the analysis. The effects coming from interactions between FD and FG variables will be tested by applying advance time series techniques in order to catch the effects between indicators. As the whole idea of FD and FG is already analyzed by the different authors, my contribution to the existing literature will come from the application of CEE data set. Also, most of previous works were analyzing effects between FD and globalization as a broader (socio, economic, political) phenomena, my paper brings additional contribution to the

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION literature by concentrating on a financial part of globalization. Finally, my paper goes deeply in application of different time series techniques in order to find effects between FD and FG while most of previous articles had found effects with an application of panel data techniques such as least- squares (LS) or generalized method of moments (GMM).

The following paper is going to be constructed in such way (see Figure 1.): Section 1 will summarize key ideas how FG can influence FD by describing the core works already made on this topic. Section 2 will introduce the main concepts and variables used in empirical research. Section 3 will describe econometric techniques used for the empirical analysis and also present the basic descriptive statistics. Section 4 will analyze the main findings of this paper given the results from different empirical tests. It will also bring some conclusions and visualization of the results. Finally, Section 5 will be dedicated for the broader discussion of the results and conclusions of the paper.

Motivation

Empirical setup

Econometric methods

Results

Conclusion Figure 1. Flow chart of following chapters

1. Motivation There are many different channels through which FG can affect FD and also vice versa. However, in this chapter I am going to concentrate on the most recent studies which talk about interaction between FG and FD. Therefore, they are as follows:

 One of the earliest attempts to identify the effect of FG on FD was made by Mishkin (2009) when he was trying to explore the relationship between these two phenomena‟s via strengthening financial institutions in an economy. He indicated that the main steps needed to

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strengthen financial institutions are as follows: better property rights, developed legal system, reduced levels of corruption and improved quality of financial information. The final two steps concentrate on improvement of corporate governance and development of supervision of the banking sector as it is the main option in providing credits in developing countries (Mishkin, 2009). Beside all of these steps of strenghtening financial institutions Mishkin (2009) also stated that globalization is the key factor for the poorer developing countries which are still at the beginner level of development as it opens their financial markets for the foreign money flows. In this case, FG increases availability of capital by opening financial markets to foreign capital and improves FD in two ways. Firstly, opened financial markets to foreign money flows directly increases access to additional capital and lowers the loan cost in support of investment in productive investment products. Secondly, opened financial markets promote reforms needed in order to improve functioning of financial system. Finally, FG is an important factor as it can decrease the political power of entrenched local business interests which could probably block reforms of financial institutions. In addition, FG can also stimulate financial deepening by reducing corruption due to decrease in tariffs.  Another important research was made by Garcia and Demetrio (2012) when they were trying to develop the idea of FG impact on FD by applying it in terms of transition countries. Authors argued that financial openness is a very strong tool as it helps to weaken interest groups power by allowing external agents to push financial markets to perform better in order to keep foreign investors attractive in country. Finally, foreign agents also develop financial market by transferring the best financial technologies and the best practices from their original countries to the country of investment (Garcia & Demetrio, 2012). At the same time, FD can take the following advantages from the financial openness: availability of investment information for foreign agents, risk diversification in global markets, pooling of savings and reduced international transaction costs. In terms of empirical findings, Garcia and Demetrio (2012) had used panel data techniques of least-squares (LS) or generalized method of moments (GMM) to test his ideas. Therefore, Garcia and Demetrio found that FG has a stronger impact on FD in countries with better developed and well-functioning financial systems. Finally, authors had found mixed effects between FG and FD that can be explained by the fact that countries from Central Eastern Europe are different from the region of Central Asia. This is the reason as countries from Central Asia tend to have higher tendencies of political instability, corruption and lack of development in terms of institutional quality.

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 The most recent way to investigate effects of globalization and FD was made by Kandil, Shahbaz and Nasreen (2015). As they had not introduced any new theoretical insights, authors were contributing current literature by applying time series techniques in order to investigate effects between FG and FD. By using heterogeneous set of countries (for example, Canada or Sri Lanka) authors expressed FD in two interesting ways: development of banking sector and development of the stock market. Therefore, they came up with the conclusion that FD can affect globalization but not the other way around. Authors were trying to explain this fact by stating that higher globalization can simply relax constraints on external financing which will lead to lower incentives to develop financial system. Moreover, authors also found the positive effect between FD and institutional quality and it supports the basic idea that developing financial system mobilizes efforts to stimulate the growth of quality of financial institutions (Kandil, Shahbaz & Nasreen, 2015).  As previously mentioned studies had shown, capital account openness can be used as a proxy for FG. Moreover, it can also play the key role in explaining cross-country differences in case of development of financial markets (Huang, 2006). This idea was developed more by Baltagi et al. (2009) who were capturing differences across developing and developed countries in terms of FG and FD. The empirical results from Baltagi et al. (2009) paper had shown that FG and trade openness are important and statistically significant variables in explaining different levels of FD. Moreover, extended results had shown that the effect is much stronger in developing countries with a special attention on the banking sector as it seems to be the most influenced by FG. Finally, it can be concluded by saying that the biggest influence of FG on FD is seen through the creation and extension of effective financial institutions.

The idea to choose Central Eastern European (CEE) countries as the core of the analysis comes from the fact that FG is much more effective in terms of developing countries. It is also the reason as in the early stage of growth, developing countries have lack or even no efficient financial institutions which make hard for them to absorb investment flows from abroad and to use this money for a better capital accumulation (Greenwood & Jovanovic, 1990). Moreover, an increase in financial openness creates a necessary environment for a foreign investment to flow into developing country markets by stimulating economic growth. Furthermore, economic growth stimulates the creation and development of new financial institutions which make country even more financially open and attractive for foreign money flows (Greenwood & Jovanovic, 1990).

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Transition countries from post-soviet bloc are the special case as all of them got back their independences after the collapse of Soviet Union. It means that their development of financial markets and financial openness were at the lowest possible level in 1991. Moreover, all CEE countries had pretty similar conditions for the growth and development in the beginning of the tenth decade. However, the current situation shows that financial openness, development of financial markets and rates of economic growth are quite diverged among CEE countries and it makes interesting to analyze the whole problem of this paper in terms of dataset of these countries. There are some papers analyzing interaction between globalization and FD in terms of Asia or South America countries but the idea of CEE countries was not already investigated. Also I have chosen to analyze CEE countries as their whole environment (neighbourhood of developed countries from Western Europe) is pretty similar and the development of financial markets had started at the similar time (Siong, Azman-Saini & Tan, (2014), Kandil, Shahbaz & Nasreen, (2015)).

To summarize, this chapter clearly indicated how FG and FD can interact with each other, and why the data set of CEE countries is the interesting case of this topic. Moreover, the following chapter is going to concentrate on the main concepts and variables used in empirical research. Also it will give a brief graphical understanding of how those indicators have varied during the period of the last few decades.

2. Empirical setup This part of the paper gives some insights about variables which will be used for empirics by drawing clear connection between theoretical ideas and measurements used in this article. As the paper is going to analyze the interaction between two economic phenomena‟s, first of them, FD, will be expressed in terms of variables of stock markets and banking sector. Such type of expression of FD is used by other papers of similar topic (Kandil, Shahbaz & Nasreen, 2015). In terms of development of stock market, paper will use three different variables: value traded (VT), turnover ratio (TR) and stock market capitalization (SMC).

 Value traded is calculated as the amount of stock traded to the number of all shares traded during the year.  Stock market turnover ratio is expressed as the ratio of total shares traded.  Stock market capitalization is counted as the share price times the number of share outstanding.

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At the same time, development of banking sector will be expressed by the following indicators: private sector credit (PC), domestic credit from banking sector (DCB), credit liquid liabilities (LL).

 Private sector credit is going to be analyzed as a transfer of financial resources to private sector through loan or other activities which establish a claim for repayment.  Domestic credit provided by banking sector is calculated as all credits to different sectors on gross basis.  Credit liquid liabilities (M3) is expressed as the sum of currency and deposit in central bank (M0), plus transferable deposit and electronic currency (M1), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus travelers checks, foreign currency time deposits, commercial papers, and shares of mutual funds or market funds held by residents.

Finally, all six variables are counted as shares of real GDP in particular year. The data set for these six variables is taken from World Development Indicators (2015) and World Bank financial structure data set (2015).

The following figures 2, 3, 4, 5, 6 and 7 show the trend of FD explanatory variables during the period of last few decades. From the first two figures which explain FD in banking sector we can see that average private sector credit and average domestic credit from banking sector have similar trends among CEE countries in timeline of 1991 – 2014. The beginning of time period shows clear decrease in these two variables while the later period from 1995 to 2004 indicates about the stagnation of trend. The following dynamics lead to idea that variables were increasing till 2009 when financial crisis happened and dropped a bit afterwards. The trend of the third variable explaining FD in banking sector is a bit different from previous two. Average credit liquid liabilities had quite stable ratio from 1991 to 2001 and it started growing constantly afterwards till 2013. Finally, all three figures indicate that on average FD in banking sector had been grown in CEE countries for the time period from 1995 to 2013.

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Figure 2. Average private sector credit of 12 CEE countries (1991-2014)

Figure 3. Average domestic credit from banking sector in 12 CEE countries (1991-2014)

Figure 4. Average liquid liabilities of 12 CEE countries (1991-2013)

The following figures 5, 6 and 7 show trends of FD in stock markets among CEE countries during the period of 1992 – 2012. Stock market indicators had fluctuated more than banking sector

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION variables. Moreover, figures 5 and 7 showing the dynamics of average stock market capitalization and average stock traded have similar trend lines for the analyzing period. Both of variables had a slow growth from 1991 to 2002 and afterwards, they started growing rapidly till the beginning of current financial crisis. Finally, the latest period of time shows a decrease in both of indicators. Quite different situation can be seen by looking at the average of the third variable indicating FD of stock market. Average stock market turnover ratio had strong variations in the beginning of the analyzing period while the later trend shows a weak constant decrease from 1997 to 2012. Overall, all three figures indicate that variables of FD of stock markets in CEE countries had dropped after the current financial crisis and remained at the low level.

Figure 5. Average Stock market capitalization of 12 CEE countries (1992-2012)

Figure 6. Average Stock market turnover ratio of 12 CEE countries (1992-2013)

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Figure 7. Average stock traded of 12 CEE countries (1992-2013)

Another part of analysis is built around the FG variable which is described by three indicators where each of them expresses the main variable in different ways.

 First from these indicators is financial markets interconnectedness (Inter/GDP) which is also adjusted to the amount of real GDP. Financial interconnectedness is measured in terms of assets owned by foreigners as percentage of real GDP and it shows how the local financial market depends from the foreign investors. It is an important variable as it can capture part of the systematic risk which is one of the key ideas of financial markets (Billio, Getmansky, Lo & Pelizzon, 2011).  Another indicator explaining financial markets interconnectedness is Chinn/Ito index (Chinn/Ito) which was constructed by Chinn and Ito in 2006. Chinn/Ito index uses de jure measures of IMF‟s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) in order to show the number of years for which a country‟s financial capital account has been opened (Ito, 2006). Financial openness division into de jure and de facto measures was first introduced and summarized by Kose et al. (2006). Authors claimed that de jure indicators depends on the legal restrictions and controls on cross-border financial capital flows, while de facto indicators shows countries‟ actual financial integration into the world financial markets through the flow variables (Kose et al., 2006). In this case Chinn/Ito index concentrates on de jure part and explains financial openness with the indicator from 0 to 1, where 1 means the sufficient and maximum openness of financial market.  The last variable which is used in this paper for the explanation of FG is foreign banking ownership as the share of all banks in the country (FBO). It is a widely applicable variable for transition countries as they tend to have relatively high values of foreign banking ownership. Moreover, it shows how the local banking sectors depend from the decisions made by foreign

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banks and from the current economic situation in countries where foreign banks are originally established. Foreign-owned banks are also seen as more cost-efficient and providing better service quality as they mostly tend to be from the more developed countries (Bonin, Hasan & Wachtel, 2005). Finally, foreign banking ownership is calculated as the amount of assets owned by foreign banks in the country as a share of all assets owned by the banks.

The following three figures 8, 9 and 10 show the dynamics of FG indicators. The first variable which is average interconnectedness to GDP ratio had strongly increased during the period from 1991 to 1995. Afterwards, it varied in a small range but kept quite stable trend till 2014. Second indicator is Chinn/Ito index which was increasing from the beginning of analyzing time line till 2007. Later on, it remained stable and showed constant trend till 2013. Explanation of Chinn/Ito index is relatively easy as most of CEE countries had increased their levels of development and globalization after the collapse of Soviet Union. Therefore, most of them had reached high level of Chinn/Ito index till 2007 and kept it stable for the following years. The final indicator explaining FG part is an average foreign banking ownership which was increasing from 2004 till the beginning of current financial crisis and dropped afterwards. Overall, all three variables show some increase in their levels during the analyzing time line which indicates about FG improvement among CEE countries.

Figure 8. Average interconnectedness to GDP ratio of 12 CEE countries (1991-2014)

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Figure 9. Average Chinn/Ito index of 12 CEE countries (1991-2014)

Figure 10. Average foreign banking ownership of 12 CEE countries (2004-2013)

To sum up, this section of the paper gives a brief and concentrated understanding of variables which will be used in the next two empirical parts. Moreover, short descriptions of variables were given by explaining the main ideas of them. Also some explanations were made in order to show how those variables analyze and explain FD and FG differences among countries. Short acronyms for all variables were also identified and written in brackets in order to use them for further empirical analysis. Finally, some figures were presented in order to show trends of all the variables which are used for the following analysis. Next part of the paper is going to present econometric techniques used for the empirical analysis and some basic descriptive statistics of variables.

3. Econometric methods This section starts with a brief methodology of the main time series tests while the second part of it concentrates on the basic descriptive statistics about the variables which are used for the

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION empirical analysis later on. As methodological part is going to talk about the universal technicalities of econometric methods, application mostly uses FG variables as independent ones and FD indicators as dependent variables. However, some tests also use FG and FD in the opposite way. It is done in order to check whether the opposite effects can also exist while the concentration of the paper still remains on FG effect on FD. Most macroeconomic series are not stationary, and for this reason two following sections are going to describe powerful tests used for detection of stationarity. The conclusion from these tests, as shown below, is that most of used series are non-stationary and any estimation method should proceed in first differences in order to come up with robust results.

3.1. IPS panel unit root test Im, Pesaran and Shin (IPS) (2003) introduced a panel unit root test for heterogeneous panel data. The null hypothesis of the test is that all panels have a unit root ( for all i) while the alternative is that fraction of panels are non-stationary. The overall t test statistic of IPS panel unit root is based on the arithmetic mean of all individual Augmented Dickey-Fuller (ADF) statistics from different countries. Let suppose that a series ( can be interpreted by the ADF test without trend.

∑ (1)

Therefore, equation is estimated and the t-statistic for checking is computed. A major restrain of some other panel unit root tests is their limitation about the similar values for all the panels. However, IPS panel unit root relaxes this assumption and allows every panel to have its own

. Moreover, IPS panel unit root test does not require strongly balanced data while it also not allow for any gaps in terms of individual time series.

The IPS panel unit root test allows for the heterogeneity in the value under the alternative hypothesis. This makes the whole test more powerful than other usual time series tests. The estimated t-statistic for IPS panel unit root test is described by the following equation where (

) stands for the t-statistic testing unit roots in individual series i, and let ( ) while

( ) . Then

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̅ ∑ (2)

and

( ̅ ) √ ⇒ (3)

As ̅ is explained in previous equation, values for and ( ) are calculated from the Monte Carlo simulation carried out by IPS panel unit root test. Values are given by using various time periods and lags. Another important characteristic of IPS panel unit root test is that when the error term has a serial correlation in heterogeneous panel and both N and T are relatively large, then, the power of ̅ test is satisfactory. Moreover, the power of IPS test is relatively more affected by increase in T rather than N.

3.2. Phillips-Perron unit root test Phillips-Perron (1988) is an alternative test used to check whether variable has a unit root or not. It uses the null hypothesis claiming that the variable contains a unit root while the alternative stands for the stationarity idea. Moreover, Phillips-Perron uses Newey-West (1987) standard errors in order to account for possible serial autocorrelation while the simple augmented Dickey-Fuller test uses additional lags of the first-differenced variable. In this case, Phillips-Perron test produces two advantages against the simple augmented Dickey-Fuller test. First one state that Phillip-Perron test is robust to general forms of heteroskedasticity in the error term while another advantage stands in line with idea that the user does not have to specify the length of lag for the test regression. Therefore, Phillips-Perron test is calculated in a standard unit root test way:

(4) and

̂ ̂ (5) ̂ where ̂ stands for the estimated individual value of the intercept ( ) while ̂ is the standard error of the estimator ̂ for every individual country ( ).

Finally, as the augmented Dickey-Fuller test uses lags of as regressors in the testing equation, the Phillips-Perron test makes a non-parametric correction to the statistic of t-test.

The following idea of using many different indicators to explain FD and FG has positive and negative impacts on the whole analysis at the same time. Variability of indicators makes the whole

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION analysis better by taking into modeling more information coming from different perspectives. However, it also brings some threaths by making analysis overcrowded and letting some indicators to be correlated. To solve it, principal component analysis (PCA) is applied in order to decrease the number of similar variables by making the analysis more concentrated and uncluttered.

3.3. Principal component analysis (PCA) PCA is applied for this analysis as it let transform possibly correlated variables into linearly uncorrelated principal components. From the technical point of view, PCA is a mathematically defined orthogonal linear transformation that transforms the data to a new kind of coordinate system (Pearson (1901), Hotelling (1933)). This new coordinate system is constructed in such way that the greatest variance by some projection of the data presents the first coordinate (it is called the first component), the second greatest variance is showed on the second coordinate, and so on. Moreover, PCA is used in statistics for the data reduction purpose. The leading eigenvectors from the Eigen decomposition of the correlation or covariance matrix of the variables present a series of uncorrelated linear combinations of the components that contain the most of the variance. Additionally to data reduction process, the eigenvectors from a PCA can also help to learn more about the underlying structure of the using data.

PCA returns eigenvectors in orthogonal and normalized form and computes correlation or covariance matrix from the variables used by the data. Moreover, the main objective of PCA is to find linear combinations of the variables that would represent the most of the variance. Therefore, all principal components together contain the same information as the initial variables. However, all principal components are orthogonal which means that the earlier ones contain more original information than the later ones. Finally, PCA transformation does not mean that the data satisfy some specific statistical model while it also requires the data to be in interval-level as the opposite situation makes linear combinations meaningless.

3.4. Panel vector autoregression (panel VAR) The next step of the analysis of this paper concentrates on a panel vector auto-regression (PVAR) methodology which combines the traditional VAR idea with the panel data approach (Love & Zicchino, 2006). PVAR is one of the core parts of this empirical research as it shows interactions between FG and FD. PVAR method fits a multivariate panel regression of every dependent variable of lags of itself and on lags of other dependent variables by using approach of generalized methods of moments (GMM). In this case, traditional VAR approach allows treating all variables as endogenous, while additional panel data technique let having unobserved individual heterogeneity.

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The simple first-order VAR model which uses fixed-effects can be described by the following equation:

(6)

where is a vector of four endogenous variables for a particular country i and year t. Moreover, is a fixed effect variable used in order to capture country specific differences while tends to be a multivariate vector of a white-noise error terms. According to the suggestion of Love and Zicchino (2006), each variable of our VAR analysis is demeaned which means that for every time period I compute the mean of variables of particular panel and substract that mean from the series. This is helpful to do as it eliminates the time-specific effects (Levin et. al, 2002). Moreover, some other problems can come from the fixed-effects as they tend to be correlated with the regressors due to the lagged variables. In this case, I use the advanced mean differencing (the Helmert procedure) in order to remove fixed-effects (Love & Zicchino, 2006). Under this ̅ transformation all variables are modeled as deviations from the forward means. Let

∑ calculates the mean obtained from the future values of , which is a variable in the

vector. In this case stands for the vector , where shows the last available time period of our data. For the ̅ I use the same transformation of , where

. In this case, final equations look as follows:

̃ ̅ (7)

̃ ̅ (8)

In the last two equations √ . The final transformed equation of PVAR model is constructed in the following manner:

̃ ̃ ̃ (9)

̃ where ̃ ̃ ̃ and ̃ ̃ ̃ ̃ . In this transformation each observation is expressed as a deviation from the average value of future observation. If the original errors are not autocorrelated then the transformed ones should give similar results. Finally, PVAR

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION technique allows me to use lagged values of regressors as instruments for the GMM estimation in order to get final coefficients (Love & Zicchino, 2006).

3.5. Granger causality The Granger causality test is useful in order to check whether one time series can be used in forecasting another or not (Granger, 1969). In this paper, it tries to identify whether FG Granger causes some impact on FD or not. The key idea of the test claims that independent variable (X) Granger causes dependent (Y) one if Y can be better explained by the historical data of X and Y instead of using historical data of Y only. A common idea of Granger causality is to regress dependent variable on its lagged values and on lagged values of other independent variables. Afterwards, test checks the null hypothesis claiming that the estimated coefficients on the lagged values of independent variables are jointly equal to zero while alternative hypothesis states that it is not. This means that the failure to reject the null hypothesis leads to the final idea that changes in independent variables do not Granger-cause changes in the dependent variable.

Granger causality tests are commonly used in the context of vector autoregressions (VAR) by using individual equations within VAR systems. Individual equations in VAR systems are better known as autoregressive distributed lag (ADL) relationships which can be explained by the following equation:

∑ ∑ (10) and

where and stands for the analyzing variables while corresponds to other indicators which are needed to be controlled for. The null hypothesis of test claims that does not Granger cause changes in by testing whether for . If is found to be non-zero than it is possible to state that Granger causes effects on . Moreover, the test statistic is calculated by using the sum of squared residuals (RSS) of the unrestricted equation above (10) and restricted equation:

∑ (11)

Therefore, test statistic is found by using the following formula for joint-significance tests:

(12)

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which is distributed as an F ( ) variable. Finally, and are the residual sum of squares of the restricted and unrestricted regressions.

3.6. Common correlated effect (CCE) mean group estimator Common correlated effect mean group estimator was first introduced by the Pesaran (2006). It is a useful technique in order to check for the possible connection between shocks in different cross-sections. CCE estimator is constructed by the following model: for (“group”, countries in this case) and (time, years are used for this analysis),

(13)

(14)

(15)

where and are observables, is the country-specific slope on the observable regressors, and

stands for the unobservables while presents the error term. The unobservables in (14) are made up of standard group-specific fixed effects , which try to capture time-invariant heterogeneity across groups (countries). Moreover, unobserved common factor with heterogeneous factor loadings are important in capturing time-variant heterogeneity and cross- section dependence. Factors and are flexible and not limited to linear evolution over time as they can be nonlinear and even non-stationary. However, some problems can arise if regressors are driven by the same common factors as the observables: the presence of in equations (14) and (15) indicates that some possible endogeneity can be found in the estimation equation and this problem was deeper discussed by Coakley, Fuertes and Smith (2006). In order to deal with this problem, Pesaran (2006) presented estimator with a simple but powerful augmentation of the group-specific regression equation: instead of and an intercept, CCE estimator uses cross-section averages of the dependent and independent variables as additional regressors. The combination of those averages can account for the unobserved common factor while the heterogeneous impact is also given. The coefficients are averaged across the panel members, when different weights are applied.

Additionally, it is interesting to mention that, in general, in the equation 13 can also be extended by including lags of both, dependent and independent variables (Pesaran & Chudik, 2013). However, for this paper I am applying original Pesaran (2006) approach of CCE mean group estimator without any lags included in the main equation (13). This is the reason as the time-series of the data of this paper is too small for the extended version of CEE group mean estimator to include lags. Finally, the

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION equations 13-15 produces the situation where only one covariate and one unobserved common factor is used in the estimation equation while way more of them can be used in application of CCE estimation technique for the real data.

3.7. Descriptive statistics This part of the chapter is going to present numbers of basic descriptive statistics which can be seen in the Table 1 below. Statistics show that value traded varies in our sample from almost to 0 to 34 percentages of GDP. At the same time, the mean of CEE countries holds at 4.782 percentages of GDP but it also has quite large variation as standard deviation is higher than the mean (almost equal to 6). In terms of turnover ratio and stock market capitalization we can see that standard deviations of these variables are also close to their means while the gap between minimum and maximum values are really large (198% and 80% for both variables respectively). Other three variables explaining FD (private sector credit, domestic credit from banking sector and credit liquid liabilities) show better proportions of the mean and standard deviation. For all of them means stand almost twice bigger than standard deviations. However, in terms of the lowest and the highest values all of these variables show similarly large gaps as previous three indicators. Two variables explaining FG part (Chinn/Ito index and foreign banking ownership) show similar pattern to previous variables: means are few times higher than standard deviations while gaps between minimum and maximum values are significant as well. A bit different situation is seen for interconnectedness to GDP ratio as its mean is few times lower than standard deviation. However, the gap between the lowest and the highest values of the variable still seems to be significantly large. Finally, such huge gaps between minimum and maximum values confirm the idea that most variables have been significantly changed during the time period of analysis.

Table 1. Descriptive statistics of variables Variables Mean SD Min Max

4.782 5.992 .0003 33.9994

32.054 33.862 .123 198.244

16.182 12.446 .026 80.065

36.765 22.874 1.126 135.964

49.194 26.116 1.677 133.113

46.771 17.387 14.513 84.561

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.0910 .2040 -1.523 .4212

.6440 .3354 0 1

.76421 .20011 .21 1

To conclude, this section explains the methodology of time series tests which is used for the later analysis. It also presents basic descriptive statistics in order to create better idea about the variables. Finally, next chapters of this paper concentrate on the results of time series tests and some conclusions built on them.

4. Results This chapter of the thesis presents the main results given from the time series tests. It starts with the results of IPS panel unit root test which are compared to individual countries results from Phillips-Perron unit root test. Afterwards, it uses principal component analysis (PCA) in order to decrease the number of variables which are later applied to the panel vector autoregressive analysis (PVAR). Granger causalities for individual countries data are also checked in order to test for relationship between FG and FD. Finally, chapter finishes with findings from common correlated effects mean group estimator (CEE) presented by Pesaran (2006) which checks for the potential connection between shocks in different countries.

4.1. Panel IPS and Phillips-Perron unit root tests The final results of IPS panel unit root test are shown in the Table 2. Table presents results calculated with the intercept and without trend. Moreover, all variables are tested in levels as well as in first differences. IPS panel unit root test uses null hypothesis claiming that all panels contain unit roots while alternative hypothesis stands for the idea that some panels are stationary. Empirical results from IPS panel unit root test suggest that most of variables are non-stationary in their level forms while turnover ratio, value traded and foreign banking ownership seem to be stationary. However, all variables seem to be stationary at the IPS panel unit root analysis of first differences. Lastly, it leads to the idea that variables in first differences should be used for the further analysis in order to have fully stationary data which will present a better estimation results.

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Table 2. Results from IPS panel unit root test Variables At level At first difference Drift & no p value Drift & no p value trend trend

-1.9980 0.0229 -6.5191 0.0000

-2.6095 0.0045 -7.6177 0.0000

-0.0763 0.4696 -4.0862 0.0000

1.7569 0.9605 -4.3920 0.0000

1.1756 0.8801 -5.6683 0.0000

4.3621 1.0000 -5.2045 0.0000

-0.3737 0.3543 -5.5734 0.0000

-0.3144 0.3766 -6.7571 0.0000

-1.9802 0.0238 -4.7343 0.0000

Alternatively, Phillips-Perron unit root test for individual countries is also made in order to re-check stationarity of all variables. Phillips-Perron unit root test uses the null hypothesis claiming that variables contain unit roots while alternative hypothesis claims that they are stationary. Final results from Phillips-Perron test suggest that most of variables are non-stationary while the same value traded; turnover ratio and foreign banking ownership variables seem to be stationary for some countries. Moreover, Chinn/Ito index also can be seen as a stationary in cases of Estonia or Latvia but the results for those countries are not reliable as their time series have only little of variation in time. Further Phillips-Perron analysis confirms the idea of stationarity and shows that all variables are stationary in their first differences. Only few variables show slight possibility to be non- stationary under first differences but they have significance levels close to 5% which still let them keep as stationary. Finally, results in levels seem to be similar and indicate about non-stationarity for most of the variables in terms of different unit root tests. At the same time, findings from first differences produce the final outcomes that all variables are stationarity.

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Table 3. Phillips-Perron unit root test for individual countries in levels, (p-values)

Albania - - - 0.9281 0.8023 0.5191 0.8650 0.3372 0.7799 Bulgaria 0.0511 0.2571 0.4980 0.3772 0.2949 0.8520 0.2481 0.8630 0.8923 Croatia 0.3793 0.2848 0.4993 0.7967 0.9701 0.3334 0.0919 0.6773 0.0000 Czech R. 0.2458 0.2246 0.4136 0.5767 0.8488 0.9832 0.1441 0.3175 0.3161 Estonia 0.0000 0.0347 0.7324 0.5392 0.4877 0.9369 0.1126 0.0000* 0.0161 Hungary 0.6185 0.1964 0.3079 0.7567 0.3165 0.8889 0.6529 0.4304 0.9369 Latvia 0.0941 0.3636 0.3729 0.6532 0.6787 0.9802 0.6206 0.0323* 0.1598 Lithuania 0.3884 0.1644 0.3724 0.5049 0.4977 0.9423 0.5293 0.9928 0.0133 Poland 0.2949 0.4675 0.6877 0.9390 0.9799 0.9911 0.4726 0.3502 0.0772 Romania 0.3124 0.1572 0.6052 0.8061 0.0690 0.2402 0.3415 0.8333 0.0199 Slovakia 0.2887 0.7557 0.3314 0.2106 0.1675 0.8221 0.2528 0.8036 0.0807 Slovenia 0.1026 0.0825 0.4884 0.6184 0.6641 0.4965 0.6429 0.3281 0.0000 *values are not reliable as they have only little of variation in time

Table 4. Phillips-Perron unit root test for individual countries in first differences, (p-values)

Albania - - - 0.0469 0.0008 0.0000 0.0348 0.0022 0.0162 Bulgaria 0.0000 0.0000 0.0232 0.0000 0.0000 0.0452 0.0051 0.0070 0.0354 Croatia 0.0217 0.0003 0.0404 0.0032 0.0007 0.0327 0.0003 0.0014 0.0000 Czech R. 0.0001 0.0001 0.0162 0.0459 0.0480 0.0485 0.0009 0.0427 0.0217 Estonia 0.0000 0.0000 0.0480 0.0002 0.0003 0.0114 0.0112 0.0000 0.0000 Hungary 0.0245 0.0076 0.0390 0.2562 0.0138 0.0390 0.0031 0.0001 0.0000 Latvia 0.0001 0.0000 0.0461 0.0034 0.0509 0.0061 0.0345 0.0000 0.0317 Lithuania 0.0007 0.0000 0.0207 0.0553 0.0347 0.0128 0.0067 0.0433 0.0000 Poland 0.0000 0.0000 0.0031 0.0057 0.0004 0.0469 0.0227 0.0001 0.0145 Romania 0.0001 0.0047 0.0427 0.0221 0.0000 0.0009 0.0197 0.0000 0.0252 Slovakia 0.0470 0.0000 0.0576 0.0002 0.0003 0.0378 0.0112 0.0105 0.0000 Slovenia 0.0000 0.0000 0.0414 0.0314 0.0554 0.0326 0.0557 0.0332 0.0000 Note: bolded values are close to 5% in order to be able to reject the null hypotheses.

4.2. Principal Component Analysis (PCA) Results of different unit root tests show the idea that most of variables are non-stationary in their levels while situation is opposite in their first differences. According to this conclusion, empirical part continues by using variables in first differences for further tests. Moreover, as the analysis uses 6 variables to describe FD part and 3 other indicators to analyze FG side I run principal component analysis (PCA) in order to decrease the number of variables and the possibility for some

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION of them to be correlated (Jolliffe, 2002). Results of PCA for 6 FD variables are presented in the Table 5 below. It shows that first component can explain more than 36% of the data, second almost 28% of it, while the last two explain just 6% and 0.9% respectively. It means that the first four out of six components explains more than 93% of real data. This conclusion let drop the last two components from the analysis by the decreasing number of variables from 6 to 4 but making all of them uncorrelated and still representing most of the real data. At the same time, the second part of Table 5 shows how each initial indicator is represented by every new component variable. It shows that the first and the second components simply differentiate banking sector and stock market variables while the other components are much more complexed and mixed. To be more precise, higher values of the first component identify banking sector variables (d_PC, d_DCB, d_LL) while lower values stand for the stock market indicators (d_SMC, d_TR, d_VT). The opposite situation is with the second component where the higher values of it stand for the stock market variables and lower (or even negative) values for the banking sector.

Table 5. Principal component analysis (PCA) for FD variables Component Eigenvalue Difference Proportion Cummulative Comp1 2.20109 .522643 0.3668 0.3668 Comp2 1.67845 .696455 0.2797 0.6466 Comp3 .981993 .25762 0.1637 0.8103 Comp4 .724373 .365231 0.1207 0.9310 Comp5 .359142 .30419 0.0599 0.9908 Comp6 .0549518 . 0.0092 1.000

Variable Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 d_PC 0.6276 -0.0304 -0.1607 -0.3334 -0.0013 -0.6843 d_DCB 0.6457 -0.0846 -0.0899 -0.2154 0.0172 0.7219 d_LL 0.4236 -0.0173 0.2812 0.8487 -0.1127 -0.0900 d_SMC 0.0287 0.5010 -0.6695 0.2824 0.4686 0.0228 d_TR 0.0895 0.5078 0.6623 -0.1866 0.5105 -0.0060 d_VT 0.0305 0.6948 -0.0122 -0.0869 -0.7118 0.0437

Also the results of PCA for FG variables are presented in the Table 6 below. It shows that the first component explains 40%, second a bit more than 34% while the third component less than 26% of original data. This means that the first two components can explain more that 74% of initial data and it leads to the idea that the third component can be dropped in order to decrease the number of variables. It also decreases the possibility of variables to be correlated. Finally, the second part of the Table 6 shows how the new principal components represent the initial FG variables.

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Table 6. Principal component analysis (PCA) for FG variables Component Eigenvalue Difference Proportion Cummulative Comp1 1.21028 .183464 0.4034 0.4034 Comp2 1.02682 .263923 0.3423 0.7457 Comp3 .762897 . 0.2543 1.000

Variable Comp1 Comp2 Comp3 d_InterGDP -0.6943 0.2849 0.6609 d_ChinnIto 0.0771 0.9425 -0.3252 d_FBO 0.7155 0.1748 0.6764

The results from PCA shows that there is a possibility to decrease the number of original indicators by making new variables uncorrelated and still explaining most of the initial data. Moreover, I drop some components and continue analysis with only first four of them explaining FD part and with the first two components from FG part.

4.3. Panel vector autoregression (VAR) estimation This section of the paper concentrates on the panel VAR estimation in order to check for linear interdependencies between multiple panels. Table of PVAR estimation shows all the interactions between variables of analysis and significances of those relationships. For PVAR estimation I use variables given by PCA: pc1_d, pc2_d, pc3_d and pc4_d indicate first four components of FD while pc1_g and pc2_g describe first two components of FG. Firstly, PVAR is used in order to test relationships among pc1_d, pc1_g, pc2_g and their lagged values. Therefore, results of panel VAR estimation for those variables are presented in Table 7. It shows that lagged value of the first component of FG has a significant positive effect on FD variable while pretty similar effect is also seen in the other way around. At the same time, the second component of FG also indicates about the positive significant effect on FD but not the other way around. Additional PVAR estimation for 2 and 3 lags (Tables A1 and A2) find the similar conclusion that first lags of FG are positively significant on FD and only the second component of FG shows ambiguous results on FD when it uses 3 lags for PVAR estimation.

Table 7. PVAR(1) estimation based on pc1_d, pc1_g, pc2_g variables pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) .212512 0.009 .166754 0.049 .0447789 0.545 pc1_g (L1) .188579 0.006 .280899 0.062 .1475636 0.088 pc2_g (L1) .190415 0.027 .256702 0.020 .2259857 0.170

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Another PVAR estimation is made by using the same both components of FG and the second components of FD. Results of the analysis with 1 lag are described in the Table 8 below. It shows relationship between the first component of FG and the second component of FD which produces a positive significant impact on FG indicator but not the vice versa. However, results become insignificant by expanding PVAR estimation for 2 and 3 lags (Tables A3 and A4). At the same time, findings from the Table 8 also indicate that the second component of FG has a positive impact on FD and not the other way around. Moreover, results keep similar even by expanding PVAR analysis with 2 or 3 lags (Tables A3 and A4).

Table 8. PVAR(1) estimation based on pc2_d, pc1_g, pc2_g variables pc2_d pc1_g pc2_g Coefficients p-values coefficients p-values coefficients p-values pc2_d (L1) -.096159 0.549 .168839 0.008 .027495 0.590 pc1_g (L1) -.128008 0.358 .270141 0.049 .141195 0.075 pc2_g (L1) .591753 0.000 .201576 0.020 .216781 0.167

PVAR analysis is run further by applying it for the third and fourth components of FD but the results stay pretty similar by showing second FG component impact on FD or no other relationships between FG and FD (Tables A5, A6, A7, A8, A9 and A10).

To conclude this chapter I could say that PVAR estimation with different number of lags shows that the second component of FG has a significant positive effect on FD in most of the cases. Moreover, the opposite relationship was found only in one case of PVAR analysis by indicating about the possible FD effect on FG.

4.4. Granger causality test and common correlated effects (CCE) estimation

Granger causality

The following part of this chapter is used to run countries specific Granger causality tests for the original variables. In the analysis I am using country specific Granger causality tests as the data set is relatively short and sensitive for unexpected deviations in values. However, results are going to be highly significant as they are found from the sensitive country specific tests. The main null hypothesis of Granger test states that FG does not Granger cause FD while the alternative hypothesis claims that it does. Moreover, final results are presented in the Tables A9-A20. They show that domestic credit provided by banks is significantly Granger caused by interconnectedness or foreign banking ownership in 8 out of 12 cross-sections (countries). Similar situation happens with a private

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION sector credit variable which is also significantly Granger caused by interconnectedness or foreign banking ownership variables in 8 out of 12 cross-sections. Even better situation is seen in terms of liquid liabilities variable which is significantly related and caused by the same interconnectedness or foreign banking ownership variables in 11 out of 12 countries. At the same time, Chinn/Ito index shows less significant effects on FD variables from banking sector. Only for the few countries it became statistically significant in terms of Granger causality.

Situation does not change heavily if stock market variables are checked as proxies for FD. Results show that stock market capitalization is significantly Granger caused by interconnectedness or foreign banking ownership indicators in 10 out of 11 countries (Albania does not present any statistics about the stock market variables). Turnover ratio and value traded are also nicely presented by the same two FG indicators. First one is Granger caused in 8 out of 11 cross-sections and the second in 7 out of 11 countries by using interconnectedness or foreign banking ownership as explanatory variables. Moreover, Chinn/Ito index does not present stock market variables in the best way as it shows effects for fewer countries than the other two indicators of FG. Finally, Granger causality tests indicate that for the most cross-sections FG has a Granger causal effect on FD variables if interconnectedness or foreign banking ownership indicators are used as proxies for FG.

CCE estimator

The last part of empirical analysis is constructed on common correlated effects estimator (CCE) in order to see if there is a connection between shocks in different countries. CCE estimator is constructed in such way that it analyzes different principal components of FD separately based on components of FG side. The finals results of two different CCE estimations are presented in the Tables 11 and 12 below. The first CCE estimator based on the first component of FD and two FG components (Table 11) shows that second principal component of FG has a significant negative effect on FD while the other indicators and unobserved common factors do not. This negative effect on the first FD component means that FG affects development of stock market way stronger than the banking sector. The second CCE estimation presented by the Table 12 indicates that FG has way less impact in FD than in previous estimation. However, this time unobserved common factor of FD is seen as positively significant on FD itself. This means that inside-country shocks are connected between different cross-sections (countries) and they positively affect dynamics of FD. To be more precise, unexpected FD shocks in some of the countries positively affect FD itself in terms of all countries. Further analysis is repeated by using third and fourth components of FD instead of the first and the second but the findings are the same (Appendix C).

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Table 9. Common Correlated Effects Mean Group estimator (pc1_d, pc1_g, pc2_g) pc1_d Coef. p-value [95% confidence interval] pc1_g .378866 0.325 -.374936 1.13267 pc2_g -.844244 0.004 -1.41963 -.268857 pc1_d_avg .732155 0.363 -.844072 2.30838 pc1_g_avg -.186377 0.771 -1.44104 1.06828 pc2_g_avg .309781 0.496 -.582209 1.20177 _cons .008156 0.987 -1.00831 1.02462 *pc1_d is used as dependent and pc1_g, pc2_g as independent variables

Table 10. Common Correlated Effects Mean Group estimator (pc2_d, pc1_g, pc2_g) pc2_d Coef. p-value [95% confidence interval] pc1_g -.253579 0.767 -1.92736 1.42020 pc2_g -1.71634 0.218 -4.44763 1.01495 pc2_d_avg .979611 0.007 .264286 1.69494 pc1_g_avg -.326082 0.597 -1.53514 .882975 pc2_g_avg 1.22484 0.213 -.703223 3.15290 _cons -.465261 0.267 -1.28713 .356606 *pc2_d is used as dependent and pc1_g, pc2_g as independent variables

To sum up, this chapter summarizes all the time series tests which were used in the paper and gives some conclusions made from the results. Empirical findings show that all the variables are stationary in their first differences. Moreover, PCA was made in order to decrease FD variables number to 4 and FG to 2. At last, time series findings from PVAR test, Granger causality and CCE estimation claim that FG impacts FD variables while the same effects hardly work in the opposite way. At the same time, the connection between FD shocks was found in case of different cross- sections. Finally, the last part of the paper is dedicated for the discussion of results in terms of combining them with the economic intuition mentioned in the literature review.

5. Conclusion The last part of this paper is based on the discussion of results presenting some possible ideas which can be implemented by the political authorities. Some results from empirical part indicate that ambiguous effects can be seen by analyzing interactions between FD and FG while the most of them still state that FG is an important factor which positively influences FD. This idea was found by the panel VAR estimation which showed positive FG effects on FD in most of cases by applying different variables given from PCA analysis. At the same time, the opposite effect was found only in few PVAR cases and even disappeared by expanding analysis for the higher number of lags. Similar results that FD can influence globalization and not the other way around were found by Kandil, Shahbaz and Nasreen (2015). In that case, they claimed that higher globalization can simply relax

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION constraints on external financing which will lead to lower incentives to develop financial system (Kandil, Shahbaz & Nasreen, 2015). However, such effect was found only in few cases which let it seen as an exception instead of tendency.

Moreover, Granger causality test were also used in order to test and expand the results given from the panel VAR estimation. Granger causality results show that FG variables have significant effects on FD in most of separate countries„ data sets. Furthermore, this Granger causal effect was way stronger when interconnectedness or foreign banking ownership variables were used as proxies for FG. Finally, common correlated effects estimator was used for the final check of relationships between FG and FD. Results of CCE estimator show that shocks in FG variables have ambiguous and unclear effects. At the same time, positive effects from the inside-country FD shocks were found by analyzing effect on FD itself. This means that shock in one or few countries in FD leads to a spread of FD in the whole region of CEE countries. At the same time, CCE estimator also came up with the results showing that FG affects development of stock market way stronger than the banking sector. To sum up, we can conclude by saying that most of results stand for the idea that FG has a positive effect on FD in the region of CEE countries.

Finding of a positive FG effect on FD should also be combined with the main theoretical ideas in order to clearly answer the main question of this research – to find and to explain interactions between FD and FG in terms of CEE countries. In his article Mishkin (2009) states that FG is the first option for the poorly financially developed countries in order to stimulate FD and have significantly high levels of economic growth. This idea seems to work perfectly in terms of CEE countries which had very low levels of FD in the end of twentieth century. Positive FG effects on FD from the empirical part indicate that increased levels in property rights, better developed legal system, reduced levels of corruption and improved quality of financial information let CEE countries became competitive and well-developed states of Europe. Moreover, most of CEE countries already became the members of EU which leads to even better levels of FG. Furthermore, there are still some rooms for improvement for CEE countries in terms of better corporate governance of different institutions and better development of supervision of the banking sector as it is the main option in providing credits in developing countries (Mishkin, 2009). Finally, political authorities should keep FG as a primary tool of action if development of stock market belongs to their agenda. At the same time, development of banking sector can also lead to a higher level of FG as small part of results also stand for this idea.

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To sum up, this research investigated interactions between FG and FD in terms of 12 CEE countries during the last few decades. From the theoretical point of view, most of researhcers were saying that both phenomenas, FG and FD, are crucial for the significant growth of developing economies. Moreover, there was no clear agreement how they both interact while the most of researchers were still arguing that an increase in FG should lead to better levels of FD. The empirical part based on different time series techniques mostly confirmed this idea by finding positive FG effects on FD in terms of 12 developing CEE countries. Finally, these results can be seen as a great work made by CEE countries‟ political authorities during the period of the last few decades. At the same time, they should continue their work by improving crucial FG parts as property rights, legal system, and levels of corruption, quality of financial information, corporate governance and better supervision of banking system. By constantly working on these FG parts, CEE countries can improve their FD and reach the economic levels which will be much closer to the one of highly developed economies.

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Appendix A

Table A1. PVAR(2) based on pc1_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) .215096 0.004 .121814 0.180 -.021056 0.588 (L2) .136782 0.064 .084347 0.564 .013747 0.416 pc1_g (L1) .175893 0.016 .149535 0.129 .086112 0.011 (L2) .000449 0.995 .029494 0.727 .015081 0.633 pc2_g (L1) .215547 0.001 .197759 0.000 .093444 0.000 (L2) .085601 0.446 .272621 0.000 -.071737 0.000

Table A2. PVAR(3) based on pc1_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) -.229836 0.078 .233926 0.000 -.025517 0.106 (L2) -.144259 0.444 .092288 0.264 -.003501 0.766 (L3) -.526238 0.000 -.068958 0.215 -.009330 0.434 pc1_g (L1) .460923 0.200 .069168 0.718 -.079653 0.109 (L2) .296392 0.067 -.161915 0.165 .015104 0.641 (L3) .231596 0.030 -.081886 0.154 -.051941 0.001 pc2_g (L1) -2.50540 0.055 1.93786 0.003 -.270569 0.159 (L2) .347275 0.039 .073793 0.375 -.012224 0.549 (L3) -.214955 0.111 .179299 0.017 .011909 0.615

Table A3. PVAR(2) based on pc2_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) -.054786 0.799 .100205 0.132 .010257 0.742 (L2) -.05476 0.715 -.006187 0.919 -.001237 0.949 pc1_g (L1) -.085772 0.508 .152813 0.086 .102239 0.016 (L2) .176885 0.429 .029362 0.752 .007118 0.818 pc2_g (L1) .534701 0.000 .161248 0.000 .089804 0.000 (L2) -.354128 0.016 .242832 0.000 -.084737 0.000

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION

Table A4. PVAR(3) based on pc2_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) -.450594 0.002 .126326 0.220 -.024127 0.224 (L2) -.304897 0.014 -.014363 0.852 .014623 0.404 (L3) -.086621 0.209 .074473 0.153 -.029235 0.010 pc1_g (L1) -.021491 0.929 -.003246 0.989 -.094474 0.030 (L2) -.153569 0.166 -.151848 0.242 .035553 0.207 (L3) -.033414 0.634 -.057255 0.257 -.048828 0.000 pc2_g (L1) 1.16796 0.076 1.12209 0.087 -.254233 0.191 (L2) -.217635 0.008 .159119 0.078 -.007249 0.650 (L3) -.086004 0.469 .253047 0.001 .000404 0.985

Table A5. PVAR(1) estimation based on pc3_d, pc1_g, pc2_g

pc3_d pc1_g pc2_g Coefficients p-values coefficients p-values coefficients p-values pc3_d (L1) .055644 0.688 -.095600 0.189 .058606 0.356 pc1_g (L1) .108246 0.410 .220429 0.071 .146123 0.050 pc2_g (L1) -.245731 0.010 .238636 0.029 .235162 0.169

Table A6. PVAR(2) based on pc3_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) .159492 0.347 -.045841 0.571 -.005551 0.882 (L2) -.243426 0.042 .056293 0.404 -.029006 0.177 pc1_g (L1) .146087 0.326 .122159 0.114 .087979 0.014 (L2) -.288365 0.203 .039438 0.668 .003832 0.905 pc2_g (L1) -.179784 0.004 .175841 0.000 .093795 0.000 (L2) -.088392 0.164 .303251 0.000 -.083838 0.000

Table A7. PVAR(3) based on pc3_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) -.154616 0.047 .025501 0.859 -.008008 0.824 (L2) -.198166 0.109 .071168 0.311 -.021001 0.420 (L3) -.059635 0.272 .027007 0.753 .006724 0.749 pc1_g (L1) .706564 0.003 -.046627 0.822 -.081385 0.074 (L2) -.061330 0.651 -.075836 0.565 .006988 0.825 (L3) -.081465 0.338 -.049870 0.487 -.044820 0.000 pc2_g (L1) -.816125 0.152 .940723 0.185 -.195710 0.338 (L2) -.158047 0.055 .265244 0.002 -.030617 0.101 (L3) -.153754 0.039 .230981 0.019 .009548 0.724

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION

Table 8. PVAR (1) estimation based on pc4_d, pc1_g, pc2_g

pc4_d pc1_g pc2_g Coefficients p-values coefficients p-values coefficients p-values pc4_d (L1) .02491 0.836 -.063590 0.727 .103946 0.276 pc1_g (L1) -.085421 0.044 .23876 0.067 .133307 0.050 pc2_g (L1) .038952 0.170 .262927 0.013 .211711 0.185

Table A9. PVAR(2) based on pc4_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) .037069 0.789 .083825 0.580 .039297 0.636 (L2) -.308789 0.001 -.117422 0.396 -.002521 0.952 pc1_g (L1) -.096420 0.013 .110875 0.117 .098278 0.004 (L2) .023160 0.806 .034025 0.714 .011310 0.685 pc2_g (L1) .044689 0.054 .182278 0.000 .087413 0.000 (L2) .05827 0.214 .318935 0.000 -.078750 0.000

Table A10. PVAR(3) based on pc4_d, pc1_g, pc2_g

pc1_d pc1_g pc2_g coefficients p-values coefficients p-values coefficients p-values pc1_d (L1) -.313462 0.011 -.1225561 0.600 -.019974 0.687 (L2) -.362409 0.000 -.1873698 0.350 .044631 0.151 (L3) -.318238 0.001 -.2513107 0.114 -.001940 0.946 pc1_g (L1) -.174117 0.017 -.1676647 0.415 -.0609543 0.174 (L2) -.055860 0.371 -.121026 0.379 .0040421 0.906 (L3) -.025871 0.627 -.0662948 0.360 -.043656 0.003 pc2_g (L1) .043814 0.851 1.116489 0.093 -.201064 0.305 (L2) .114275 0.000 .2912635 0.001 -.032412 0.104 (L3) .093446 0.048 .298489 0.005 .006808 0.788

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Appendix B

Table A9. Granger causality test for Albania (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.0248 0.1237 0.0376 - - - d_ChinnIto 0.0000 0.7184 0.6618 - - - d_FBO 0.0000 0.0000 0.3621 - - -

Table A10. Granger causality test for Bulgaria (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.0062 0.0093 0.0277 0.0006 0.0164 0.0636 d_ChinnIto 0.6688 0.7763 0.5595 0.4475 0.0101 0.7508 d_FBO 0.0036 0.0529 0.0000 0.0000 0.0000 0.1013

Table A11. Granger causality test for Croatia (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.0049 0.0089 0.0003 0.2815 0.0123 0.9714 d_ChinnIto 0.0336 0.0866 0.2784 0.9282 0.7012 0.9337 d_FBO 0.0000 0.1613 0.9277 0.0782 0.0000 0.0222

Table A12. Granger causality test for Czech Republic (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.7757 0.0447 0.1230 0.0009 0.0173 0.4776 d_ChinnIto 0.0721 0.0064 0.9670 0.8432 0.0755 0.1469 d_FBO 0.1446 0.0008 0.2812 0.0411 0.3139 0.4414

Table A13. Granger causality test for Estonia (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.7980 0.7773 0.7111 0.0043 0.0093 0.0003 d_ChinnIto 0.4691 0.5385 0.8622 - - - d_FBO 0.0253 0.0426 0.0006 0.0044 0.3858 0.0832

Table A14. Granger causality test for Hungary (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.1350 0.0010 0.0007 0.1822 0.1931 0.0000 d_ChinnIto 0.9365 0.5764 0.2190 0.1099 0.0719 0.0000 d_FBO 0.2570 0.0516 0.9893 0.0068 0.0944 0.0026

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION

Table A15. Granger causality test for Latvia (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.6165 0.9528 0.0238 0.0027 0.1359 0.0000 d_ChinnIto 0.9867 0.8745 0.6969 0.8411 0.4705 0.1561 d_FBO 0.0000 0.0035 0.0181 0.3608 0.2047 0.1482

Table A16. Granger causality test for Lithuania (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.0009 0.0032 0.3521 0.0028 0.5788 0.0325 d_ChinnIto 0.2842 0.4419 0.4382 0.1688 0.1852 0.0000 d_FBO 0.0133 0.0109 0.0100 0.2222 0.1720 0.0000

Table A17. Granger causality test for Poland (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.3766 0.8181 0.6784 0.0269 0.0485 0.3461 d_ChinnIto 0.2322 0.3912 0.2374 0.6705 0.0000 0.7076 d_FBO 0.2732 0.0913 0.0000 0.0774 0.2803 0.0796

Table A18. Granger causality test for Romania (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.0034 0.7658 0.9797 0.0238 0.0325 0.0005 d_ChinnIto 0.0000 0.0015 0.0509 0.0000 0.0000 0.0000 d_FBO 0.2618 0.7319 0.0056 0.0013 0.0000 0.0919

Table A19. Granger causality test for Slovakia (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.2820 0.6951 0.0000 0.0051 0.0960 0.7096 d_ChinnIto 0.7322 0.2709 0.0000 0.1601 0.1721 0.9907 d_FBO 0.8863 0.7805 0.3838 0.0000 0.0000 0.9593

Table A20. Granger causality test for Slovenia (p-values)

d_PC d_DCB d_LL d_SMC d_TR d_VT d_InterGDP 0.0250 0.1225 0.7462 0.0189 0.1389 0.0615 d_ChinnIto 0.7425 0.8460 0.1622 0.5000 0.6922 0.9729 d_FBO 0.7377 0.7800 0.0106 0.0001 0.0040 0.0006

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Appendix C

Table A21. Common Correlated Effects Mean Group estimator (pc3_d, pc1_g, pc2_g) pc3_d Coef. p-value [95% confidence interval] pc1_g -.038419 0.940 -1.04254 .965700 pc2_g -1.73274 0.051 -3.47401 .008540 pc3_d_avg .724228 0.017 .128711 1.31975 pc1_g_avg -.222980 0.104 -.491997 .046038 pc2_g_avg -.004921 0.992 -1.0204 1.01056 _cons -.407663 0.092 -.881637 .066311 *pc3_d is used as dependent and pc1_g, pc2_g as independent variables

Table A22. Common Correlated Effects Mean Group estimator (pc4_d, pc1_g, pc2_g) pc4_d Coef. p-value [95% confidence interval] pc1_g .725469 0.179 -.332510 1.78345 pc2_g -.887648 0.225 -2.32303 .547738 pc4_d_avg 2.50671 0.130 -.740037 5.75346 pc1_g_avg .207042 0.460 -.341889 .755972 pc2_g_avg .788093 0.528 -1.66021 3.23639 _cons -.424465 0.475 -1.58892 .739985 *pc4_d is used as dependent and pc1_g, pc2_g as independent variables

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Appendix D

Table A22. Definition of variables and data sources

Variables Definition Sources Private sector credit (% of Private sector credit is a transfer of financial World Development Indicators GDP) resources to private sector through loan, (WB, 2015) purchases of non-equity securities, and trade credits and other accounts receivable. Domestic credit provided Domestic credit provided by banking sector World Development Indicators by banking sector (% of includes all credit to various sectors on gross (WB, 2015) GDP) basis. The banking sector include monetary authorities and deposit money bank as well as other banking institutions. Liquid Liabilities (% of Liquid liabilities is known as M3 and is the World Development Indicators GDP) sum of currency and deposit in the central bank (WB, 2015) (M0), plus transferable deposit and electronic currency (M1) plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus travelers checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents. Stock market capitalization Stock market capitalization is a share price World Bank Financial Structure (% of GDP) times the number of share outstanding. Database (2015) Stock market turnover ratio Stock market turnover ratio is equal to ratio of World Bank Financial Structure (% of GDP) total shares traded and average real market Database (2015) capitalization. Total share value added (% Stock traded refers to the total value of shares World Bank Financial Structure of GDP) traded during the period. Database (2015) Financial Financial interconnectedness is measured in World Bank Financial Structure interconnectedness (% of terms of assets owned by foreigners as Database (2015) GDP) percentage of real GDP. Chinn/Ito index The Chinn-Ito index is an index measuring a Chinn, Menzie D. and Hiro Ito country‟s degree of capital account openness. (2006). “What Matters for It is based on the binary dummy variables that Financial Development? Capital codify the tabulation of restrictions on cross- Controls, Institutions and border financial transactions reported in the Interactions,” Journal of IMF‟s Annual Report on Exchange Development Economics, Arrangements and Exchange Restrictions. Volume 81, Issue 1, pp. 163- 192. (data updated in 2016) Foreign banking ownership Foreign ownership is a ratio between assets of EBRD and the Bankscope, (% of total assets) the foreign banks to the total assets owned by Bureau van Dijk (BvD) database all the banks in the country.

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INTERACTION BETWEEN FINANCIAL DEVELOPMENT AND FINANCIAL GLOBALIZATION

Appendix E

Table A21. List of CEE countries

Albania Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Poland Romania Slovakia Slovenia

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