Biến động giá cổ phiếu ngành ngân hàng, có khác biệt giữa các nhóm nước ở các khu vực khác nhau và mức thu nhập khác nhau không?

Nội dung text: Biến động giá cổ phiếu ngành ngân hàng, có khác biệt giữa các nhóm nước ở các khu vực khác nhau và mức thu nhập khác nhau không?

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 BANKING VOLATILITY, ARE THERE DIFFERENCES IN LOCATIONS AND INCOME ACROSS COUNTRY GROUPS? BIẾN ĐỘNG GIÁ CỔ PHIẾU NGÀNH NGÂN HÀNG, CÓ KHÁC BIỆT GIỮA CÁC NHÓM NƯỚC Ở CÁC KHU VỰC KHÁC NHAU VÀ MỨC THU NHẬP KHÁC NHAU KHÔNG? Tran Quoc Thanh*, Vo Xuan Vinh *Thu Dau Mot University, University of Economics Ho Chi Minh City thanh.tq@vnp.edu.vn ABSTRACT There are evidences indicating the banking volatility-economic growth nexus in developed markets and emerging markets. However, it is not clear to assume this relationship across countries at various levels of income and geography in low-income and middle-income countries. By using GMM techniques for dynamic panel data to analyze the bank-growth nexus in the full sample and the five subsamples in 21 low-income and middle-income countries from 2003 to 2014, we find unclear impact of bank volatility on economic growth that may result from combining all countries together. When we combine countries across different geography, but in the same income group, the above relationship is still mix. Surprisingly, the impacts of bank volatility on economic growth and the influences of country characteristics and financial development characteristics on this nexus are more clear in groups of countries are combined at the same geography, with the overall effects varying with the legal frameworks, institutional structure for market orientation in groups of countries. Keywords: Banking volatility, upper middle income countries, low income and lower middle income countries, Sub-Saharan Africa, South Asia and East Asia, Latin America. TÓM TẮT Nhiều bằng chứng cho thấy mối quan hệ giữa biến động giá cổ phiếu ngành ngân hàng và tăng trưởng kinh tế ở thị trường phát triển cũng như thị trường mới nổi. Tuy nhiên, chưa có nghiên cứu nào cho mối quan hệ này ở các nhóm quốc gia được phân theo thu nhập và địa lý ở các nước thu nhập thấp và thu nhập trung bình. Áp dụng phương pháp GMM cho dữ liệu bảng để phân tích mối quan hệ giữa biến động giá cổ phiếu ngành ngân hàng và tăng trưởng kinh tế trong mẫu 21 nước và năm mẫu phụ ở các nước thu nhập thấp và thu nhập trung bình từ 2003 đến 2014, chúng tôi thấy tác động không rõ ràng trong mẫu 21 nước. Trong các mẫu phụ, khi kết hợp các quốc gia ở các khu vực địa lý khác nhau nhưng có cùng mức thu nhập, tác động này vẫn không rõ ràng. Tuy nhiên, tác động của biến động giá cổ phiếu ngành ngân hàng lên tăng trưởng kinh tế và ảnh hưởng của đặc thù quốc gia, phát triển tài chính lên mối quan hệ trên rõ ràng hơn ở các nhóm nước có cùng khu vực địa lý, với các tác động chịu ảnh hưởng bởi khung pháp lý, cấu trúc thể chế cho định hướng thị trường trong các nhóm quốc gia. Từ khóa: Giá cổ phiếu ngành ngân hàng, nước thu nhập trung bình cao, nước thu nhập thấp và trung bình thấp, châu Phi Sahara, Nam Á và Đông Á, Mỹ Latinh. 1. Introduction Banking sector is of great importance in promoting economic activities through various channels. A well-functioning banking system facilitates infrastructure for other sectors to run smoothly in the form of loan, so banking crises will cause output growth to slow down. This role is more profound in low- income countries (Kim and Lin, 2013). Mihail and Jordan (2014)’s paper has proved the connection between the banking sector and economic growth when they use bank credit to the private sector, interest rate, and ratio of quasi money as banking sector development indicators. Moreover, share prices are always being traded at their fair value incorporating and reflecting all relevant information in the market, and stock returns of banking industry reflect bank performance. Banks’ expected future cash flows are reflected in the present stock price which depends on the efficiency of loan projects. When the financial prospects of loan borrowers get depressed, these affect loan portfolio quality and present stock price. Consequently, there are correlations between bank stock returns and 32
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 economic growth. However, not many papers directly consider the effects of banks’ stock prices on economic growth, only few empirical evidences suggest these effects in developed markets and emerging markets when countries are combined at different geography. Moshirian and Wu (2012)’s paper proves that banking industry volatility reflects a great deal of information on economic growth through various channels, which are country and institutional characteristics and financial development. This relationship remains a controversial question in frontier markets and in groups of countries are combined at the same geography. Moshirian and Wu (2012)’s paper indicates the impact of country and institutional characteristics on bank-growth nexus. Not surprisingly, our choice of other factors are different than Moshirian and Wu (2012)’s. Firstly, Insider trading laws have some flaws when being applied in the model of banking volatility and economic growth. Since judicial system is one of the most important elements for private sector development, but basic facilities contributing to an efficient judicial system are almost missing in low-income and middle-income countries. History has shown that legal regulations occur behind adverse business behaviors in these countries. Besides, in the banking sector, laws against inside trading are usually general and they can be predicted in silhouette. Because of these drawbacks, insider trading law is not proposed in our models. Likewise, the disclosure of the non-financial and financial information benefits both firms and investors by way of improve market liquidity and reduced the level of information asymmetry. Surprisingly, in recent studies, most positive information is announced to public, but negative one is coved, and the transparency is used as mean of advertising (Kundeliene & Leitoniene, 2016). So that we do not employ bank accounting disclosure in this study. Additionally, banking crisis easily causes fiscal problems in most economies in the world nowadays, and government expenditures for economic recovery have grown enormously during crisis time. This cause a rocket in government debts and asset price buddle simultaneously. Sustainable development requires commitment to sound economic policies and the effectiveness of regulations. Besides, Cornett, et al (2010) and Naceur and Ghazouani (2007) states that institutional frameworks (i.e. country attributes and financial development indicators) have strong influence on banking operations. Regulation and supervision incentive bank operate effectively for achieving the good financial goals. Therefore, quality of governance shapes economic and banking activities, and affects activity of the stock market, liquidity. This will further increase output growth. However, this quality is not employed in the model of banking volatility and economic growth in previous papers, government effectiveness is studied in our research rather than banking crisis in the paper of Moshirian and Wu (2012). In low-income countries, the effects of political institutions on growth are significant or large as oppose to rich countries (Pereira, et al 2010). State-owned banks based in developing economies almost have lower profitability and higher costs than private ones. Another paper of La Porta, et al (2002) also find that higher government ownership of banks, interventionist, inefficient governments and backward financial systems are almost typical features of low income countries. Therefore, to test characteristics of institutional economics in affecting performance of banks in middle income economies and in low income economies, (2013)’s Six Worldwide Governance Indicators (WGI) are used in our models rather than the variable (government ownership of banks) in the paper of Moshirian and Wu (2012). Firms and households receive loans from banks and repay the loans with interest. These relationships are private and independent of the information about all public companies listed in stock market. So that, the volatility of the bank relates to the variation of stock returns of the banking industry, which refers to each individual bank. Whereas stock market volatility refers to the information being reflected in market excess returns, which is representative for all public limited companies (PLCs) in the stock market. Hence, the relationship between banking industry volatility and economic growth is independent of overall market returns. In the research paper of Naceur and Ghazouani (2007) also indicates that the impact of equity market on economic growth is independent of the impact of bank development on economic growth. 33
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 The new economic reography and economic growth theories have explained differences among income levels as well as GDP growth across spaces. They affect the decisions of economic policy makers and shape economic interacions. Hence, the bank volatility – economic growth nexus remains controversial question in groups of countries having the same geography in low-income and middle- income countries. In this paper, we extend previous researches by testing the above relationship in 3 subsamples based on geographic criteria and in 2 subsamples based on income criteria after analyzing the full sample of 21 low income and middle income countries, by using panel GMM (Generalized-Method- of-Moments) techniques. We also examine the influences of country characteristics and financial development characteristics on bank-growth nexus. Furthermore, there exist a strong relationship between economic growth and inflation rates in one country (the Philips Curve and Keynesian theory). Thus, in this paper, we advance previous studies by examining a large number of new measure of country characteristics, which are inflation rates and (2013)’s Six Worldwide Governance Indicators (WGI). Then, we construct variables for country-specific and institutional characteristics, which are inflation rates, WGI, and variables for financial development, including Domestic Credit to private sector, Liquid Liabilities, and Stock-Market-Capitalization to GDP. Next, these variables are interacted with banking industry volatility to evaluate the effect of country- specific, institutional characteristics and financial development on bank-growth nexus from 2003 to 2014. Our research contributes to the literature on banks’ stock prices and economic growth in many ways. First of all, we find unclear impact of bank volatility on economic growth that may result from combining all countries together. When we combine countries at different geography, this relationship is still mix. Surprisingly, the impacts of bank volatility on economic growth is more clear in groups of countries are combined at the same geography. Secondly, our developing illustrates, the influences of country-specific, institutional characteristics and financial development on bank-growth nexus are more clear in groups of countries are combined at the same geography, with the overall effects varying with the legal frameworks, institutional structure for market orientation in groups of countries. 2. Literature Review A lot of studies prove bank-economic growth relationship. Campello et al. (2010) indicates that firms do not have ability to borrow externally during the credit crisis of 2008. The weakened function of the banking system causes negative effects on the flow of economic activities. More importantly, Moshirian and Wu (2012) prove that there exists a negative relationship between bank volatility and future economic growth in developed markets and in emerging markets. Besides, Kim and Lin (2013) find that banking development contributes more to economic growth in low-income countries, and stock market development has more contribution to output growth in high-income or low-inflation countries. Moreover, Rabiul (2010) prove that banks and stock markets have positive and separate impacts on economic growth, so they are important to boost long-run growth in developing countries. However, we find the above relationship is unclear in all countries combined together and in countries are grouped based on income criteria. In the case of three subsamples based on geographic criteria, we find the negative effect of bank volatility on economic growth is very weak and marginally significant in South Asian and East Asian countries and Latin American countries. However, the result is robust in Sub- Saharan African countries. In other studies show stock market-economic growth nexus. Wang and Ajit (2013) prove that stock market does not affect economic growth positively in China. This result agrees with the report of Harris (1997) for developing countries. Osamwonyi et al. (2013) find that there is no causal relationship between stock market development and economic growth in Ghana and Nigeria, but they have a bidirectional causal relationship when Granger Causality test procedure is used. This report is similar to Rahimzadeh (2012), who researches the Middle East and North Africa. In our study, we find this relationship is still mix. Financial development indicators for each country including domestic credit to private sector, liquid liabilities, stock market capitalization are employed in our study. Based on the literature on financial development-economic growth nexus, we hope these indicators affect banking volatility- economic growth relationship. However, we find the impact of these indicators is sensitive in different samples. 34
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Some studies prove country and institutional characteristics-economic growth connection. The negative effects of inflation have been studied through a wide range of applied models of economic growth; it undermines the confidence of domestic and foreign investors as well as consumer in the future economic growth (Andrés & Hernando, 1999). Bruno and Easterly (1998) maintain that a high levels of inflation (the level at which the inflation rate exceeds the calculated threshold, estimated 40 percent yearly) would harm the economic growth, and conversely, low levels of inflation boost the economic growth. These variables are negatively correlated, especially in the long run (Andrés & Hernando, 1999). Futhermore, financial shock may cause very high inflation rates in the economies surveyed after that and high interest rates simultaneously. Moshirian and Wu (2012) also indicate that the effect of country-specific and banking institutional characteristics on bank-growth relationship is ambiguous. In our study, we find the effect of high levels of inflation on bank-growth nexus is almost positive, but the effect of low levels of inflation on this relationship is almost negative in most of samples. Omoteso and Ishola Mobolaji (2014) prove that political stability and regulatory variables impact positively and significantly on economic growth in the separate region (Sub-saharan Africa). We find similar effect in the sample of South Asian and East Asian countries and in the sample of Latin American countries, but not in Sub-Saharan African countries. The positive effects are also more powerful for the variables of voice and accountability and rule of law in the research of Omoteso and Ishola Mobolaji (2014). Conversely, we find these relationships are negative in three subsamples based on geographic criteria. Government effectiveness has a negative impact on economic growth. Although there are anti- corruption strategies, the effect of corruption control on economic growth is unclear (Omoteso & Ishola Mobolaji, 2014). In our study, we find the impact of control of corruption is negative, but regulatory quality is positive in Sub-Saharan African countries. We also find that the interaction terms of bank volatility with government effectiveness, control of corruption, are negative in the sample of Latin American countries. This paper is organized as follows. In the next section, we outline our data and research methodology. Results and discussion are discussed in the section four. Finally, section five concludes the paper. 3. Data and Research Methodology 3.1. Data Our data sets comprise information based on income criteria and geographic criteria, including the full sample and the 5 subsamples. The full sample covers 21 countries while 5 subsamples comprise of 10 upper middle income countries, 11 low income and lower middle income countries, 8 Sub-Saharan African countries, 6 South Asian and East Asian countries, and 5 Latin American countries. The data used cover the period 2003 - 2014 on a quarterly basis. The sample period for each country is shown in Column 3 of Table 3. The selected economies’ data are based on the available data on bank equity price, quarterly macroeconomic time series, and short term interest rates. Table 1 provides a summary of the variables calculated and their sources. The primary variable is banking industry volatility. In this study, we use a detailed analysis which is a disaggregate approach based on the method of Campell et al. (2001) to calculate the banking volatility. This approach is carried out using the following steps. First of all, we calculate the portfolio of listed banks for each country collected from International Datastream sources. Concerning the available market price data, we have the maximum number of thirty listed banks for Indonesia, and the minimum of two listed banks for Mauritius and Uganda. Nevertheless, when collecting the available data for market capitalization, we have the maximum number of thirty listed banks for Indonesia and the minimum of one listed banks for the Philippines. In this case, the Philippines constitutes a complete data set of market price of nineteen banks, quarterly GDP series, and short-term interest rates. However, it has market 35
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 capitalization for one listed banks in the Datastream, so data are based on these indicators of individual banks. Since all 21 countries have mixed economies, we only collect data for available banks on domestic stock market. The banks operating in both domestic and international markets, but listed in international stock markets are excluded from our samples. Therefore, only a few banks can be representative of the whole market. Variables (interest rates, GDP series, and the market price index for each country) are also extracted from the Datastream. The sources of the data collected are diverse. Secondly, this paper calculates the continuous stock return over Rf (risk-free-rate) when we measure the excess-return in weighted value on the portfolio of the bank in each country. This research collects Treasury-Bill rate in three months or Deposit-rate in three months depending on the available data in Datastream. We also use MC (Market-Capitalization) to estimate the weights. The Market- Capitalization of bank-j over the total Market-Capitalization of the banking field at the end of period (t-1) remains constant within period (t). It is used to build the weight of bank-j. Third of all, Excess-Return is calculate on the market index for each country. In the next stage, we get the beta for each country when regressing the quarterly bank excess return against the quarterly market excess return, beta is assumed to vary in the long run, but to be constant over the sample period. Nonetheless, this study analyzes a large number of economies. It makes more sense to simplify them for our assumption and run the same model for different economies in the most consistent way. After taking all the above steps, we have a complete data set of 21 economies. We divide the full sample into the five subsamples. We follow the threshold levels of GNI per capita calculated by the World Bank in 2012 to collect data for low-income countries, lower middle income countries, and upper middle income countries. 1 In the third step, we calculate quarterly bank volatility (VOLit) by using monthly frequency data , which is documented as follows: VOLit = Var(Rit) = Var(Rmit) + ̂ where: ∑( ) ̂ ∑( ) Rmi is the monthly excess market return in market i. Mmit is the moving average monthly excess market return for country i over period t (in this case t is quarter). βim is the beta of the banking industry which proxies for the market in economy i. Ri is the monthly excess-return of the banking industry in weighted value in the market-i. The value of Ri is taken by deducting the monthly risk-free rate, which is obtained by dividing the annualized short term interest rate by 12 months. As a result, we have the excess-return for each month. Following the method of Cole et al. (2008), most variables are estimated, including continuous economic growth rate (dependent variable); lagged market Excess-Return (controlled variable); characteristics of each country (six governance indicators); low levels of inflation; high levels of inflation; indicators of financial development, which are Domestic Credit to private sector, Liquid Liabilities, and Stock-Market- Capitalization to GDP. The indicators representing characteristics of each country relate to economic growth or the efficiency of the economy in a long stage. These indictors respect the difference of the cross section in banking institutional framework of each sample. Next, we estimate the effect of this differences in the institutional framework on the relationship between baking industry volatility and economic growth. 1 Our estimation is different from Moshirian and Wu (2012) who use weekly frequency data. 36
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 - The dependent variable is GDP growth rate. It is calculated by taking logarithm of the ratio of GDP at period t and GDP at period t-1 at constant prices (Growth=LOG(GDPt / GDPt-1)). - The control variable is lagged market excess return. It is defined as the excess return on the market index in country-i, and is estimated by taking logarithm of the ratio of market price index at the end of period t and market price index at period t-1 of country i (t is in quarter), then minus the risk-free rate (Rf), which is Treasury-Bill rate in three months or deposit-rate in three months (Rm = Rmit = log(Pmit/Pmi(t-1)) – Rfit ). Eight country characteristic indicators: Six Worldwide Governance Indicators (WGI): they are a dataset covering some qualify indicators representing the health of Government in one country all over the world. They range in units from around -2.5 to 2.5, with higher values corresponding to better governance outcomes (Kaufmann, 2013). Voice and Accountability: “The variable measures the degree to which their citizens may present in election for authorities in one country, freedom of voice, and free media”. Political Stability and Absence of Violence: “The variable measures perceptions of the likelihood that the government will be destabilized by unconstitutional or violent means, including politically motivated violence and terrorism”. Government Effectiveness: “The variable measures the public quality in serving its citizens, and the extent of its independence, the policy quality, and the commitment of authorities to make their policy occur in the real life. The more effectiveness, the less vulnerability of financial sector”. Regulatory Quality: “The variable measures the ability of authorities to make the regulations feasible and improve the private sector”. Rule of Law: “The variable measures the degree to which government make the quality of policy, courts, crime laws, violence, enforcement of contract, and property rights feasible”. Control of Corruption: “The variable measures the degree to which authorities exercise their power over the public”. - Inflation (1) is the dummy variable, taking on the value of one when the value of inflation is smaller than the sample group (all countries) median, and the value of zero otherwise. - Inflation (2) is the dummy variable, taking on the value of one if inflation is greater than the sample group (all countries) median, and the value of zero otherwise. Three financial development indicators - Domestic-Credit to private sector is defined as financial resources mostly of corporations, which are provided to the private sector in the forms of loans, non-equity securities. - The ratio of Liquid-Liabilities of the financial system to GDP. The total value of currency and deposit in the central bank plus deposits and electronic currency, then plus time and savings deposit and other deposit for transferable foreign currency, certificates as well as securities repurchase agreements. Next is the addition of checks for travelers, paper for trades, time deposits for foreign currency, and share of funds for the market. - The ratio of Stock-Market-Capitalization to GDP equals the total value of all listed shares in a stock market as a percentage of GDP. With the purpose of prolonging the time-series information in this research, this study handles yearly data using the overlapping method with observations in quarter. Lagged bank excess return (Rb) is computed for comparison with lagged bank volatility (Vol) and lagged market excess return (Rm). The descriptive statistics and correlation matrices are presented in Table 2. 37
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 1: Summary information about variables measured Variable Definition Expected sign Data sources Growth GDP Growth rate Datastream International Rm Lagged market excess return Ambiguous Datastream International Vol Lagged bank volatility Negative Datastream International Indicators of country characteristics Voice Voice and accountability Ambiguous WGI Annually Political Political stability and Absence of violence Positive WGI Annually Gov Government effectiveness Ambiguous WGI Annually Regu_qua Regulatory quality Positive WGI Annually Rule Rule of law Positive WGI Annually Controlcur Control of corruption Positive WGI Annually Infla1 Inflation 1 Positive World Bank Annually Infla2 Inflation 2 Negative World Bank Annually Indicators of financial development Credit Private credit Positive World Bank Annually Liquid Liquid liabilities Positive World Bank Annually Stock_cap Stock market capitalization to GDP Positive World Bank Annually Table 2: Summary of descriptive statistics of primary variables All economies Upper middle income Low and Lower middle income growth Rm Rb Vol Growth rm Rb vol growth rm rb vol Descriptive statistics - - - - - - Mean 0.007 7.349 7.450 0.032 0.007 7.228 7.489 0.046 0.006 7.499 7.402 0.014 Std. Dev 0.025 5.482 5.669 0.143 0.026 5.944 6.164 0.188 0.023 4.855 4.970 0.045 - - - - - - Min -0.201 37.06 48.85 0.00 -0.2 37.06 48.85 0.00 -0.07 25.83 25.77 0.00 Max 0.126 0.138 1.150 1.978 0.120 0.138 1.150 1.978 0.126 0.107 0.149 0.634 Obs 696 779 766 779 398 431 431 431 298 348 335 348 Correlations Growth 1 1 1 Rm -0.018 1 -0.024 1 -0.008 1 Rb -0.006 0.931 1 -0.003 0.901 1 -0.011 0.994 1 - - - - Vol -0.015 0.064 0.129 1 -0.016 0.098 0.143 1 -0.036 0.151 0.135 1 38
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Africa South Asia & East Asia Latin America growth rm rb Vol growth rm Rb vol growth Rm rb vol Descriptive statistics - - - - - - Mean 0.007 8.119 8.056 0.017 0.006 6.455 6.296 0.010 0.007 8.691 9.305 0.074 Std. Dev 0.027 4.710 4.919 0.061 0.023 3.765 3.759 0.023 0.020 6.054 6.287 0.248 - - - - - - Min -0.087 25.83 25.77 0.00 -0.07 18.77 18.74 0.00 -0.04 25.46 48.85 0.00 - Max 0.104 0.725 0.000 0.634 0.126 0.020 0.053 0.226 0.074 0.138 1.150 1.978 Obs 189 218 206 218 188 225 224 225 230 235 235 235 Correlations growth 1 1 1 Rm 0.003 1 -0.032 1 -0.006 1 Rb 0.031 0.984 1 -0.038 0.985 1 0.012 0.843 1 - - - - - Vol -0.040 0.115 0.109 1 -0.019 0.219 0.153 1 -0.024 0.198 0.115 1 Table 3: Country specifics Economy Region Sample period Interest rate(Year) Year No.of Median Max banks Upper middle income economies Argentina Latin America Q2/2003 - Q4/2014 10.20 22.26 2014 6 Botswana Sub-Saharan Africa Q2/2009 - Q4/2014 4.76 12.41 2003 3 Brazil Latin America Q2/2003 - Q4/2014 11.60 22.11 2003 21 Chile Latin America Q2/2003 - Q4/2014 6.04 6.95 2008 7 Malaysia East Asia Q2/2003 - Q4/2014 2.95 3.60 2006 10 Mauritius Sub-Saharan Africa Q4/2008 - Q4/2014 5.43 10.95 2007 2 Peru Latin America Q2/2003 - Q4/2014 0.43 2.91 2008 8 South Africa Sub-Saharan Africa Q2/2003 - Q4/2014 7.15 10.85 2008 8 Turkey Europe&Central Asia Q2/2003 - Q4/2014 19.02 37.68 2003 17 Venezuela Latin America Q2/2003 - Q4/2014 12.51 15.00 2008 10 Low income economies Kenya Sub-Saharan Africa Q1/2004 - Q4/2014 7.54 12.58 2012 8 Uganda Sub-Saharan Africa Q1/2007 - Q4/2013 11.51 16.04 2003 2 39
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Lower middle income economies Ghana Sub-Saharan Africa Q2/2011 - Q4/2014 16.96 27.25 2003 7 Indonesia East Asia Q2/2003 - Q3/2014 8.61 11.80 2006 30 Morocco Middle East Q2/2003 - Q4/2014 5.38 9.50 2009 6 Nigeria Sub-Saharan Africa Q1/2010 - Q4/2014 9.85 14.79 2003 15 Pakistan South Asia Q2/2003 - Q2/2008 9.61 13.12 2011 19 Philippines East Asia Q2/2005 - Q2/2014 5.08 9.77 2004 1 Sri lanka South Asia Q2/2003 - Q4/2014 10.02 18.60 2008 13 Viet nam East Asia Q2/2007 - Q4/2013 6.64 12.35 2011 7 Zambia Sub-Saharan Africa Q1/2011 - Q4/2014 12.28 29.97 2003 3 Table 2 shows that the mean of GDP growth rates for the six samples ranges around 0.7% with the smallest range, whereas the mean of banking industry volatilities is positive and higher than the previous one with a larger range. In contrast, the mean of market excess returns and the mean of bank excess returns are negative and fluctuate widely among the samples of all economies. They have the largest range in general. Notably, the simple correlations between GDP growth rates and banking volatilities are negative in all samples. 3.2. Research Methodology We apply the generalized method of moments (GMM) econometric techniques developed for dynamic panel data. Based on the method suggested by Cole et al. (2008), Campello et al. (2010), Cornett et al. (2010), Moshirian and Wu (2012), and Arellano and Bover (1995), we examine a fixed-effect dynamic model for the full sample and five subsamples at the beginning: (1) Yit = αi + Yi(t-1) + ’Xi(t-1) + ni + it In which i and t are used to indicate country and time period. Yit is the GDP growth rate for the selected samples at the time t. Yi(t-1) is the lagged value of the dependent variable. X is the vector of explanatory variables containing lots of variables, such as banking industry volatility (VOLit), lagged market excess_return (Rm), and the interaction terms between banking industry volatility and the variables of financial development and country-specific characteristics. ni is the unobserved specific effect for country i, and it is an error term. We use these interaction terms to examine the effects of country characteristic, and financial development on economic growth. In the process of applying GMM, we eliminate the group effects from the fixed-effect model by employing one simple technique, which is taking the first difference. Consequently, we have: ’ Yit - Yi(t-1) = λ(Yi(t-1) - Yi(t-2)) + β (Xi(t-1) - Xi(t-2)) + (ɛit - ɛi(t-1)) (2) We rewrite this equation as: ’ (3) Δyit = λΔyi(t-1) + β Δxi(t-1) + The endogeneity in the above model causes serious problems (e.g., the results calculated are inconsistent and biased, or the link exists between the lagged dependent variable and the error terms). To handle these problems, we use proper instrument variables as suggested by Arellano and Bond (1991), who documented that “instruments are lagged values of explanatory-variables in the regression at the original level.” We also give out the assumption that the link between disturbances in the time-varying setting does not exist. 40
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 εit, E(εit εis) = 0, for i = 1, . . ., N and ∀t s; and the initial conditions Yi1 is not correlated with future realizations of the error term, E(Yi1 ) = 0, for i = 1, . . ., N and t = 2, . . ., T, we can use the following m = 0.5(T− 1)(T−2) moment conditions for the autoregressive parameter: E[Yi(t-s). ] = 0 for s ≥ 2; t [3, T] (4) E[Xi(t-s). ] = 0 for s ≥ 2; t [3, T] (5) The generalized method of moments (GMM) estimators could be given by: ̂ [(∑ ) (∑ )] (∑ ) (∑ ) (6) In the above equation, Wi is the (T − 2)×q (q is the number of regressors) matrix, Zi is the (T − 2) ×m matrix, AN is the weighting matrix, and yi is the (T − 2) vector. Here, the choices of AN give rise to a set of GMM estimators based on the moment conditions. The difference GMM is called original estimator. However, this first-differenced estimator is less suitable when reducing the sample length, surveying a huge amount of information on the levels of the variables, and on the indirect-link among the levels and the first differences. Thus, we will do an inefficient calculation (Ahn & Schmidt, 1995). The system GMM will have lower bias and highly precise results in a finite sample, so it is introduced by Arellano and Bover (1995) to handle the above problems. In the system GMM, the original level is linked to the first-differenced regressions. In the specific way, the instruments in the level regressions are the lagged first-differences variables, and the lagged level variables are manipulated as instruments in the first differenced regressions. Hence, we will get the original level regressions, and then we have the additional moment conditions as follows: E[Δyi(t-1). ( + ɛit )] = 0 (7) E[Δxi(t-1). ( + ɛit )] = 0 (8) In our study, there are two main techniques employed for the panel data: the first-differenced GMM (GMM(DIF)), and system GMM (GMM(SYS)). The GMM techniques provide consistent estimators, so they have lots of advantages over others in estimating the dynamic panel data. The data of this research comprise 21 economies. The shortest time-series observation has 11 quarters, and the longest one has 47 quarters. Hence, we use the commands of David Roodman (2013) to run the difference GMM and system GMM. The estimated results of both GMM(SYS) and GMM(DIF) are reported below. The main objective of this study is to examine whether there exists a relationship between banking industry volatility and economic growth. More importantly, this paper examines which factors influencing this link. We address these issues by looking at the significance of the coefficients of relevant variables rather than the scales of relevant coefficients. In the first stage, we employ GMM-Dif and GMM-Sys estimations for the full sample of 21 countries. In the second stage, we repeat estimations of each of the two methods using 10 upper middle income countries and 11 low income and lower middle income countries. In the next step, we repeat estimations of each of the two methods using 8 Sub-Saharan African countries, 6 South Asian and East Asian countries, and 5 Latin American countries. 4. Results and Discussion We examine the effect of banking industry volatility on economic growth, and then test influence of country characteristics and financial development on this link. To observe this effect, we interact banking industry volatility with these country characteristics and financial development. We look at the signs of the coefficients of these interaction terms to identify whether these variables strengthen or weaken the impact of banking industry volatility on economic growth. 41
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 4a: Dynamic panel GMM (DIF) estimation results for the full sample of 21 economies 1 2 3 4 5 6 Lag -0.203 -0.189 -0.191 -0.196 -0.193 -0.206 (-5.12) (-4.42) (-4.54) (-3.62) (-6.47) (-4.89) Vol -0.095 -0.257 -0.101 -0.26 -0.177 -0.177 (-1.44) (-1.53) (-0.59) (-0.69) (-0.86) (-1.10) Rm 0.0002 0.0003 -0.0008 -0.0001 0.005 -0.00002 (-0.21) (-0.08) (-0.23) (-0.03) (-0.44) (-0.01) Vol*voice -0.425 (-0.92) Vol*political -0.032 (-0.19) Vol*gov -0.236 (-0.40) Vol*regu_qua -0.276 (-0.44) Vol*rule -0.082 (-0.56) N 626 560 558 560 560 560 7 8 9 10 11 12 Lag -0.195 -0.230 -0.225 -0.197 -0.192 -0.237 (-4.24) (-5.22) (-6.60) (-4.07) (-4.96) (-4.24) Vol -0.355 -0.034 -0.06 -0.013 0.004 -0.144 (-1.40) (-0.12) (-0.57) (-0.03) (-0.01) (-1.13) Rm -0.001 -0.001 0.003* -0.001 0.0004 0.001 (-0.86) (-0.51) (-1.92) (-1.40) (-0.1) (-0.37) Vol*controlcur -0.227 (-0.94) Vol*infla1 -0.104 (-0.41) Vol*infla2 0.122 (-0.55) Vol*credit -0.004 (-0.25) Vol*liquid -0.003 (-0.33) Vol*stock_cap -0.0005 (-0.15) N 560 584 626 518 539 410 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 42
12. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 4b: Dynamic panel GMM (SYS) estimation results for full sample of 21 economies 1 2 3 4 5 6 lag 0.284 0.268 0.330 0.265 0.292 0.291 (-4.91) (-6.64) (-5.61) (-3.84) (-3.85) (-4.07) Vol 0.158 0.154 0.182 -0.346 0.191 0.129 (-1.58) (-0.87) (-0.23) (-0.91) (-0.54) (-0.33) Rm -0.003 -0.003 -0.002 -0.009 -0.003 -0.003 (-0.45) (-0.88) (-0.28) (-0.89) (-0.27) (-0.34) Vol*voice -0.033 (-0.07) Vol*political -0.055 (-0.06) Vol*gov -0.241 (-0.72) Vol*regu_qua 0.221 (-0.3) Vol*rule -0.025 (-0.07) cons -0.0483 -0.047* -0.032 -0.076 -0.042 -0.04 (-0.55) (-1.71) (-0.36) (-0.79) (-0.33) (-0.43) N 605 581 577 560 539 539 7 8 9 10 11 12 lag 0.265 0.314 0.294 0.105 0.337 0.289 (-8.09) (-3.85) (-3.87) (-0.77) (-5.67) (-8.7) Vol -0.013 -0.004 0.147 -0.319 -0.709 -0.134 (-0.05) (-0.07) (-1.3) (-0.73) (-0.80) (-0.29) Rm -0.003 -0.003 -0.003 -0.003 -0.0003 0.001 (-0.35) (-0.45) (-0.37) (-0.41) (-0.05) -0.18 Vol*controlcur -0.174 (-0.66) Vol*infla1 0.162 (-2.47) Vol*infla2 0.0461 (-0.08) Vol*credit 0.02 (-1.09) Vol*liquid 0.019 (-0.91) Vol*stock_cap 0.006 (-0.41) cons -0.044 -0.049 -0.044 -0.058 -0.018 0.002 (-0.43) (-0.52) (-0.47) (-0.61) (-0.26) (-0.03) N 581 626 605 539 559 439 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 43
13. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 5a: Dynamic panel GMM (DIF) estimation results for both subsamples 1 2 3 4 5 6 Low income and lower middle income economies lag -0.208 -0.255* -0.210 -0.13 -0.292 -0.300 (-6.25) (-1.90) (-5.49) (-0.69) (-3.12) (-3.77) Vol -0.028 -0.08 0.213 -0.031 -0.092 -2.034 (-0.21) (-0.07) (-0.08) (-0.07) (-0.21) (-0.73) Rm -0.003 0.002 0.002 -0.002 -0.001 0.003 (-0.82) (-0.59) (-0.09) (-0.36) (-0.51) (-1.18) Vol*voice -0.097 (-0.07) Vol*political -0.151 (-0.08) Vol*gov -0.043 (-0.07) Vol*regu_qua -0.116 (-0.19) Vol*rule -2.178 (-0.73) N 265 238 238 238 216 216 7 8 9 10 11 12 lag -0.279* -0.250 -0.250 -0.19 -0.237 -0.276 (-1.85) (-6.45) (-7.56) (-0.66) (-2.91) (-20.33) Vol -0.469 -1.099 -0.435 -0.542 -0.441 -0.009 (-0.28) (-1.23) (-0.13) (-0.64) (-0.39) (-0.03) Rm 0.01 -0.002 -0.0001 -0.0005 -0.002 -0.001 (-0.61) (-0.28) (-0.02) (-0.10) (-1.14) (-0.20) Vol*controlcur -0.487 (-0.30) Vol*infla1 0.867 (-1.17) Vol*infla2 0.428 (-0.13) Vol*credit 0.031 (-0.63) Vol*liquid 0.01 (-0.36) Vol*stock_cap -0.0002 (-0.01) N 238 243 243 238 217 154 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 44
14. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 5b: Dynamic panel GMM (DIF) estimation results for both subsamples 1 2 3 4 5 6 Upper middle income economies lag -0.049 -0.052 -0.112 -0.061* -0.021 -0.52 (-1.15) (-1.31) (-1.30) (-1.70) (-0.49) (-1.23) Vol -0.084 -0.233 -0.164 -0.125 -0.093 -0.448* (-0.53) (-1.52) (-1.56) (-0.52) (-0.54) (-1.65) Rm -0.001 -0.004 -0.001 -0.001 -0.002 0.003 (-1.35) (-0.55) (-0.52) (-0.40) (-0.67) (.) Vol*voice -0.381 (-0.78) Vol*political 0.027 (-0.23) Vol*gov 0.015 (-0.03) Vol*regu_qua 0.144 (-0.19) Vol*rule -0.315 (-1.21) N 341 302 320 322 312 312 7 8 9 10 11 12 lag -0.045 -0.041 -0.033 -0.045 -0.045 -0.056* (-1.18) (-0.86) (-0.49) (-1.18) (-1.19) (-1.73) Vol -0.352 -0.224 -0.248 -0.126 -0.242 -0.189 (-1.33) (-0.53) (-0.58) (-0.56) (-1.34) (-1.37) Rm -0.004 -0.003 -0.001 -0.003 -0.003 0.0002 (-0.79) (-0.43) (-0.42) (-0.84) (-0.93) -0.13 Vol*controlcur -0.209 (-0.81) Vol*infla1 0.096 (-0.23) Vol*infla2 0.033 (-0.08) Vol*credit -0.001 (-0.08) Vol*liquid 0.002 (-0.56) Vol*stock_cap 0.001 (-0.25) N 302 361 361 302 302 282 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 45
15. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 6a: Dynamic panel GMM (SYS) estimation results for both subsample 1 2 3 4 5 6 Low income and lower middle income economies lag 0.286* 0.165 0.286 0.175 0.165 0.181 (-1.7) (-1.21) (-0.96) (-1.15) (-1) (-1.19) Vol -0.043 -0.653 -7.554 -5.395 -0.843 -0.049 (-0.14) (-0.19) (-0.44) (-0.60) (-0.65) (-0.00) Rm -0.011 -0.007 -0.0004 -0.008 -0.009 -0.008 (-0.78) (-0.68) (.) (-0.69) (-0.75) (-0.67) Vol*voice -0.663 (-0.17) Vol*political 5.837 (-0.45) Vol*gov -6.62 (-0.60) Vol*regu_qua -1.008 (-0.59) Vol*rule 0.016 (0) cons -0.119 -0.105 0.02 -0.081 -0.119 -0.094 (-0.71) (-0.80) (-0.16) (-0.68) (-0.80) (-0.67) N 254 249 249 249 227 249 7 8 9 10 11 12 lag 0.186 -0.041 0.056 0.152 0.167 0.185 (-1.22) (-0.17) (-0.31) (-1.97) (-2.07) (-2.62) Vol -7.247 10.48 2.246 -15.62 -20.07 -0.226 (-0.73) (-1.4) (-0.96) (-0.91) (-0.82) (-0.27) Rm -0.008 -0.003 0.004 -0.03 -0.004 -0.01 (-0.74) (-0.22) (-0.13) (-1.20) (-0.31) (-0.99) Vol*controlcur -6.943 (-0.74) Vol*infla1 -8.344 (-1.44) Vol*infla2 10.17 (-1.37) Vol*credit 0.776 (-0.92) Vol*liquid 0.533 (-0.82) Vol*stock_cap 0.006 (-0.17) cons -0.083 -0.207 -0.152 -0.477 -0.148 -0.1 (-0.70) (-1.17) (-0.95) (-1.13) (-0.79) (-0.93) N 249 276 276 249 207 189 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 46
16. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 6b: Dynamic panel GMM (SYS) estimation results for both subsamples 1 2 3 4 5 6 Upper middle income economies lag 0.204 -0.092 0.257 0.07 0.298 0.272 (-0.82) (.) (-0.84) (-0.41) (-3.07) (-2.46) Vol -0.05 2.232 -2.638 0.599 0.396 -0.048 (-0.62) (-1.37) (-0.82) (-0.38) (-0.52) (-0.11) Rm 0.006 0.008* 0.006 0.005 0.004 0.006 (-1.5) (-1.68) (-1.46) (-1.31) (-0.69) (-1.02) Vol*voice 6.869 (-1.11) Vol*political 2.806 (-0.94) Vol*gov 0.706 (-0.31) Vol*regu_qua 1.218 (-0.52) Vol*rule -0.108 (-0.37) cons 0.076 0.05 0.14 0.05 0.045 0.074 (-1.39) (-0.74) (-1.03) (-0.71) (-0.56) (-0.94) N 371 312 312 332 312 312 7 8 9 10 11 12 lag 0.162 -0.2 0.234 0.396 0.252* -0.041 (-0.54) (-0.36) (-1.07) (.) (-1.69) (-0.05) Vol -0.007 -0.098 -0.003 -4.83 -5.77 -0.4 (-0.01) (-1.49) (-0.02) (-0.77) (-0.87) (-0.17) Rm 0.007 0.00 -0.004 0.015 0.017 0.011 (-0.91) (-1.14) (-0.26) (-1.31) (-1.44) (-0.97) Vol*controlcur -0.095 (-0.09) Vol*infla1 0.331 (-1.44) Vol*infla2 -13.76 (-0.62) Vol*credit 0.174 (-0.8) Vol*liquid 0.126 (-0.88) Vol*stock_cap 0.012 (-0.19) cons 0.078 0.075 0.084 0.103 0.15 0.125 (-0.98) (-1.01) (-1.15) (-1.5) (-1.54) (-1.03) N 322 361 371 322 322 272 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 47
17. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 7a: Dynamic panel GMM (DIF) estimation results for three subsamples 1 2 3 4 5 6 Africa lagG -0.014 -0.017 -0.5* -0.3 -0.281 -0.52 (-0.19) (-0.22) (-1.76) (-1.26) (-0.81) (-1.39) vol -0.023 -0.299 0.32 -1.241 -1.695 -11.8* (-0.43) (-0.99) (-0.27) (-0.33) (-2.03) (-1.90) rm -0.002 0.003 0.014 0.005 0.037* 0.06* (-0.81) (-0.54) (-1.6) (-0.95) (-1.93) (-1.75) Vol*voice -0.379 (-0.96) Vol*political -0.273 (-0.30) Vol*gov -0.96 (-0.33) Vol*regu_qua 2.310 (-2.04) Volrule -7.544 (-1.36) N 165 139 139 139 123 131 7 8 9 10 11 12 lagG 1.148 -1.601 -1.646 -0.325 -0.106 -0.078 (-0.7) (-0.42) (-0.39) (-0.98) (-0.22) (-0.24) vol -14.83 5.15 -0.055 -0.001 -4.274 -1.914 (-0.94) (-0.47) (-0.16) (-0.01) (-1.51) (-1.19) rm -0.457 -0.003 -0.003 0.001 -0.027 0.011 (-0.81) (-0.13) (-0.13) (-0.05) (-1.22) (-1.33) Vol*controlcur -13.56 (-0.94) Vol*infla1 -5.209 (-0.47) Vol*infla2 5.207 (-0.44) Vol*credit 0.0002 (-0.08) Vol*liquid 0.08 (-1.52) Vol*stock_cap 0.111 (-1.19) N 123 157 157 131 111 91 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 48
18. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 7b: Dynamic panel GMM (DIF) estimation results for three subsamples 1 2 3 4 5 6 South Asia and East Asia lag -0.229 -0.24 -0.252 -0.218 -0.242 -0.0579 (-3.38) (-1.32) (-5.15) (-6.95) (-14.47) (-2.79) Vol -0.923 -0.016 -1.284 -9.356 -0.434 -2.03 (-0.53) (-0.00) (-0.56) (-0.57) (-0.16) (-0.34) Rm -0.037 -0.009 -0.002 -0.01 0.014 -0.025 (-0.92) (-0.42) (-0.06) (-0.68) (-0.71) (-0.40) Vol*voice -4.136 (-0.28) Vol*political 2.029 (-0.85) Vol*gov -15.54 (-0.66) Vol*regu_qua -0.894 (-0.51) Vol*rule -2.703 (-0.42) N 158 145 151 157 157 145 7 8 9 10 11 12 lag -0.226 -0.276 -0.183 -0.264 -0.339 -0.236 (-4.39) (-12.37) (-1.40) (-2.84) (-2.14) (-16.85) Vol -15.71 -2.343 -0.178 4.108 -7.229 -4.726 (-4.59) (-0.48) (-0.23) (-0.15) (-0.46) (-0.84) Rm -0.012 0.005 -0.044 -0.002 -0.07 -0.004 (-0.48) (-0.96) (-0.64) (-0.06) (-0.62) (-0.28) Vol*controlcur -15.80 (-3.77) Vol*infla1 -60.2 (-1.32) Vol*infla2 0.317 (-0.11) Vol*credit -0.06 (-0.13) Vol*liquid 0.102 (-0.46) Vol*stock_cap 0.171 (-0.88) N 151 164 170 145 157 115 t statistics in parenthese: * significant 10%, significant 5%, significant 1% 49
19. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 7c: Dynamic panel GMM (DIF) estimation results for three subsamples 1 2 3 4 5 6 Latin America lag -0.806 -1.335 -1.309* -2.061 -1.337 -0.661 (-1.31) (-1.55) (-1.67) (-1.52) (-1.62) (-1.10) Vol -0.067 -3.613 -1.207 -135.2 -1.425 -0.231 (-1.33) (-1.09) (-0.77) (-1.35) (-0.84) (-0.28) Rm 0.059 -0.033 -0.05 -0.005 0.011 0.012 (.) (.) (.) (-0.07) -0.12 (.) Vol*voice -11.49 (-1.09) Vol*political 1.044 (-0.69) Vol*gov -232.7 (-1.33) Vol*regu_qua -5.636 (-0.76) Vol*rule 0.24 (-0.19) N 210 196 194 196 186 186 7 8 9 10 11 12 lag -1.296 -1.463* -1.319* -1.217 -1.341* -0.636 (-1.47) (-1.90) (-1.94) (-1.49) (-1.90) (-1.07) Vol -2.949 -0.558 -0.078 -9.705 -11.99 -3.568 (-1.11) (.) (-0.72) (-0.45) (-0.42) (-1.18) Rm -0.385 -0.094 -0.11 -0.13 -0.034 -0.018 (-1.64) (-0.49) (-0.26) (-0.76) (-0.82) (-0.75) Vol*controlcur -2.017 (-0.82) Vol*infla1 -4.62 (-0.63) Vol*infla2 -7.07 (-0.07) Vol*credit 0.363 (-0.44) Vol*liquid 0.278 (-0.42) Vol*stock_cap 0.083 (-1.16) N 186 205 215 196 196 166 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 50
20. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 8a: Dynamic panel GMM (SYS) estimation results for three subsamples 1 2 3 4 5 6 Africa Lag -2.128 0.056 0.043 -0.988* -0.002 -0.124 (-1.28) (-0.72) (-0.09) (-1.78) (-0.00) (-0.28) Vol -0.029 -0.349 2.95 -0.198 2.925 -9.559 (-0.40) (-1.17) (-0.67) (-1.49) (-0.77) (-1.00) Rm 0.001 0.0005 0.0005 0.002 0.003* -0.002 (-0.39) (-0.15) (-0.25) (-0.61) (-1.81) (-0.40) Vol*voice -0.354 (-0.94) Vol*political -2.22 (-0.64) Vol*gov 0.075 (-0.35) Vol*regu_qua 3.021 (-0.63) Vol*rule -7.084 (-1.00) cons 0.06 0.016 -0.017 0.04 -0.005 0.119 (-0.93) (-0.53) (-0.67) (-1.01) (-0.25) (-1.06) N 173 147 147 147 147 139 7 8 9 10 11 12 lagG -0.059 -2.113 -1.355 0.221 0.827 0.037 (-0.25) (-3.39) (.) (-0.79) (-0.44) (-0.08) vol -14.44 -5.663 -14.65 -2.549 -34.48* -0.356 (-2.11) (-2.63) (-2.69) (-0.65) (-1.88) (-0.38) Rm 0.003 -0.008* 0.003 -0.003 -0.022 0.001 (-1.05) (-1.70) (-1.04) (-0.28) (-0.73) (-0.28) Vol*controlcur -13.39 (-2.12) Vol*infla1 4.541 (-2.39) Vol*infla2 14.55 (-2.69) Vol*credit 0.075 (-0.59) Vol*liquid 0.797* (-1.75) Vol*stock_cap -0.086 (-0.78) Cons 0.117* 0.037 0.207 -0.026 -0.293 0.08 (-1.69) (-1.39) (-2.53) (-0.29) (-0.75) (-0.6) N 131 173 173 131 125 99 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 51
21. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 8b: Dynamic panel GMM (SYS) estimation results for three subsamples 1 2 3 4 5 6 South Asia and East Asia lag -0.688 -1.119 0.036 0.333 0.345 -1.695* (-0.42) (-0.71) (-0.25) (-2.16) (-2.09) (-1.76) Vol -4.864 -28.54 200.2 -53.97 -60.47 -40.84 (-0.33) (-0.74) (-1.06) (-1.31) (-1.17) (.) Rm -0.104 -0.08 -0.074 -0.055 -0.033* -0.131 (-0.87) (-1.22) (-1.09) (-3.20) (-1.74) (.) Vol*voice -41.3 (-0.96) Vol*political -223.2 (-1.15) Vol*gov -52.58 (-1.35) Vol*regu_qua -43.65 (-1.21) Vol*rule -43.81 (.) cons -0.978 -0.919 -1.332 -0.099 0.072 -1.306 (-0.99) (-1.43) (-1.01) (-0.35) (-0.17) (.) N 170 151 157 163 163 151 7 8 9 10 11 12 lag 0.2 -0.102 -0.11 -0.221 0.651 -0.367 (-1.37) (-0.29) (-0.32) (-0.63) (-0.67) (-0.49) Vol 180.9 36.98 -1.549 -2.852 18.14 -6.685 (-1.41) (-1.56) (-0.11) (-0.42) (-0.68) (-0.92) Rm 0.048 -0.002 -0.001 -0.05 0.006 -0.101 (-0.93) (-0.06) (-0.04) (-2.23) (-0.07) (-1.28) Vol*controlcur 159.1 (-1.33) Vol*infla1 -38.57 (-1.26) Vol*infla2 39.97 (-1.32) Vol*credit 0.051 (-0.55) Vol*liquid -0.07 (-0.18) Vol*stock_cap 0.179 (-0.9) cons -0.561* -0.397 -0.392 -0.514 -0.078 -0.856 (-1.91) (-1.23) (-1.23) (-2.26) (-0.09) (-1.32) N 157 164 164 157 157 111 t statistics in parenthese: * significant 10%, significant 5%, significant 1%. 52
22. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 8c: Dynamic panel GMM (SYS) estimation results for three subsamples 1 2 3 4 5 6 Latin America lagG -0.581 0.258 -0.797 -2.109 -0.586 -3.048 (-0.38) (-3.87) (-1.16) (-1.35) (-0.98) (-1.44) vol -5.944 -0.178 -4.118 -72.47 -3.163 -75.79 (-0.34) (-0.99) (-0.82) (-0.65) (-1.52) (-1.54) rm 0.07 0.0137 0.032* 0.096 0.037* 0.074 (-0.67) (-5.78) (-1.9) (-1.05) (-1.88) (-1.56) Vol*voice -0.592 (-0.98) Vol*political 4.563 (-0.8) Vol*gov -119.3 (-0.62) Vol*regu_qua -14.18 (-1.53) Vol*rule -76.61 (-1.54) cons 1.2 0.170 0.521 2.043 0.419* 1.444 (-0.54) (-6.48) (-1.6) (-0.97) (-1.85) (-1.63) N 215 196 194 201 191 196 7 8 9 10 11 12 lagG 0.161 0.364 -1.164 2.272 -7.202 -4.618 (-0.24) (-0.21) (-0.85) (-1.19) (-1.17) (-0.96) vol -3.451 -0.244 -9.85 -16.47 -46.03 -18.18 (-0.62) (-0.69) (-0.75) (-1.09) (-1.15) (-0.90) rm 0.017 0.182 0.118 -0.222 0.192 0.124 (-2.08) (-0.96) (-1.08) (-0.97) (-1.46) (-1.07) Vol*controlcur -3.293 (-0.62) Vol*infla1 -12.89 (-0.80) Vol*infla2 22.23 (-0.68) Vol*credit -0.195 (-1.13) Vol*liquid 1.098 (-1.16) Vol*stock_cap 0.516 (-0.9) cons 0.240 2.414 1.749 -0.38 2.156 1.457 (-2.7) (-0.96) (-0.95) (-0.59) (-1.43) (-1.08) N 196 215 215 196 196 176 t statistics in parenthese: * significant 10%, significant 5%, significant 1% . 53
23. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 Table 4a and Table 4b report the results for all 21 countries using GMM(DIF) and GMM(SYS), respectively. Tables 5a and Table 5b present the results for the subsample of low income and lower middle income countries and the subsample of upper middle income countries using GMM(DIF). While Table 6a and 6b present GMM(SYS) results for these samples, respectively. Tables 7a, 8a present the results for Sub-Saharan African countries, Tables 7b, 8b present the results for South Asian and East Asian countries, Tables 7c, 8c present the results for Latin American countries employing GMM(DIF) and GMM(SYS), respectively. In each Table, Columns 1 shows the unclear relationships between bank volatility, market excess return and economic growth (not statistically significant) in the full sample and in two subsamples based on income criteria. In the case of three subsamples based on geographic criteria, we find the negative effect of bank volatility on economic growth is very weak and marginally significant in South Asian and East Asian countries and Latin American countries. However, the result is robust concerning both estimation methods in Sub-Saharan African countries (most of coefficients are statistical significant). It means that a greater value of bank volatility, a smaller degree of economic growth. This result is support by findings of Moshirian and Wu (2012). It should be note that the results in groups of countries combined at different geography and in all countries combined together are not driven by a small number of Sub-Saharan African countries having evolving market-based system. Columns 2-12 in each table illustrate the results from adding interaction terms of bank volatility and indicators of country characteristics and financial development. In the full sample and in two subsamples based on income criteria, we find that the coefficient of the interaction of bank volatility with control of corruption is negative, and high levels of inflation is positive, but they are not statistically significant. Whereas others are ambiguous. It means that all variables are not good in explaining economic growth. That may result from combining all countries together. Therefore, the above results are inconsistent with the findings of Moshirian and Wu (2012) in developed markets and emerging markets. In contrast, In Saharan African countries we find that the coefficients of the interaction of bank volatility with voice and accountability, political stability and absence of violence, rule of law, control of corruption are negative in both estimation methods, indicating that these variables increase the negative impact of bank volatility on future economic growth. These results are not supported by Kaufmann, 2013 findings that WGI with higher values correspond to better governance outcomes. Furthermore, the coefficient of the interaction term between bank volatility and high levels of inflation is positive and statistically significant. This results imply that high levels of inflation weaken the association between bank volatility and economic growth. It is inconsistent with the finding of Bruno and Easterly (1998) maintain that a high levels of inflation would harm the economic growth. Besides, the coefficient of the interaction term between bank volatility and regulatory quality, private credit, liquid liabilities are positive. These indicators would weaken the negative connection between bank volatility and economic growth. They are consistent with related literatures. These results are good evidences of the effect of interaction terms on economic growth due to most of coefficients are statistically significant. Whereas, the effect of other variables is ambiguous in this subsample. In South Asian and East Asian countries, the interaction terms between bank volatility and voice and accountability, rule of law, low levels of inflation have negative signs in both estimation methods. These results are inconsistent with previous literatures findings that these variables have positive association with economic growth. Moreover, the interactions between bank volatility and political stability and absence of violence, liquid liabilities, stock market capitalization are positive. These results are supported by related literatures. However, the coefficient of bank volatility with high levels of inflation is positive, inconsistent with previous studies. Most of variables do not have strong relationship 54
24. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 due to most of coefficients are not statistical significant. We also find that the signs of other coefficients are mix in this subsample. In Latin American countries, we find that the coefficient of the interaction term between bank volatility and voice and accountability, government effectiveness, regulatory quality, control of corruption, low levels of inflation are negative in both methods, indicating that these variables exaggerate the negative effect of bank volatility on economic growth. However, these results are not much strong evidences of the effects of bank volatility and interaction terms on economic growth (not statistically significant). Furthermore, the coefficients of the interaction term between bank volatility and political stability and absence of violence, liquid liabilities, stock market capitalization are positive. These variables would weaken the negative connection between bank volatility and economic growth. However, they are not good in explaining the effects of bank volatility and other interaction terms on economic growth when coefficients are still not statistically significant. Some coefficients of other variables become mix in this subsample. In short, we find that the associations are stronger for African countries and not their Asian and Latin-American counterparts. Simply, we can interpret the above results that the growth effects of bank volatility and other indices are stronger in groups of countries where have almost built necessary infrastructures and governance structures to support an evolving market-based system. These results are consistent with the significant findings of Moshirian and Wu (2012) in developed markets and in emerging markets. These associations are clear, but not robust in groups of countries where have been successful in boosting their economic performances in their own ways but not in the standard models for the market economic success. They do not have the systems dominated by developed countries. Their strategies focus on three essential policy preconditions: sound macroeconomic management, peasant and small entrepreneurs. The achievements in these fields have overcome the lack of legal frameworks and well-developed market-based institutions. Whereas, the above relationships are unclear in groups of countries combined at different geography and in all countries combined together. These nexuses are not driven by a small number of Sub-Saharan African countries having evolving market-based system. 5. Conclusion There are evidences indicating the banking volatility-economic growth nexus in developed markets and emerging markets. However, it is not clear to assume this relationship across countries at various income criteria and geographic criteria in 21 low-income and middle-income countries from 2003 to 2014. This paper advances others when our sample has frontier markets, when we combine countries at different income levels, but in the same geography. We also advance previous ones when examining WGI, inflation rates as indicators to proxy for country characteristics in the model of bank volatility- economic growth. We find the unclear relationships between bank volatility, market excess return and economic growth in all countries combined together and in countries are grouped based on income criteria. In the case of three subsamples based on geographic criteria, we find the negative effect of bank volatility on economic growth is very weak and marginally significant in South Asian and East Asian countries and Latin American countries. However, the results are robust in Sub-Saharan African countries. It should be note that the results in the full sample and in two subsamples based on income criteria are not driven by a small number of outliers. When we add interaction terms of bank volatility and indicators of country characteristics and financial development. In the full sample of 21 economies and 2 subsamples based on income criteria. The evidence obtained in this study indicates that the effect other indicators on economic growth is unclear, except for negative effect of control of corruption and positive effect of high levels of inflation on economic growth, but they are not robust that may result from combining all countries together. 55
25. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 In Sub-Saharan African countries, we find that the interaction terms of bank volatility with voice and accountability, political stability and absence of violence, rule of law, control of corruption are negative. These variables magnify the negative link between bank volatility and economic growth. Whereas, the interaction between bank volatility and regulatory quality, high levels of inflation, private credit, liquid liabilities are positive. They relieve the negative impact of bank volatility on economic growth. It should be noted that most of variables are good in explaining economic growth in this subsample that may results from building almost necessary infrastructures and governance structures to support an evolving market-based system in this subsample. In the sample of South Asian and East Asian countries. The signs of the interaction terms of bank volatility with voice and accountability, rule of law, low levels of inflation are negative but political stability and absence of violence, high levels of inflation, liquid liabilities, stock market capitalization are positive. Whereas, the coeffictiens of other variables are unclear. In general, all results are not robust in the relationship with economic growth. Lastly, in the sample of Latin American countries. We also find that the interaction terms of bank volatility with voice and accountability, government effectiveness, regulatory quality, control of corruption, low levels of inflation are negative but political stability and absence of violence, liquid liabilities, stock market capitalization are positive. Overall, we find unclear impact of bank volatility on economic growth that may result from combining all countries together. When we combine countries across different geography, but in the same income group, this relationship is still mix. Surprisingly, the impacts of bank volatility on economic growth and the influences of country characteristics and financial development characteristics on this nexus are more clear when various countries are combined at the same geography, with the overall effects varying with the legal frameworks, institutional structure for market orientation in groups of countries. REFERENCES [1] Andrés, J., & Hernando, I. (1999). Does inflation harm economic growth? Evidence from the OECD. In The costs and benefits of price stability (pp. 315-348). University of Chicago Press. [2] Arellano, M., Bover, O., (1995). Another look at the instrumental variable estimation of error components models. Journal of Econometrics 68, 29-51. [3] Asante, S., Agyapong, D., & Adam, A. M. (2011). Bank competition, stock market and economic growth in ghana. International Journal of Business Administration, 2(4), 33. [4] Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking & Finance, 28(3), 423-442. [5] Blundell, R., Bond, S., Windmeijer, F., (2000). Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator. Advances in Econometrics 15, 53-91. [6] Bruno,M.,& Easterly,W. (1998). Inflation crises and long-run growth. Journal of Monetary Economics, 41(1), 3-26. [7] Campello, M., Graham, J.R., Harvey, C.R. (2010). The real effect of financial constraints: evidence from a financial crisis. Journal of Finance Economics, 97(3), 470–487. [8] Carlin, W., & Mayer, C. (2003). Finance, investment, and growth. Journal of financial Economics, 69(1), 191-226. [9] Cavenaile, L., Gengenbach, C., & Palm, F. (2014). Stock markets, banks and long run economic growth: A panel cointegration-based analysis. De Economist, 162(1), 19-40. [10] Cole, R. A., Moshirian, F., & Wu, Q. (2008). Bank stock returns and economic growth. Journal of Banking & Finance, 32(6), 995-1007. 56
26. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 [11] Campell,J.Y., Lettau, M., Malkiel, B.G., Xu, Y. (2001). Have Individual Stocks Become MoreVolatile? An Empirical Exploration of Idiosyncratic Risk. The Journal of Finance 56, 1-44. [12] Cooray, A. (2010). Do stock markets lead to economic growth? Journal of Policy Modeling, 32(4), 448-460. [13] Cornett, M. M., Guo, L., Khaksari, S., & Tehranian, H. (2010). The impact of state ownership on performance differences in privately-owned versus state-owned banks: An international comparison. Journal of Financial Intermediation, 19(1), 74-94. [14] Filippidis, I., & Katrakilidis, C. (2014). Institutions, policy and banking sector development: A reassessment. Finance a Úvěr, 64(6), 501-521. [15]Galvan, C, 2014. Top 10 most competitive economies in sub-Saharan Africa. [16] Harris, R. D. (1997). Stock markets and development: A re-assessment. European Economic Review, 41(1), 139-146. [17] Islam, R. (2010). Banks, stock markets and economic growth: Evidence from selected developing countries. Discussion papers. [18] Kaplan, S, 2012. What can Southeast Asia teach Africa about development. [19] Kim, D., & Lin, S. (2013). Interrelationships among banks, stock markets and economic growth: An empirical investigation. Applied Economics, 45(31/33), 4385-4394. [20] Kim, M. J. (2015). East Asia versus Latin America: The Literature in Review. The Sigma Iota Rho (SIR) Journal of International Relations. [21] Kong, T. (2011). Governance Quality and Economic Growth (No. 2011-537). Australian National University, College of Business and Economics, School of Economics. [22] La Porta, R., & Lopez-de-Silanes, F. (2002). A. Shleifer, 2002. Government ownership of banks. Journal of Finance, 57(1), 265-301. [23] Mihail Petkovski & Jordan Kjosevski (2014) Does banking sector development promote economic growth? An empirical analysis for selected countries in Central and South Eastern Europe, Economic Research-Ekonomska Istraživanja, 27:1, 55-66. [24] Moshirian, F., & Wu, Q. (2012). Banking industry volatility and economic growth. Research in International Business and Finance, 26(3), 428-442. [25] Munk, C. (2013). Financial asset pricing theory. OUP Oxford. [26] Nabi, M. S., & Suliman, M. O. (2009). Institutions banking development, and economic growth. The Developing Economies, 47(4), 436-457. [27] Naceur, S. B., & Ghazouani, S. (2007). Stock markets, banks, and economic growth: Empirical evidence from the MENA region. Research in International Business and Finance, 21(2), 297-315. [28] Ndlovu-Gatsheni, S. J. (2013). Why decoloniality in the 21st century. The thinker, 48(10). [29] Ndlovu-Gatsheni, S. J. (2013). Empire, global coloniality and African subjectivity. Berghahn Books. [30] Omoteso, K., & Ishola Mobolaji, H. (2014). Corruption, governance and economic growth in Sub-Saharan Africa: a need for the prioritisation of reform policies. Social Responsibility Journal, 10(2), 316-330. [31] Osamwonyi, I. O., & Kasimu, A. (2013). Stock market and economic growth in ghana, kenya and nigeria. International Journal of Financial Research, 4(2), 83-98. [32] Pani, M . IMF Survey: Strong Growth in Sub-Saharan Africa, but Pockets of Difficulty, 2014. 57
27. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 [34] Perez, G. H., Al Nasser, O. M., & Hajilee, M. (2015). Banking sector development and interest rate volatility in emerging economies. Applied Economics, 47(17), 1739-1747. [35] Rahimzadeh, F. (2012). Banking sector, stock market and economic growth: Evidence from mena1 countries. International Journal of Social Sciences and Humanity Studies, 4(1-2), 181-190. [36] Serwa, D. (2010). Larger crises cost more: Impact of banking sector instability on output growth. Journal of International Money and Finance, 29(8), 1463-1481. [37] Tripathy, S., & Pradhan, R. P. (2014). Banking sector development and economic growth in india. Global Business Review, 15(4), 767-776. [38] Wang, B., & Ajit, D. (2013). Stock market and economic growth in china. Economics Bulletin, 33(1), 95-103. 58