Banking industry volatility and economic growth

UNIVERSITY OF ECONOMICS ERAMUS UNIVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS BANKING INDUSTRY VOLATILITY AND ECONOMIC GROWTH A thesis submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRAN QUOC THANH Academic Supervisor: Assoc Prof Dr VO XUAN VINH HO CHI MINH CITY, December 2015 CERTIFICATION This is to certify that this thesis entitled “BANKING INDUSTRY VOLATILITY AND ECONOMIC GROWTH ”, which is submitted by me in fulfillment of the requirements for the degree of Master of Art in Development Economic to the Vietnam – The Netherlands Programme The thesis constitutes only my original work and due supervision and acknowledgement have been made in the text to all materials used 30th November 2015 Trần Quốc Thanh ACKNOWLEDGEMENT I would never have been able to finish my dissertation without the help and support of people surrounding me First and foremost, I would like to express my gratitude to my mentors Assoc Prof Dr Võ Xuân Vinh for the continuous support of my M.A study and research; for their patience, encouragement, erudite knowledge Their excellent guidance encouraged me in all the time of doing this study I have been strikingly lucky to have supervisors who cared so much my thesis, and answered to all my questions and queries punctually I could not have imagined having better supervisors and advisors for my research Besides my mentors, I would like to thank Dr Pham Khánh Nam, Dr Dương Như Hùng, and Prof Dr Ardeshir Sepehri for their thorough comments and worthy ideas that help to enhance my thesis’s value My sincere thanks also goes to all the lecturers at the Vietnam – Netherlands Program for their knowledge of all the courses, during the time I studied at the program I would like to offer my special thanks to lecturers in Data Center in University of Economic and Law, Dr Lê Văn Chơn, Dr Trương Đăng Thụy, lecturer Hoàng Trọng, who help me significantly in the courses and thesis writing processes In addition, I would like to express my great appreciation to my friends for their motivations Last but not the least; I owe a very important debt to my family for giving birth to me at the first place and supporting me spiritually throughout my life Hô Chi Minh city, December 2015 ABSTRACT There is growing evidence from multi- studies indicating that there are lots of determinants advocate to economic growth However, very few research papers contribute to banking sector, vital field of modern economy It is unclear whether it is appropriate to assume an identical turning point in the banking industry volatility and growth relation divided into across income criteria and geographical region criteria In this research, we keep investigating the relationship between banking volatility and economic growth in detail ways after examining carefully the studies of Moshirian & Wu, (2012); Lin & Huang, (2012) Using GMM techniques for dynamic panel data to analyze one main group and five subsamples: all 22 economies, 11 upper middle income economies, 11 low income and lower middle income economies, Sub-Saharan Africa economies, South Asia and East Asia economies, Latin America economies, by using dynamic panel techniques to analyze panel data Particularly, we pay more attention on the way country characteristics, such as the effect of low and high inflation, Worldwide Governance Indicators (WGI) from the updated database of Kaufmann (2013) and financial development characteristics influence the relationship between bank volatility and economic growth The quarterly panel dataset, which is available and easy approach from international Datastream The simple correlation between GDP growth rates and banking volatility is slightly higher in geographic region groups There is relationship of banking industry volatility and economic growth in all 22 economies, and in five subsamples divided into income criteria and geographical region criteria, even in the presence of market excess returns, and the relationship between banking volatility and economic growth is affected by the country characteristics and financial development when the interaction terms have statistical significant Except for Voice and Accountability having no effect Some research papers of Fama (1981, 1990) and Schwert (1990) have proved that the effect of the uncertainty of banking industry on economic growth is uncorrelated with the effect of the market stock return in general on economic growth Hence, our results is more one evidence for the relationship between the stock returns of bank and economic growth Key words: banking volatility, difference GMM, system GMM, country characteristics, financial development characteristics, effect of inflation TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problems statement: .6 1.2 Research objectives: .8 1.3 Main research questions 1.4 Structure of the thesis: CHAPTER 2: LITERATURE REVIEW 10 2.1 Banking industry volatility and economic growth .10 2.2 Indicators of country characteristics 12 2.3 Financial indicators and real activity 16 2.4 Stock markets and economic growth 17 2.5 Conceptual framework: 19 CHAPTER 3: METHODOLOGY, MODEL SPECIFICATION AND DATA 20 3.1 Data 20 3.2 Methodology 27 CHAPTER 4: RESULTS AND FINDINGS: 30 4.1 Descriptive statistics of variables: 30 4.2 Econometric results 33 CHAPTER 5: POLICY IMPLICATION, CONCLUSION AND LIMITATION: .60 4.1 Policy implication and conclusion: 60 4.2 Limitation of the research: 62 REFERENCES 64 APPENDICES : Table of empirical studies relating to economic growth rate: .68 Appendix 1: Full sample of all 22 economies 74 Appendix 2: 11 Upper middle income economies 98 Appendix 3: 11 Low income and 11 lower middle income economies 122 Appendix 4: Africa economies 146 Appendix 5: Asia economies 170 Appendix 6: Latin America economies 194 ABBREVIATIONS WGI: World Governnance Indicator EMH: The Efficient market hypothesis GDP: Gross Domestic Product GNI: Gross National Income GMM: The Generalized Method of Moments Estimation GMM(DIF): The Difference Generalized Method of Moments Estimation GMM(SYS): The system Generalized Method of Moments Estimation CHAPTER 1: INTRODUCTION 1.1 Problems statement Very few research papers contribute to banking sector, vital field of modern economy It is evident that numerous research papers for many decades have shown financial development as important channel for economic growth Banks play a crucial role in the economic growth of a country by allocating funds among all sectors, primary sectors, secondary sectors, tertiary sector, etc Most of existing researches focus on performance of the bank, liquid liabilities to GDP, market capitalization per GDP, credit to private sector per GDP, etc as factors of financial development Bank sector play crucial role in supply facilities through deposit and lending, credit, banking services, money transfer, etc to economic activities However, very few researches measure directly the effect of banking operation on economic growth Serwa (2010) indicates that banking crises cause output growth to slow down A well-functioning banking system facilitates infrastructure for other sectors running smoothly Therefore, banking stock return will be reflected in the quality of bank credits According to Bruner & Simms (1987); Cornell & Shapiro 1986, the market for commercial bank securities operating are efficient and contain information about the quality of bank loan portfolios There are close relationship between bank stock returns and economic growth Base on asset-pricing theory, and on many researches of economists Cole, et al (2008); Moshirian & Wu, (2012); Lin & Huang, (2012), prove that stock returns of banking industry reflecting the performance of the bank can predict economic growth In addition, according to the view of market efficiency, at any point in time, prices of securities in efficient markets reflect all known information available to investors In other words, the expected future cash flows of the banks are reflected in the present stock price This depends on efficiency of loan projects Bank stock returns will reflect the efficiency of the market in using funds to investment Furthermore, in most of countries, commercial banks, PLCs, are broadly representative of country’s banking sector since they account for very high position in the whole banking system Consequently, there are correlation between bank stock returns and future economic growth In some research of Cornett (2010) and Naceur and Ghazouani (2007), institutional framework, such as country specific, financial system indicators also have significant influence on banking operations Moreover, in the investigation of Asante, S., Agyapong, D., & Adam, A M (2011), country characteristics help banks operate smoothly as well as improve their services This promotes economic growth significantly In the indicators representative country specific, the negative effects of inflation have been studied in a lot of models of economic growth, it undermines the confidence of domestic and foreign investors as well as consumers about the future economic growth (Andrés & Hernando, 1999) Secondly, the sustainable increase in living standard for a country means a larger voice on the world stage There are a lot of measures of the quality of governance have been built to evaluate of the quality of governance, among these are the Worldwide Governance Indicators, six institutional variables rank countries on six aspects of good governance (Kaufmann, 2013) Besides, the impact of banking stock returns on economic growth is captured by country characteristics and financial development (cole, et al, 2008; Moshirian & Wu, 2012) According to the point of view that banking operation contains information about performances of a lot of sectors reflecting the health of the economy (cole, et al, 2008; Moshirian & Wu, 2012; Lin & Huang, 2012) It is indicate that the relationship between banking industry volatility and economic growth that is independent of the information contained by overall market returns Since the volatility of the bank relate to the variation of stock returns of the banking industry which refer to each individual bank Therefore, this information should be independent of information being reflected in market excess returns which is representative for the whole public limited company (PLCs) in the stock market (Oshiriana & 2012; Lin & Huang 2012) Similarly, Naceur & Ghazouani (2007) indicate that the impact of equity market on growth is independent to the impact of bank development on growth Publicly traded banks also account for high proportion in the whole, this lead to banking industry stock returns will represent for the whole banking sector in the most of countries In the researches on banking industry volatility and economic growth topic Moshirian & Wu, (2012), the investigators concentrate more on country classified Dynamic panel-data estimation, two-step difference GMM Group variable: id Time variable : year Number of instruments = 194 Wald chi2(4) = 759.72 Prob > chi2 = 0.000 growth Coef lagG vol rm volcredit -1.359689 -.0023564 -.0006591 Number of obs Number of groups Obs per group: avg max Corrected Std Err .428259 (omitted) 0015606 0002073 z P>|z| = = = = = 201 37 40.20 41 [95% Conf Interval] -3.17 0.001 -2.199061 -.5203165 -1.51 -3.18 0.131 0.001 -.0054151 -.0010653 0007023 -.0002529 Instruments for first differences equation Standard D.(rm volcredit) GMM-type (missing=0, separate instruments for each period unless collapsed) L(2/4).(lagG L3.vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Pr > z = Pr > z = 0.141 0.106 Prob > chi2 = 0.444 Prob > chi2 = 1.000 Difference-in-Hansen tests of exogeneity of instrument subsets: iv(rm volcredit) Hansen test excluding group: chi2(188) = 0.08 Prob > chi2 = Difference (null H = exogenous): chi2(2) = 0.01 Prob > chi2 = 1.000 0.994 Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(190) = 192.10 but not weakened by many instruments.) overid restrictions: chi2(190) = 0.09 weakened by many instruments.) -1.47 -1.62 Dynamic panel-data estimation, two-step difference GMM Group variable: id Time variable : year Number of instruments = Wald chi2(4) = 76.38 Prob > chi2 = 0.000 growth Coef lagG vol rm volliquid -.8402831 -6.328746 -.0017417 1465289 Number of obs Number of groups Obs per group: avg max Corrected Std Err .2640528 2.97252 0058331 0695534 z -3.18 -2.13 -0.30 2.11 P>|z| 0.001 0.033 0.765 0.035 = = = = = 197 37 39.40 40 [95% Conf Interval] -1.357817 -12.15478 -.0131744 0102068 -.3227492 -.5027138 009691 282851 Instruments for first differences equation Standard D.(L.rm L2.volliquid) GMM-type (missing=0, separate instruments for each period unless collapsed) L(4/5).(lagG vol) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Pr > z = Pr > z = 0.103 0.150 Prob > chi2 = 0.963 Prob > chi2 = 0.384 Difference-in-Hansen tests of exogeneity of instrument subsets: iv(L.rm L2.volliquid) Hansen test excluding group: chi2(0) = 0.00 Prob > chi2 = Difference (null H = exogenous): chi2(2) = 1.91 Prob > chi2 = 0.384 Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(2) = 0.07 but not weakened by many instruments.) overid restrictions: chi2(2) = 1.91 weakened by many instruments.) -1.63 -1.44 Dynamic panel-data estimation, two-step difference GMM Group variable: id Time variable : year Number of instruments = 165 Wald chi2(4) = 131.25 Prob > chi2 = 0.000 growth Coef lagG vol rm volstock_cap -1.435178 -.1277735 0037044 0056441 Number of obs Number of groups Obs per group: avg max Corrected Std Err .4971309 0368113 0040729 0019117 z -2.89 -3.47 0.91 2.95 P>|z| 0.004 0.001 0.363 0.003 = = = = = 165 33 33.00 33 [95% Conf Interval] -2.409537 -.1999224 -.0042782 0018972 -.4608198 -.0556246 0116871 0093909 Instruments for first differences equation Standard D.(L5.rm L5.volstock_cap) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/5).(lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Pr > z = Pr > z = 0.074 0.218 Prob > chi2 = 0.228 Prob > chi2 = 1.000 Difference-in-Hansen tests of exogeneity of instrument subsets: iv(L5.rm L5.volstock_cap) Hansen test excluding group: chi2(159) = 2.11 Prob > chi2 = Difference (null H = exogenous): chi2(2) = -2.10 Prob > chi2 = 1.000 1.000 Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(161) = 174.05 but not weakened by many instruments.) overid restrictions: chi2(161) = 0.02 weakened by many instruments.) -1.79 -1.23 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 225 Wald chi2(3) = 8.17 Prob > chi2 = 0.043 growth Coef lagG vol rm _cons -.3873748 0034644 0000894 0096433 Number of obs Number of groups Obs per group: avg max Corrected Std Err .2340133 0080612 0004 0025611 z -1.66 0.43 0.22 3.77 P>|z| 0.098 0.667 0.823 0.000 = = = = = 225 42 45.00 46 [95% Conf Interval] -.8460324 -.0123352 -.0006946 0046236 0712828 019264 0008734 0146631 Instruments for first differences equation Standard D.rm GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(lagG vol) Instruments for levels equation Standard rm _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(221) = 239.82 but not weakened by many instruments.) overid restrictions: chi2(221) = 0.85 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(rm) Hansen test excluding group: Difference (null H = exogenous): -1.34 0.75 Pr > z = Pr > z = 0.181 0.454 Prob > chi2 = 0.183 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(149) chi2(72) = = 1.67 -0.82 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(220) chi2(1) = = 0.32 0.52 Prob > chi2 = Prob > chi2 = 1.000 0.470 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 194 Wald chi2(4) = 9.05 Prob > chi2 = 0.060 growth Coef lagG vol rm volvoice _cons -1.576921 8118113 -.0002593 3.035575 0013634 Number of obs Number of groups Obs per group: avg max Corrected Std Err .8304 502829 0011907 1.948918 0194103 z -1.90 1.61 -0.22 1.56 0.07 P>|z| 0.058 0.106 0.828 0.119 0.944 = = = = = 206 38 41.20 42 [95% Conf Interval] -3.204475 -.1737155 -.0025932 -.7842335 -.0366802 0506332 1.797338 0020745 6.855384 039407 Instruments for first differences equation Standard D.(L.rm L.volvoice) GMM-type (missing=0, separate instruments for each period unless collapsed) L(5/7).(growth vol) Instruments for levels equation Standard L.rm L.volvoice _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL4.(growth vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(189) = 206.62 but not weakened by many instruments.) overid restrictions: chi2(189) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L.rm L.volvoice) Hansen test excluding group: Difference (null H = exogenous): -1.43 -1.51 Pr > z = Pr > z = 0.154 0.132 Prob > chi2 = 0.180 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(145) chi2(44) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(187) chi2(2) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 192 Wald chi2(4) = 20.55 Prob > chi2 = 0.000 growth Coef lagG vol rm volpolitical _cons -1.340772 -1.456354 0020833 1.462954 0895604 Number of obs Number of groups Obs per group: avg max Corrected Std Err .7359782 1.034049 0013583 1.038483 055781 z -1.82 -1.41 1.53 1.41 1.61 P>|z| 0.068 0.159 0.125 0.159 0.108 = = = = = 200 38 40.00 41 [95% Conf Interval] -2.783263 -3.483054 -.0005789 -.5724348 -.0197684 101719 5703453 0047454 3.498342 1988891 Instruments for first differences equation Standard D.(L2.rm volpolitical) GMM-type (missing=0, separate instruments for each period unless collapsed) L(5/6).(lagG vol) Instruments for levels equation Standard L2.rm volpolitical _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL4.(lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(187) = 205.52 but not weakened by many instruments.) overid restrictions: chi2(187) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L2.rm volpolitical) Hansen test excluding group: Difference (null H = exogenous): -2.91 -1.76 Pr > z = Pr > z = 0.004 0.078 Prob > chi2 = 0.168 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(125) chi2(62) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(185) chi2(2) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 199 Wald chi2(4) = 6.52 Prob > chi2 = 0.164 growth Coef lagG vol rm volgov _cons -1.330156 7806483 -.0006488 1.315814 0009007 Number of obs Number of groups Obs per group: avg max Corrected Std Err .6410581 5002361 0007146 817025 0135713 z -2.07 1.56 -0.91 1.61 0.07 P>|z| 0.038 0.119 0.364 0.107 0.947 = = = = = 202 38 40.40 41 [95% Conf Interval] -2.586607 -.1997965 -.0020494 -.2855259 -.0256987 -.0737054 1.761093 0007519 2.917153 0275 Instruments for first differences equation Standard D.(L2.rm L.volgov) GMM-type (missing=0, separate instruments for each period unless collapsed) L(3/4).(L.lagG vol) Instruments for levels equation Standard L2.rm L.volgov _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL2.(L.lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(194) = 206.60 but not weakened by many instruments.) overid restrictions: chi2(194) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L2.rm L.volgov) Hansen test excluding group: Difference (null H = exogenous): -1.97 -1.52 Pr > z = Pr > z = 0.048 0.128 Prob > chi2 = 0.255 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(130) chi2(64) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(192) chi2(2) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 202 Wald chi2(4) = 14.10 Prob > chi2 = 0.007 growth Coef lagG vol rm volregu_qua _cons -34.296 -13.69583 0623045 -47.55735 9565137 Number of obs Number of groups Obs per group: avg max Corrected Std Err 23.18841 9.853779 0446911 34.21657 6703365 z -1.48 -1.39 1.39 -1.39 1.43 P>|z| 0.139 0.165 0.163 0.165 0.154 = = = = = 202 38 40.40 41 [95% Conf Interval] -79.74444 -33.00888 -.0252884 -114.6206 -.3573218 11.15244 5.617222 1498974 19.50591 2.270349 Instruments for first differences equation Standard D.(L.rm L2.volregu_qua) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/46).(L.lagG vol) Instruments for levels equation Standard L.rm L2.volregu_qua _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(197) = 214.22 but not weakened by many instruments.) overid restrictions: chi2(197) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L.rm L2.volregu_qua) Hansen test excluding group: Difference (null H = exogenous): 0.14 Pr > z = Pr > z = 0.886 Prob > chi2 = 0.190 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(184) chi2(13) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(195) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 206 Wald chi2(4) = 15.01 Prob > chi2 = 0.005 growth Coef lagG vol rm volrule _cons 0430994 3.9973 -.0065998 3.521012 -.1110067 Number of obs Number of groups Obs per group: avg max Corrected Std Err .3687875 2.475744 0037623 2.19059 0722396 z 0.12 1.61 -1.75 1.61 -1.54 P>|z| 0.907 0.106 0.079 0.108 0.124 = = = = = 206 38 41.20 42 [95% Conf Interval] -.6797108 -.8550695 -.0139738 -.772465 -.2525937 7659097 8.84967 0007742 7.814488 0305803 Instruments for first differences equation Standard D.(L.rm L.volrule) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(lagG vol) Instruments for levels equation Standard L.rm L.volrule _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(201) = 218.05 but not weakened by many instruments.) overid restrictions: chi2(201) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L.rm L.volrule) Hansen test excluding group: Difference (null H = exogenous): -2.14 Pr > z = Pr > z = 0.033 Prob > chi2 = 0.195 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(136) chi2(65) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(199) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 206 Wald chi2(4) = 15.11 Prob > chi2 = 0.004 growth Coef lagG vol rm volcontrol_cur _cons -.0186764 2.7196 -.0070109 2.174541 -.1109286 Number of obs Number of groups Obs per group: avg max Corrected Std Err .3349939 1.68812 0040151 1.360442 0722374 z P>|z| -0.06 1.61 -1.75 1.60 -1.54 0.956 0.107 0.081 0.110 0.125 = = = = = 206 38 41.20 42 [95% Conf Interval] -.6752524 -.5890547 -.0148803 -.4918758 -.2525113 6378997 6.028254 0008585 4.840958 030654 Instruments for first differences equation Standard D.(L.rm L.volcontrol_cur) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(lagG vol) Instruments for levels equation Standard L.rm L.volcontrol_cur _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D (lagG vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(201) = 218.05 but not weakened by many instruments.) overid restrictions: chi2(201) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L.rm L.volcontrol_cur) Hansen test excluding group: Difference (null H = exogenous): -1.96 Pr > z = Pr > z = 0.050 Prob > chi2 = 0.195 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(136) chi2(65) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(199) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = Wald chi2(4) = 191.46 Prob > chi2 = 0.000 growth Coef lagG vol rm volinfla1 _cons -1.599841 -.3952668 0009861 2837677 0389512 Number of obs Number of groups Obs per group: avg max Corrected Std Err .3079734 2139445 0009725 1950628 0114238 z -5.19 -1.85 1.01 1.45 3.41 P>|z| 0.000 0.065 0.311 0.146 0.001 = = = = = 209 41 41.80 42 [95% Conf Interval] -2.203457 -.8145903 -.0009199 -.0985484 0165611 -.996224 0240567 0028922 6660839 0613414 Instruments for first differences equation Standard D.(L5.rm L4.volinfla1) GMM-type (missing=0, separate instruments for each period unless collapsed) L6.(lagG vol) collapsed Instruments for levels equation Standard L5.rm L4.volinfla1 _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL5.(lagG vol) collapsed Arellano-Bond test for AR(1) in first differences: z = -11.33 Arellano-Bond test for AR(2) in first differences: z = -1.45 Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(2) = 0.80 but not weakened by many instruments.) overid restrictions: chi2(2) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L5.rm L4.volinfla1) Hansen test excluding group: Difference (null H = exogenous): Pr > z = Pr > z = 0.000 0.146 Prob > chi2 = 0.669 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(0) chi2(2) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 chi2(0) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = Wald chi2(4) = 1719.92 Prob > chi2 = 0.000 growth Coef lagG vol rm volinfla2 _cons -1.106027 -.1138877 001892 -.7747245 0448608 Number of obs Number of groups Obs per group: avg max Corrected Std Err .0711819 0738762 0016956 7567829 0262345 z -15.54 -1.54 1.12 -1.02 1.71 P>|z| 0.000 0.123 0.265 0.306 0.087 = = = = = 209 41 41.80 42 [95% Conf Interval] -1.245541 -.2586824 -.0014313 -2.257992 -.0065578 -.9665126 0309069 0052153 7085428 0962794 Instruments for first differences equation Standard D.(L5.rm L4.volinfla1) GMM-type (missing=0, separate instruments for each period unless collapsed) L6.(lagG vol) collapsed Instruments for levels equation Standard L5.rm L4.volinfla1 _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL5.(lagG vol) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(2) = 2.64 but not weakened by many instruments.) overid restrictions: chi2(2) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L5.rm L4.volinfla1) Hansen test excluding group: Difference (null H = exogenous): -1.77 -1.56 Pr > z = Pr > z = 0.076 0.120 Prob > chi2 = 0.267 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(0) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 chi2(0) chi2(2) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = 203 Wald chi2(4) = 6.05 Prob > chi2 = 0.196 growth Coef lagG vol rm volcredit _cons -7.76368 -8.237733 0011356 3171886 0693814 Number of obs Number of groups Obs per group: avg max Corrected Std Err 6.136382 5.879644 0012334 2264675 0454252 z -1.27 -1.40 0.92 1.40 1.53 P>|z| 0.206 0.161 0.357 0.161 0.127 = = = = = 206 38 41.20 42 [95% Conf Interval] -19.79077 -19.76162 -.0012818 -.1266795 -.0196503 4.263407 3.286158 003553 7610568 1584131 Instruments for first differences equation Standard D.(rm volcredit) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/46).(L.lagG L2.vol) Instruments for levels equation Standard rm volcredit _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.lagG L2.vol) Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(198) = 219.70 but not weakened by many instruments.) overid restrictions: chi2(198) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(rm volcredit) Hansen test excluding group: Difference (null H = exogenous): -1.65 0.82 Pr > z = Pr > z = 0.099 0.412 Prob > chi2 = 0.139 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(183) chi2(15) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 chi2(196) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = Wald chi2(4) = 152.78 Prob > chi2 = 0.000 growth Coef lagG vol rm volliquid _cons -1.340769 -1.078032 0013918 0255041 0291109 Number of obs Number of groups Obs per group: avg max Corrected Std Err .1949017 8013895 0009289 0186938 009924 z -6.88 -1.35 1.50 1.36 2.93 P>|z| 0.000 0.179 0.134 0.172 0.003 = = = = = 198 38 39.60 40 [95% Conf Interval] -1.722769 -2.648727 -.0004288 -.011135 0096601 -.9587684 4926622 0032123 0621432 0485616 Instruments for first differences equation Standard D.(L3.rm L3.volliquid) GMM-type (missing=0, separate instruments for each period unless collapsed) L7.(lagG vol) collapsed Instruments for levels equation Standard L3.rm L3.volliquid _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(lagG vol) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(2) = 1.41 but not weakened by many instruments.) overid restrictions: chi2(2) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(L3.rm L3.volliquid) Hansen test excluding group: Difference (null H = exogenous): -2.43 -1.41 Pr > z = Pr > z = 0.015 0.157 Prob > chi2 = 0.494 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(0) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 chi2(0) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 Dynamic panel-data estimation, two-step system GMM Group variable: id Time variable : year Number of instruments = Wald chi2(4) = 534.35 Prob > chi2 = 0.000 growth Coef lagG vol rm volstock_cap _cons -.6928669 -.145761 0001472 0039274 0148417 Number of obs Number of groups Obs per group: avg max Corrected Std Err .1090443 0470129 0004046 001291 0039257 z -6.35 -3.10 0.36 3.04 3.78 P>|z| 0.000 0.002 0.716 0.002 0.000 = = = = = 186 34 37.20 38 [95% Conf Interval] -.9065898 -.2379046 -.0006458 0013971 0071475 -.4791441 -.0536175 0009403 0064577 0225358 Instruments for first differences equation Standard D.(rm volstock_cap) GMM-type (missing=0, separate instruments for each period unless collapsed) L4.(L.lagG vol) collapsed Instruments for levels equation Standard rm volstock_cap _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL3.(L.lagG vol) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(2) = 0.01 but not weakened by many instruments.) overid restrictions: chi2(2) = 0.00 weakened by many instruments.) Difference-in-Hansen tests of exogeneity instruments for levels Hansen test excluding group: Difference (null H = exogenous): iv(rm volstock_cap) Hansen test excluding group: Difference (null H = exogenous): -1.69 -1.67 Pr > z = Pr > z = 0.091 0.094 Prob > chi2 = 0.994 Prob > chi2 = 1.000 of instrument subsets: GMM chi2(0) chi2(2) = = 0.00 0.00 Prob > chi2 = Prob > chi2 = 1.000 chi2(0) chi2(2) = = 0.00 -0.00 Prob > chi2 = Prob > chi2 = 1.000 MISS TRẦN QUỐC THANH - VNP 20 ... between bank volatility and future economic growth Bankingcrisis and banking accounting standards make the relationship between bank-excess-returns and future economic growth to be positive and make... characteristics and financial development impact on the relationship between bank volatility and economic growth 1.3 Main research question: - Is there any relationship between banking industry volatility and. .. 2.1 Banking industry volatility and economic growth .10 2.2 Indicators of country characteristics 12 2.3 Financial indicators and real activity 16 2.4 Stock markets and economic