Empirical studies on the volatility of china stock market

逢 甲 大 學 金融博士學位學程 博士論文 中國股市波動之實證研究 Empirical studies on the volatility of China stock market 指導教授：吳仰哲教授 : 翁慈青 教授 研 究 生 ：王氏香江 中華民國一百一十年一月 Empirical studies on the volatility of China stock market ACKNOWLEDGEMENTS I would like to express my sincere thanks to the Chair of the Ph.D Finance program, the Director of Finance College, Feng Chia University’s administration for creating all favorable conditions for me to complete this thesis Most important, I would like to thanks both Professors Yang-Che Wu and Tzu-Ching Weng guided enthusiastically me to carry out my thesis step by step During my studying process in Taiwan, I highly appreciate their contributions for time, subsidies, and inspiration ideas to me They taught me a lot of knowledge in the finance and accounting fields Especially, they have always encouraged and supported me to perform the Ph.D Finance program Their successes and passion for researching inspired me to complete my thesis I appreciate Professor Richard Lu, Li-Jiun Chen, Nathan Liu, Thomas Chinan Chiang, Wei-Feng Hung, Yi-Ting Hsieh, Shin-Heng Michelle Chu Their classes provided me with a lot of specialized knowledge about econometrics and finance Professor Richard Lu who always welcomes all students if we need any helps I am very impressed with his outdoor trips for all Ph.D students to give memorable memories in Taiwan country I would like to express my sincere thanks to my classmate, namely, Huu Manh Nguyen for his help and collaborative assistance in the thesis During two academic years, he taught me basic knowledge in the financial field that I have ever not known because before my studies focus mainly on the accounting field In our teamwork, he always enthusiastically guided me to how present in my studies, my presentations in the best way With his bits of help, I obtain more knowledge, better skills in my research For the MATLAB code, Uyen Kim Nguyen who graduated Master IT program a Feng Chia University has significant contributions to my empirical results She helps me how to write code in MATLAB software to solve the ICSS algorithm in the methodology sector of the first study I appreciate her time and her effort in my thesis I would like to thank Finance College’s assistant who was ready to help with any works related to us in Taiwan and arranged this thesis defense Because of i FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market language limits, I cannot write exactly her name but I hope that she may get my gratefulness About the final defense, I am grateful to committee members: Professor YangChe Wu, Professor Tzu-Ching Weng, Professor Tsang-Yao Chang, Professor YuChih Lin and Professor Meng-Fen Hsieh for their time, attention, and insightful suggestions for completing this thesis Finally, I express all thanks to my family in Vietnam I am so grateful for my parents who encourage me to pursue the Ph.D Finance program at Feng Chia University Especially for my mother who helps me to take care of my daughter during the long period of the Ph.D Finance program I am so appreciated Thank you for all! Vuong Thi Huong Giang Feng Chia University January 2021 ii FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market ABSTRACT The volatility of the stock market returns needs to be carefully considered because it relates closely to the degree of risking contagion between the equity markets and the adjustment on the capital structure of listed firms In the macro aspect, the first study examines the bidirectional volatility spillovers between the US and China stock markets in the post-2000 period We employ a variant model of EGARCH (1,1) with controlling the excessive volatility points that are detected by the ICSS algorithm Our results imply the barriers in the bilateral US-China relationship and foreign investment’s restrictions in China’s financial market have distinctly influenced the bidirectional volatility infections Most crucially, we indicate that the global financial crisis exposed the majority volatility contagion from the US to China stock market while the Covid-19 pandemic strongly promoted the volatility infection from China to the US equity market in March 2020 In a micro aspect, an essential issue of listed firms is adjusting their market leverages as the volatility of the stock market returns increases Our paper examines this concern on the biggest stock exchange of China market covering 2008 to 2018 in a panel model The volatility of Chinese stock market returns immediately has positive impacts on both total market leverage and short-term market leverage, but a negative influence on the long-term market leverage of Chinese listed firms We indicate that in this situation, Chinese listed firms adjust their debt structure by employing more bank debts and cutting trade credit Finally, we present robust evidence that the proportion of bank debts in total debts visibly increases while the ratio of trade credit in total debts distinctly reduces Furthermore, we implement robust tests regarding potential issues such as sample selection, model selection, endogenous factors, and apply quantile regression (QR) to enhance the robustness of our empirical results Keywords: US stock market, China stock market; Bidirectional volatility spillovers; ICSS algorithm; EGARCH (1,1) model; Capital structure; Panel model iii FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market CONTENTS ACKNOWLEDGEMENTS i ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES .viii STUDY I: THE BIDIRECTIONAL VOLATILITY SPILLOVERS BETWEEN THE US AND CHINA STOCK MARKETS 1.1 Introduction 1.1.1 Research background 1.1.2 Research motivations and Research contributions 1.1.3 Research structure 1.2 Literature review 1.3 Sample, Methodology and Empirical models 1.3.1 Sample 1.3.2 ICSS algorithm to detect structural breakpoints in the variance of volatility source’s returns 1.3.3 Modeling the bidirectional volatility spillovers between the US and China stock markets 11 1.4 Analyzing empirical results on the bidirectional volatility spillovers between the US and China stock markets 13 1.4.1 Basic analysis 13 1.4.2 Empirical results on the volatility spillovers from the US to China stock market 15 1.4.2.1 Modeling the volatility of Shanghai Composite’s returns by using structural breakpoints in the variance of US stock market returns 15 iv FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market 1.4.2.2 Modeling the volatility of Shenzhen Composite’s returns by using structural breakpoints in the variance of US stock market returns 17 1.4.2.3 Modeling the volatility of Chinese stock market returns by using the variance of US stock market returns 18 1.4.3 Empirical results on the volatility spillovers from the China to US stock market 19 1.4.3.1 Modeling the volatility of S&P500’s returns by using structural breakpoints in the variance of Chinese stock market returns 19 1.4.3.2 Modeling the return volatility of other US indexes by using structural breakpoints in the variance of Chinese stock market returns 20 1.4.3.3 Modeling the volatility of US stock market returns by using the variance of Chinese stock market returns 21 1.5 Conclusion and Recommendation 23 References 24 Appendix A I 37 Appendix B I 30 STUDY II: 43 THE VOLATILITY OF CHINESE STOCK MARKET RETURNS AND CAPITAL STRUCTURE OF CHINESE LISTED FIRMS 44 2.1 Introduction 44 2.1.1 Research background 44 2.1.2 Research motivations and Research contributions 46 2.1.3 Research structure 48 2.2 Literature review 48 2.3 Data, Empirical models and Variables 52 2.3.1 Data 52 2.3.2 Empirical models and Variables 52 v FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market 2.4 Analyzing the volatility impact of Chinese stock market returns on the adjusting capital structure of Chinese listed firms 55 2.4.1 The volatility impact of Chinese stock market returns on market leverages of Chinese listed firms 55 2.4.2 The volatility impact of Chinese stock market returns on bank debts of Chinese listed firms 58 2.4.3 The volatility impact of Chinese stock market returns on trade credit of Chinese listed firms 61 2.4.4 Robust checks 63 2.4.4.1 Sample selection 63 2.4.4.2 Model selection 64 2.4.4.3 Endogenous factors 65 2.4.4.4 Using quantile regression (QR) 66 2.5 Conclusion and Recommendation 67 References 68 Appendix A II 72 Appendix B II 73 vi FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market LIST OF FIGURES LIST OF FIGURES IN STUDY I 27 Figure 1.1 Examining the bidirectional volatility spillovers between the US and China stock markets 27 Figure 1.2 The structural breakpoints in the variance of US stock market returns are detected by the ICSS algorithm (2001–10/2020) 28 Figure 1.3 The structural breakpoints in the variance of Chinese stock market returns are detected by the ICSS algorithm (2001–10/2020) 29 LIST OF FIGURES IN STUDY II 72 Figure 2.1 The volatility of Chinese stock market returns per year and annual China’s lending interest rate (2001-2019) 72 vii FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market LIST OF TABLES LIST OF TABLES IN STUDY I 30 Table 1.1: Descriptive statistics 30 Table 1.2: Unit root tests .31 Table 1.3: Break dates corresponding to structural breakpoints are detected in the variance of stock market returns using the ICSS algorithm (2001-10/2020) .32 Table 1.4: Modeling the volatility of stock market returns without using the detected structural breakpoints (2001-10/2020) 34 Table 1.5: Modeling the volatility of SSEC’s returns using structural breakpoints in the variance of US stock market returns (2001-10/2020) 35 Table 1.6: Modeling the volatility of SZSC’s returns using structural breakpoints in the variance of US stock market returns (2001-10/2020) 37 Table 1.7: Modeling the volatility of Chinese stock market returns by using the variance of US stock market returns .39 Table 1.8: Modeling the volatility of S&P500’s returns using structural breakpoints in the variance of Chinese stock market returns (2001-10/2020) 40 Table 1.9: Modeling the volatility of DJIA’s returns using structural breakpoints in the variance of Chinese stock market returns (2001-10/2020) .41 Table 1.10: Modeling the volatility of Nasdaq Composite’s returns using structural breakpoints in the variance of Chinese stock market returns (2001-10/2020) .42 Table 1.11: Modeling the volatility of US stock market returns by using the variance of Chinese stock market returns .43 viii FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market LIST OF TABLES IN STUDY II .73 Table 2.1: Definition of variables 73 Table 2.2: Firms in different industries 74 Table 2.3: Descriptive statistics and correlation of variables .75 Table 2.4: The volatility impact of Chinese stock market returns on market leverages of Chinese listed firms (2008-2018) .77 Table 2.5: The volatility impact of Chinese stock market returns on debts of banks and financial institutions in of Chinese listed firms (2008-2018) 78 Table 2.6: The volatility impact of Chinese stock market returns on trade credit of Chinese listed firms (2008-2018) 79 Table 2.7: The volatility impact of Chinese stock market returns on market leverages of Chinese listed firms excluding utility firms (2008–2018) 80 Table 2.8: The volatility impact of Chinese stock market returns on market leverages of Chinese listed firms using a sample of Shenzhen Stock Exchange (2008–2018) 81 Table 2.9: The volatility impact of the lag of Chinese stock market returns on market leverages of Chinese listed firms (2008-2018) 82 Table 2.10: Controlling for an endogenous factor (2008–2018) – IV regression 83 Table 2.11: Estimated results using quantile regression (2008-2018) 84 ix FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Tong, G., Green, C J (2005) Pecking order or trade-off hypothesis? Evidence on the capital structure of Chinese companies Applied economics, 37(19), 21792189 Welch, I (2004) Capital structure and stock returns Journal of political economy 112(1): 106-131 Yang, Y., Albaity, M., Hassan, C H B (2015) Dynamic capital structure in China: determinants and adjustment speed Investment Management and Financial Innovations, 12(2), 195-204 Yeh, C C., Lin, P C (2013) Financial structure on growth and volatility Economic Modelling, 35, 391-400 Yiming, H., Lin, T W., Siqi, L., Shilei, X (2008) The Role of the Great Creditors: Do the Banks in China Have the Monitoring Effect on the Borrowers?[J] Economic Research Journal, 10 Zhang, G., Han, J., Pan, Z., Huang, H (2015) Economic policy uncertainty and capital structure choice: Evidence from China Economic Systems, 39(3), 439457 Zou, H., Xiao, J Z (2006) The financing behaviour of listed Chinese firms The British Accounting Review, 38(3), 239-258 71 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Appendix A II LIST OF FIGURES IN STUDY II 0.0800 0.0700 0.0600 0.0500 0.0400 Global financial crisis (2008-2009) China stock market's turbulence (2015-2016) 0.0300 Trade War US China (2018) 0.0200 0.0100 0.0000 2000 2002 2004 2006 2008 LENDING INTEREST RATE 2010 2012 VOL_SSEC 2014 2016 2018 2020 VOL_SZSC Figure 2.1 The volatility of Chinese stock market returns per year and annual China’s lending interest rate (2001-2019) 72 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Appendix B II LIST OF TABLES IN STUDY II Table 2.1: Definition of variables Variables SMKTLEV LMKTLEV TMKTLEV PROFIT FIXASSET SIZE MB LIQ Name Short-term market leverage Long-term market leverage Total market leverage Profitability Fixed assets Firm size Market to book ratio Liquidity BANKDEBT Bank debts S-BANKDEBT Short-term bank debts L-BANKDEBT Long-term bank debts TC Trade credit VOL_SSEC Volatility of SSEC’s returns COSTOFDEBT The debt costs BANKDEBT-DEBT ratio The ratio of bank debts to total debts TC-DEBT ratio The ratio of trade credit to total debts Definition Short-term liabilities divided by Market value plus Total liabilities Long-term liabilities divided by Market value plus Total liabilities Total liabilities divided by Market value plus Total liabilities Earnings before interest and tax divided by Total assets The ratio of Fixed assets to Total assets Logarithm of Total assets Market value plus Total liabilities divided by Total assets The ratio of total current assets to Total asset Total borrowings from banks and financial institutions divided by Total assets Short-term borrowings from banks and financial institutions (less than one year) divided by Total assets Long-term borrowings from banks and financial institutions (more than one year) divided by Total assets The ratio of Accounts payable to Total assets The standard deviation of daily stock returns of the Shanghai Composite Index (SSEC) for the same year The ratio of Interest expenses to Total liabilities Total borrowings from banks and financial institutions divided by Total liabilities Accounts payable divided by Total liabilities 73 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.2: Number of firms in different industries 10 11 12 13 14 15 16 17 Industry Agriculture, Forestry and Fishery Mining Manufacturing Elec, Heat, Gas and Water Construction Wholesale and Retail Transportation, Warehouse and Post Accommodation and Restaurant Industry Information Technology and soft ware Real Estate Leasing and Business Science Research and Technical Services Water, Environment and Utilities Education Health and Social Work Culture, Sports and Entertainment Conglomeration Total firms Number of firms 13 38 412 44 21 78 49 32 68 2 10 12 801 74 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.3: Descriptive statistics and Correlations Panel A: Descriptive statistics of all variables Variable Mean Maximum Minimum Std Dev Obs Q.25 Q.50 Q.75 SMKTLEV 0.2621 0.9335 0.0002 0.1682 8811 0.1282 0.2304 0.3700 LMKTLEV 0.0859 0.6635 0.0000 0.1057 8811 0.0097 0.0453 0.1239 TMKTLEV 0.3480 0.9852 0.0002 0.2141 8811 0.1698 0.3133 0.5051 PROFIT 0.0377 0.7494 -2.8592 0.0850 8811 0.0127 0.0352 0.0668 FIXASSET 0.2546 0.9709 0.0000 0.1944 8811 0.0963 0.2138 0.3826 SIZE 8.7147 14.7045 4.5134 1.4371 8811 7.7230 8.5584 9.5964 MB 2.0635 15.8049 0.6438 1.4213 8811 1.2051 1.6232 2.3911 LIQ 0.5147 0.9989 0.0000 0.2291 8811 0.3407 0.5211 0.6917 VOL_SSEC 0.0149 0.0285 0.0055 0.0064 8811 0.0109 0.0124 0.0191 BANKDEBT 0.1897 0.8935 0.0000 0.1498 8811 0.0622 0.1721 0.2859 S-BANKDEBT 0.1193 0.8492 0.0000 0.1171 8811 0.0219 0.0908 0.1824 L-BANKDEBT 0.0704 0.7751 0.0000 0.1009 8811 0.0000 0.0257 0.1038 COSTOFDEBT BANKDEBTDEBT ratio TC 0.0228 0.3477 0.0000 0.0185 8811 0.0037 0.0104 0.0188 0.3328 0.9854 0.0000 0.2253 8811 0.1469 0.3312 0.4941 0.0935 0.6830 0.0000 0.0735 8811 0.0404 0.0745 0.1276 TC-DEBT ratio 0.1925 1.4289 0.0000 0.1478 8811 0.0841 0.1516 0.2698 Note: Statistics are described in detail by three quantum levels (.25, 50, 75) 75 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Panel B: Correlation of main variables Variables (1) (2) (3) (4) (5) (6) (7) TMKTLEV (1) 1.0000 LMKTLEV (2) 0.6347 1.0000 SMKTLEV (3) 0.8743 0.1798 1.0000 BANKDEBT (4) 0.5083 0.4738 0.3494 1.0000 S-BANKDEBT (5) 0.2708 -0.0890 0.4006 0.7406 L-BANKDEBT (6) 0.4407 0.8071 0.0540 0.6259 -0.0606 1.0000 COSTOFDEBT (7) BANKDEBTDEBT ratio (8) TC (9) 0.1330 0.1964 0.0460 0.4366 0.3884 0.1977 1.0000 0.2712 0.3502 0.1252 0.8741 0.6577 0.5350 0.4979 0.2022 -0.1710 (8) (9) (10) (11) (12) TC-DEBT ratio (10) -0.2288 -0.3658 -0.0615 -0.4674 -0.2942 -0.3528 -0.3784 -0.4978 0.7312 1.0000 PROFIT (11) -0.2086 -0.1157 -0.1929 -0.2446 -0.2412 -0.0834 -0.1605 -0.1458 -0.0675 0.0829 0.0776 0.2264 -0.0435 0.2607 0.1493 0.2140 0.2687 0.2749 -0.0820 -0.0928 -0.0656 1.0000 SIZE (13) 0.6013 0.5202 0.1677 -0.0654 0.3251 0.0072 0.0808 0.0666 VOL_SSEC (16) 0.0784 -0.1170 0.0952 -0.6174 -0.3807 -0.5469 -0.2308 -0.0995 -0.2274 -0.0716 -0.1930 -0.0540 0.0968 0.0567 -0.0960 -0.4824 1.0000 0.2706 0.0664 -0.6850 -0.0838 0.0731 0.0017 -0.3101 0.1969 -0.2360 -0.0707 -0.2686 -0.3256 -0.3162 -0.0539 -0.0770 -0.0202 0.0620 (16) 1.0000 FIXASSET (12) LIQ (15) (15) 1.0000 1.0000 MB (14) (14) 1.0000 0.3649 -0.1447 -0.0332 -0.1764 -0.2547 -0.2987 0.4386 (13) 0.0846 -0.0061 -0.0070 0.2882 0.0849 -0.0190 -0.0252 -0.0338 1.0000 1.0000 0.0544 -0.1369 0.0740 -0.0257 1.0000 76 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.4: The volatility impact of Chinese stock market returns on market leverages of Chinese listed firms (2008-2018) Dependent variables Coefficients ß Constant ß PROFIT ß FIXASSET ß SIZE ß MB ß LIQ ß VOL_SSEC R2 within R2 between R2 overall F-statistic (p-value) Breusch-Pagan LM (p-value) Modified Wald (p-value) Robust Hausman (p-value) Observations TMKTLEV (1) -0.0842 a (0.0460) -0.3224 c (0.0463) 0.1228 c (0.0282) 0.0561 c (0.0042) -0.0522 c (0.0030) 0.0476 a (0.0270) 0.4766 b (0.1860) 0.3939 0.6125 0.5454 0.0000 c 0.0000 c 0.0000 c Market leverages SMKTLEV (2) 0.0092 (0.0404) -0.2078 c (0.0321) 0.1412 c (0.0206) 0.0271 c (0.0038) -0.0438 c (0.0026) 0.1291 c (0.0226) 0.8161 c (0.1708) 0.3090 0.4659 0.4107 0.0000 c 0.0000 c 0.0000 c LMKTLEV (3) -0.0934 c (0.0241) -0.1146 c (0.0217) -0.0185 (0.0195) 0.0290 c (0.0021) -0.0084 c (0.0007) -0.0815 c (0.0144) -0.3394 c (0.1005) 0.1895 0.4453 0.3712 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000c 0.0001c 8811 8811 8811 Collinearity Diagnostics Variable VIF Variable VIF Variable VIF (1) (2) (3) TMKTLEV 2.29 SMKTLEV 1.84 LMKTLEV 1.61 PROFIT 1.15 PROFIT 1.11 PROFIT 1.06 FIXASSET 1.92 FIXASSET 1.93 FIXASSET 1.90 SIZE 1.80 SIZE 1.50 SIZE 1.67 MB 1.69 MB 1.64 MB 1.36 LIQ 1.94 LIQ 2.12 LIQ 2.00 VOL_SSEC 1.03 VOL_SSEC 1.03 VOL_SSEC 1.02 Mean (VIF) 1.69 Mean (VIF) 1.59 Mean (VIF) 1.52 Note: Table 2.4 reports the estimated results from the basic Equation (II) using a panel model with a fixed-effect a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The Modified Wald test calculates for the Groupwise heteroscedasticity in the residuals of the panel model with a fixed-effect The heteroscedasticity-robust standard errors are gathered by firms are reported in parentheses () 77 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.5: The volatility impact of Chinese stock market returns on bank debts of Chinese listed firms (2008-2018) Dependent Variables Coefficients ß Constant ß PROFIT ß FIXASSET ß SIZE ß MB ß LIQ ß VOL_SSEC R2 within R2 between R2 overall F-statistic (p-value) Breusch-Pagan LM (p-value) Modified Wald (p-value) Robust Hausman (p-value) Observations (1) 0.2408 c (0.0485) -0.2084 c (0.0310) 0.1155 c (0.0345) -0.0045 (0.0044) -0.0114 c (0.0016) -0.0527 a (0.0276) 1.1670 c (0.1750) 0.0807 0.1846 0.1431 (2) 0.2481 c (0.0371) -0.1708 c (0.0265) 0.1029 c (0.0231) -0.0162 c (0.0036) -0.0098 c (0.0016) -0.0034 (0.0209) 0.9616 c (0.1509) 0.0931 0.0707 0.0791 (3) -0.0073 (0.0296) -0.0376 c (0.0144) 0.0126 (0.0252) 0.0117 c (0.0025) -0.0016 b (0.0008) -0.0493 c (0.0184) 0.2055 b (0.1040) 0.0242 0.2616 0.1773 (4) 0.0311 c (0.0059) -0.0123 b (0.0048) 0.0160 c (0.0038) -0.0005 (0.0005) -0.0003 (0.0003) -0.0107 c (0.0035) -0.0744 b (0.0310) 0.0300 0.2193 0.1180 BANKDEBTDEBT ratio (5) 0.5266 c (0.0699) -0.1078 c (0.0363) 0.0833 a (0.0478) -0.0163 c (0.0063) -0.0177 c (0.0026) -0.1349 c (0.0391) 2.4726 c (0.2709) 0.0588 0.1762 0.1273 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0002 c 0.0025 c 0.0000 c 0.0000 c 0.0000 c 8811 8811 8811 8811 8811 BANKDEBT S-BANKDEBT Collinearity Diagnostics Variable VIF Variable (1) (2) BANKDEBT PROFIT FIXASSET SIZE MB LIQ VOL_SSEC Mean VIF VIF Variable L-BANKDEBT COSTOFDEBT VIF (3) Variable VIF Variable (5) BANKDEBT 1.21 S-BANKDEBT 1.11 L-BANKDEBT 1.22 COSTOFDEBT 1.15 -DEBT ratio 1.09 PROFIT 1.08 PROFIT 1.04 PROFIT 1.05 PROFIT 1.93 FIXASSET 1.93 FIXASSET 1.90 FIXASSET 1.91 FIXASSET 1.37 SIZE 1.37 SIZE 1.45 SIZE 1.35 SIZE 1.35 MB 1.35 MB 1.33 MB 1.33 MB 1.90 LIQ 1.90 LIQ 1.94 LIQ 1.97 LIQ 1.03 VOL_SSEC 1.03 VOL_SSEC 1.02 VOL_SSEC 1.02 VOL_SSEC 1.41 Mean VIF 1.39 Mean VIF 1.42 Mean VIF 1.40 Mean VIF Note: Table 2.5 reports the estimated results from three minor Equations (II.1), (II.2), (II.3) using a panel model with a fixed-effect a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The heteroscedasticity-robust standard errors are gathered by firms are reported in parentheses () VIF (4) 78 FCU e-Theses & Dissertations (2021) 1.18 1.04 1.91 1.35 1.36 1.96 1.03 1.41 Empirical studies on the volatility of China stock market Table 2.6: The volatility impact of Chinese stock market returns on trade credit of Chinese listed firms (2008-2018) Dependent variables Coefficients ß Constant TC TC –DEBT ratio (1) (2) 0.0098 0.1531 c (0.0215) (0.0416) -0.0312 c 0.0990 c (0.0091) (0.0190) 0.0694 c 0.0417 a (0.0121) (0.0233) b 0.0049 -0.0024 (0.0020) (0.0040) 0.0007 0.0038 c (0.0007) (0.0014) c 0.0503 0.0939 c (0.0124) (0.0213) b -0.1629 -0.6590 c (0.0738) (0.1573) 0.0310 0.0300 0.0666 0.1389 0.0569 0.0994 c 0.0000 0.0000 c c 0.0000 0.0000 c 0.0000 c 0.0000 c c 0.0000 0.0000 c 8811 8811 ß PROFIT ß FIXASSET ß SIZE ß MB ß LIQ ß VOL_SSEC R2 within R2 between R2 overall F-statistic (p-value) Breusch-Pagan LM (p-value) Modified Wald (p-value) Robust Hausman (p-value) Observations Collinearity Diagnostics Variable VIF Variable VIF (1) (2) TC 1.15 TC-DEBT ratio 1.12 PROFIT 1.04 PROFIT 1.04 FIXASSET 1.95 FIXASSET 1.94 SIZE 1.37 SIZE 1.36 MB 1.33 MB 1.33 LIQ 2.12 LIQ 2.05 VOL_SSEC 1.02 VOL_SSEC 1.03 Mean VIF 1.43 Mean VIF 1.41 Note: Table 2.6 reports the estimated results from two minor Equations (II.4), (II.5) using a panel model with a fixed-effect a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The Modified Wald test calculates for the Groupwise heteroscedasticity in the residuals of the panel model with a fixed-effect The heteroscedasticity-robust standard errors are gathered by firms are reported in parentheses () 79 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.7: The volatility impact of Chinese stock market returns on market leverages and debt structure of Chinese listed firms excluding utility firms (2008– 2018) Dependent variables Market leverages TMKTLEV SMKTLEV LMKTLEV Coefficients ß Constant ß PROFIT ß FIXASSET ß SIZE ß MB ß LIQ ß VOL_SSEC R2 within R2 between R2 overall F-statistic (p-value) Breusch-Pagan LM (p-value) Modified Wald (p-value) Robust Hausman (p-value) Observations Collinearity Diagnostics Variable VIF Variable (1) (2) Debt structure BANKDEBT- TC-DEBT DEBT ratio ratio (4) (5) c 0.5224 0.1537 c (0.0701) (0.0417) -0.1081 c 0.0990 c (0.0364) (0.0191) 0.0785 a 0.0427 a (0.0481) (0.0235) -0.0156 b -0.0025 (0.0064) (0.0040) -0.0177 c 0.0039 c (0.0026) (0.0014) -0.1362 c 0.0936 c (0.0393) (0.0215) 2.4794 c -0.6461 c (0.2718) (0.1582) 0.0581 0.0299 0.1781 0.1394 0.1279 0.0996 0.0000 c 0.0000 c (1) -0.0850 a (0.0461) -0.3218 c (0.0463) 0.1223 c (0.0285) 0.0563 c (0.0042) -0.0522 c (0.0031) 0.0487 a (0.0271) 0.4529 b (0.1865) 0.3945 0.6125 0.5455 0.0000 c (2) 0.0097 (0.0405) -0.2074 c (0.0321) 0.1403 c (0.0208) 0.0272 c (0.0038) -0.0439 c (0.0026) 0.1299 c (0.0227) 0.8054 c (0.1718) 0.3090 0.4660 0.4106 0.0000 c (3) -0.0947 c (0.0242) -0.1143 c (0.0218) -0.0180 (0.0197) 0.0291 c (0.0022) -0.0083 c (0.0007) -0.0812 c (0.0144) -0.3526 c (0.1004) 0.1904 0.4449 0.3713 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c 8745 0.0000 c 0.0000 c 8745 0.0000 c 0.0003 c 8745 0.0000 c 0.0000 c 8745 0.0000 c 0.0000 c 8745 VIF Variable VIF Variable VIF Variable (4) (5) BANKDEBTTC-DEBT TMKTLEV 2.29 SMKTLEV 1.83 LMKTLEV 1.61 DEBT ratio 1.18 ratio PROFIT 1.15 PROFIT 1.11 PROFIT 1.06 PROFIT 1.04 PROFIT FIXASSET 1.92 FIXASSET 1.93 FIXASSET 1.90 FIXASSET 1.91 FIXASSET SIZE 1.80 SIZE 1.50 SIZE 1.67 SIZE 1.35 SIZE MB 1.68 MB 1.64 MB 1.36 MB 1.36 MB LIQ 1.95 LIQ 2.12 LIQ 2.00 LIQ 1.96 LIQ VOL_SSEC 1.03 VOL_SSEC 1.03 VOL_SSEC 1.02 VOL_SSEC 1.03 VOL_SSEC Mean VIF 1.69 Mean VIF 1.59 Mean VIF 1.52 Mean VIF 1.41 Mean VIF Note: Table 2.7 reports the estimated results from the basic Equation (II) and minor Equations (II.3), (II.5) using a panel model with a fixed-effect, but, excluding the Chinese utility firms a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The Modified Wald test calculates for the Groupwise heteroscedasticity in the residuals of the panel model with a fixedeffect The heteroscedasticity-robust standard errors are gathered by firms are reported in parentheses () VIF (3) 80 FCU e-Theses & Dissertations (2021) 1.12 1.04 1.94 1.36 1.33 2.06 1.03 1.41 Empirical studies on the volatility of China stock market Table 2.8: The volatility impact of Chinese stock market returns on market leverage and debt structure of Chinese listed firms using a sample of Shenzhen Stock Exchange (2008-2018) Dependent variables Market leverages TMKTLEV SMKTLEV LMKTLEV (1) 0.0263 (0.0351) -0.2253 c (0.0370) 0.0530 b (0.0221) 0.0475 c (0.0036) -0.0436 c (0.0023) -0.1081 c (0.0253) 0.3101 a (0.1719) 0.3575 0.6726 0.5698 0.0000 c 0.0000 c 0.0000 c 0.0000 c 9537 (2) 0.1734 c (0.0334) -0.1487 c (0.0323) 0.0148 (0.0209) 0.0189 c (0.0034) -0.0369 c (0.0019) -0.0576 b (0.0218) 0.4370 b (0.1609) 0.2611 0.4788 0.3934 0.0000 c 0.0000 c 0.0000 c 0.0000 c 9537 (3) -0.1471 c (0.0259) -0.0766 c (0.0136) 0.0381 a (0.0210) 0.0286 c (0.0026) -0.0067 c (0.0006) -0.0505 c (0.0111) -0.1269 a (0.0702) 0.1655 0.4390 0.3558 0.0000 c 0.0000 c 0.0000 c 0.0000 c 9537 Coefficients ß Constant ß PROFIT ß FIXASSET ß SIZE ß MB ß LIQ ß VOL_SZSC R2 within R2 between R2 overall F-statistic (p-value) Breusch-Pagan LM (p-value) Modified Wald (p-value) Robust Hausman (p-value) Observations Collinearity Diagnostics Variable VIF Variable VIF (1) (2) Variable VIF BANKDEBTDEBT ratio (4) 0.4366 c (0.0537) -0.0678 b (0.0291) 0.1666 c (0.0390) -0.0141 c (0.0055) -0.0208 c (0.0022) -0.0768 b (0.0371) 2.5272 c (0.2748) 0.0613 0.2772 0.1740 0.0000 c 0.0000 c 0.0000 c 0.0000 c 9537 Debt structure TC-DEBT ratio (5) 0.2924 c (0.0317) 0.0848 c (0.0231) -0.0050 (0.0215) -0.0081 b (0.0033) 0.0034 b (0.0016) -0.0489 b (0.0227) -1.0946 c (0.1662) 0.0153 0.0229 0.0195 0.0000 c 0.0000 c 0.0000 c 0.0000 c 9537 Variable VIF Variable VIF (4) (5) BANKDEBT TC-DEBT TMKTLEV 2.38 SMKTLEV 1.73 LMKTLEV 1.59 -DEBT ratio 1.27 ratio 1.07 PROF 1.14 PROF 1.11 PROF 1.07 PROF 1.07 PROF 1.06 TANG 1.13 TANG 1.15 TANG 1.17 TANG 1.19 TANG 1.13 SIZE 1.81 SIZE 1.48 SIZE 1.66 SIZE 1.34 SIZE 1.35 MB 1.69 MB 1.65 MB 1.36 MB 1.39 MB 1.34 LIQ 1.21 LIQ 1.19 LIQ 1.21 LIQ 1.23 LIQ 1.20 VOL_SZSC 1.03 VOL_SZSC 1.03 VOL_SZSC 1.03 VOL_SZSC 1.04 VOL_SZSC 1.03 Mean VIF 1.48 Mean VIF 1.33 Mean VIF 1.30 Mean VIF 1.22 Mean VIF 1.17 Note: Table 2.8 reports the estimated results from the basic Equation (II) and two minor Equations (II.3), (II.5) using a panel model of listed firms on the Shenzhen Stock Exchange (2208-2018) with a fixed-effect a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The Modified Wald test calculates for the Groupwise heteroscedasticity in the residuals of the panel model with a fixed-effect The heteroscedasticity-robust standard errors are gathered by firms are reported in parentheses () VOL_SZSC is the volatility of the Shenzhen Composite Index (SZSC)’s returns in the period 2008-2018 (3) 81 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.9: The volatility impact of the lag of Chinese stock market returns on market leverages and debt structure of Chinese listed firms (2008-2018) Dependent variable Market leverages TMKTLEV SMKTLEV LMKTLEV Coefficients ß Constant ß LAG.PROFIT ß LAG.FIXASSET ß LAG.SIZE ß LAG.MB ß LAG.LIQ ß LAG.VOL_SSEC (1) -0.0219 (0.0413) -0.2768 c (0.0355) 0.0253 (0.0257) 0.0529 c (0.0041) -0.0210 c (0.0017) 0.0342 (0.0243) -4.0762 c (0.1900) 0.2602 0.6207 0.4998 0.0000 c (2) 0.0148 (0.0343) -0.1824 c (0.0292) 0.0923 c (0.0201) 0.0306 c (0.0034) -0.0167 c (0.0014) 0.0882 c (0.0195) -3.3383 c (0.1601) 0.1992 0.4674 0.3665 0.0000 c (3) -0.0367 (0.0228) -0.0944 c (0.0197) -0.0669 c (0.0183) 0.0223 c (0.0021) -0.0043 c (0.0006) -0.0540 c (0.0129) -0.7379 c (0.0974) 0.1169 0.3761 0.2970 0.0000 c Debt structure BANKDEBT- TC-DEBT DEBT ratio ratio (4) (5) c 0.5613 0.1818 c (0.0649) (0.0383) -0.0137 0.1187 c (0.0409) (0.0226) 0.0256 -0.0099 (0.0461) (0.0224) c -0.0214 -0.0019 (0.0059) (0.0035) c -0.0208 0.0040 b (0.0026) (0.0016) b -0.0803 0.0522 b (0.0383) (0.0210) c 1.9088 -0.5832 c (0.2585) (0.1409) 0.0464 0.0257 0.1013 0.0909 0.0756 0.0685 c 0.0000 0.0000 c R2 within R2 between R2 overall F-statistic (p-value) Breusch-Pagan LM 0.0056 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c (p-value) Modified Wald (p-value) 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c Robust Hausman 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c (p-value) Observations 8010 8010 8010 8010 8010 Note: Table 2.9 reports the estimated results from the basic Equation (II) and two minor Equations (II.3), (II.5) with all independent variables at lag; a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The Modified Wald test calculates for the Groupwise heteroscedasticity in the residuals of the panel model with a fixed-effect The heteroscedasticity-robust standard errors clustered by firms are reported in parentheses () 82 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.10: Controlling for an endogenous factor (2008–2018) – IV regression Dependent variable Coefficients Tool ß PROFIT ß FIXASSET ß SIZE ß MB ß LIQ ß VOL_SSEC Market leverages Debt structure TMKTLEV SMKTLEV LMKTLEV (1) 1.1319 c (0.0062) -0.3140 c (0.0441) 0.1103 c (0.0281) 0.0675 c (0.0043) -0.0523 c (0.0031) 0.0498 a (0.0268) 3.7302 c (0.2940) (2) 1.1319 c (0.0062) -0.2009 c (0.0305) 0.1311 c (0.0206) 0.0365 c (0.0038) -0.0440 c (0.0026) 0.1309 c (0.0226) 3.4797 c (0.2773) (3) 1.1319 c (0.0062) -0.1131 c (0.0214) -0.0207 (0.0195) 0.0311 c (0.0023) -0.0084 c (0.0008) -0.0811 c (0.0143) 0.2505 (0.1696) BANKDEBTDEBT ratio (4) 1.1319 c (0.0062) -0.1001 c (0.0349) 0.0719 (0.0470) -0.0058 (0.0065) -0.0178 c (0.0025) -0.1329 c (0.0387) 5.4601 c (0.4764) TC-DEBT ratio (5) 1.1319 c (0.0062) 0.0970 c (0.0187) 0.0448 a (0.0232) -0.0052 (0.0042) 0.0039 c (0.0014) 0.0934 c (0.0213) -1.4490 c (0.2869) Stock-Wright LM 0.0000 c 0.0000 c 0.1400 0.0000 c 0.0000 c (p-value) Anderson-Rubin 0.0000 c 0.0000 c 0.1397 0.0000 c 0.0000 c Wald (p-value) F-statistic 0.0000 c 0.0000 c 0.0000 c 0.0000 c 0.0000 c (p-value) Observations 8811 8811 8811 8811 8811 Note: Table 2.10 shows the estimations from the basic Equation (II) and two minor Equations (II.3), (II.5) using the two stage least squares with a fixed-effect We use “Tool” as an IV to estimate in the first stage In the second stage, we analyze the impact of instrumented VOL_SSEC variable by the VOL_S&P500 variable on three market leverages (Column 1, Column and Column 3) and on debt structure (Column 4, Column 5) VOL_S&P500 is the volatility of Standard & Poor’s 500 (S&P500) Index Daily closed-priced price of S&P500 is download from Thomson Reuters Eikon in the period 2008-2018 a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The heteroscedasticity-robust standard errors gathered by firms are reported in parentheses () The Stock-Wright LM test and the Anderson-Rubin Wald test are used to examine the weakness of instruments 83 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Table 2.11: Estimated results using the unconditional quantile regression (QR) with a fixed-effect (2008-2018) Panel A: The volatility impact of Chinese stock market returns on market leverages of Chinese listed firms Market leverages TMKTLEV (1) SMKTLEV (2) LMKTLEV (3) Quantile 25 50 75 25 50 75 25 50 75 c c c c c c c c ß Constant 0.0661 -0.2936 -0.5877 0.0420 -0.1099 -0.2893 -0.0225 -0.0811 -0.2205 c (0.0186) (0.0271) (0.0340) (0.0141) (0.0198) (0.0280) (0.0038) (0.0086) (0.0187) c c c c c c ß PROFIT -0.4273 -0.7699 -0.8359 -0.2939 -0.5025 -0.5930 -0.0454 c -0.1376 c -0.2772 c (0.0227) (0.0329) (0.0413) (0.0172) (0.0240) (0.0340) (0.0046) (0.0105) (0.0228) c c c c c c ß FIXASSET 0.0905 0.1198 0.1641 0.1103 0.1084 0.1329 -0.0150 c -0.0200 c -0.0068 (0.0134) (0.0195) (0.0245) (0.0102) (0.0143) (0.0202) (0.0028) (0.0062) (0.0135) ß SIZE 0.0257 c 0.0785 c 0.1172 c 0.0113 c 0.0346 c 0.0596 c 0.0085 c 0.0255 c 0.0556 c (0.0015) (0.0022) (0.0028) (0.0012) (0.0016) (0.0023) (0.0003) (0.0007) (0.0015) c c c c c c ß MB -0.0843 -0.0846 -0.0458 -0.0587 -0.0616 -0.0482 -0.0071 c -0.0144 c -0.0182 c (0.0015) (0.0022) (0.0028) (0.0012) (0.0016) (0.0023) (0.0003) (0.0007) (0.0015) ß LIQ 0.0650 c 0.1398 c 0.2581 c 0.1560 c 0.2580 c 0.4069 c -0.0346 c -0.0982 c -0.1675 c (0.0114) (0.0166) (0.0208) (0.0086) (0.0121) (0.0171) (0.0023) (0.0053) (0.0114) ß VOL_SSEC 0.9333 c 1.6025 c 1.5060 c 0.8087 c 1.6275 c 1.2137 c -0.2275 c -0.3264 b -0.2495 (0.3003) (0.4366) (0.5478) (0.2280) (0.3185) (0.4513) (0.0615) (0.1386) (0.3017) R-square 0.4069 0.3957 0.3066 0.3482 0.3264 0.2476 0.2515 0.3210 0.2644 Observations 8811 8811 8811 8811 8811 8811 8811 8811 8811 chi2(6) = 781.75 chi2(6) = 460.38 chi2(6) = 2004.85 Q.25 vs Q.50 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 chi2(6) = 284.04 chi2(6) = 251.78 chi2(6) = 875.86 Q.50 vs Q.75 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 chi2(6) = 940.90 chi2(6) = 702.48 chi2(6) = 2087.77 Q.25 vs Q.75 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Note: Panel A of Table 2.11 shows the estimations from the basic Equation (II) using the quantile regression (QR) with a fixed-effect at three quantile levels (0.25, 0.50, 0.75) a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The standard errors are reported in parentheses () Dependent variable 84 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market Panel B: The volatility impact of Chinese stock market returns on the debt structure of Chinese listed firms Dependent variable Quantile ß Constant Debt structure BANKDEBT-DEBT ratio TC-DEBT ratio (1) (2) 25 50 75 25 50 c c c 75 c 0.1447 0.5313 0.6943 0.0101 0.0716 0.1062 c (0.0380) (0.0325) (0.0328) (0.0117 (0.0167) (0.0279) c c c c ß PROFIT -0.5991 -0.3605 -0.1984 0.0660 0.1404 c 0.2118 c (0.0462) (0.0395) (0.0398) (0.0142) (0.0203) (0.0339) c c c c ß FIXASSET 0.0909 0.0940 0.1723 0.1527 0.1607 c 0.1418 c (0.0274) (0.0234) (0.0236) (0.0084) (0.0120) (0.0201) c c c ß SIZE 0.0195 -0.0042 -0.0118 -0.0044 -0.0088 c -0.0094 c (0.0031) (0.0027) (0.0027) (0.0010) (0.0014) (0.0023) c c c ß MB -0.0455 -0.0338 -0.0199 -0.0011 0.0015 0.0096 c (0.0031) (0.0027) (0.0027) (0.0010) (0.0014) (0.0023) c c c c ß LIQ -0.2353 -0.2917 -0.2816 0.1555 0.2367 c 0.3846 c (0.0232) (0.0199) (0.0200) (0.0071) (0.0102) (0.0171) c c c c ß VOL_SSEC 3.1071 3.0725 3.5019 -0.4779 -0.9608 c -1.0987 b (0.6126) (0.5234) (0.5279) (0.1878) (0.2685) (0.4495) R-square 0.1061 0.1025 0.0956 0.0590 0.0739 0.0835 Observations 8811 8811 8811 8811 8811 8811 Q.25 vs Q.50 chi2(6) = 191.08 chi2(6) = 228.46 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Q.50 vs Q.75 chi2(6) = 100.76 chi2(6) = 284.47 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Q.25 vs Q.75 chi2(6) = 325.20 chi2(6) = 503.29 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Note: Panel B of Table 2.11 reports the estimations from two minor Equations (II.3), (II.5) using the quantile regression (QR) with a fixed-effect at three quantile levels (0.25, 0.50, 0.75) a, b, c characteristics indicate that the coefficient is significantly different from zero corresponding to 10%, 5%, and 1% levels; The standard errors are reported in parentheses () 85 FCU e-Theses & Dissertations (2021) ... developed markets to the emerging markets The volatility of the US stock market has no continuous impacts on the volatility of China stock market The volatility effects from the US to China stock market. .. use another measure of the volatility of the Chinese stock market returns 22 FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market 1.5 Conclusion and Recommendation... FCU e-Theses & Dissertations (2021) Empirical studies on the volatility of China stock market the volatility of China stock market has significant spillovers to the US stock market during the currency