Empirical studies on the volatility of china stock market

Modeling the volatility of S&P500’s returns by using structural breakpoints in the variance of Chinese stock market returns .... Modeling the return volatility of other US indexes by usi

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

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

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 Che Wu, Professor Tzu-Ching Weng, Professor Tsang-Yao Chang, Professor Yu-Chih Lin and Professor Meng-Fen Hsieh for their time, attention, and insightful suggestions for completing this thesis

Yang-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

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

iv

CONTENTS

ACKNOWLEDGEMENTS i

ABSTRACT iii

CONTENTS iv

LIST OF FIGURES vii

LIST OF TABLES viii

STUDY I: 1

THE BIDIRECTIONAL VOLATILITY SPILLOVERS BETWEEN THE US AND CHINA STOCK MARKETS 1

1.1 Introduction 1

1.1.1 Research background 1

1.1.2 Research motivations and Research contributions 4

1.1.3 Research structure 6

1.2 Literature review 6

1.3 Sample, Methodology and Empirical models 9

1.3.1 Sample 9

1.3.2 ICSS algorithm to detect structural breakpoints in the variance of volatility source’s returns 9

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

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

vi

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

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

viii

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

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

STUDY I:

THE BIDIRECTIONAL VOLATILITY SPILLOVERS BETWEEN THE

US AND CHINA STOCK MARKETS 1.1 Introduction

1.1.1 Research background

Globalization results in increasing interdependence on capital markets (Baele, 2005) A large of studies indicate that the rise in financial integration leads to the stock return transmission and volatility spillover between stock markets in the

world (Baele, 2005; Sui and Sun, 2016; Lien et al., 2018; Vo and Tran, 2020) At

present, emerging markets appear more and get more attention in the integrated context of stock markets (Moon and Yu, 2010) Volatility spillovers between stock markets are usually related to the variance of stock returns and investment risks in equity markets (Vo and Tran, 2020) In addition, volatility spillovers indicate the level of integration between stock markets (Mukherjee and Mishra, 2010) Therefore, the important issues are to find out the source of volatility, the moment

of volatility, and the degree of volatility spillovers in the international equity market

The volatility transmission from advanced markets to emerging markets seems like a natural issue shown by a large number of empirical results (Ng, 2000;

Worthington and Higgs, 2004; Chow, 2017; Vo and Tran, 2020) implying that

emerging markets are excitable by the small fluctuations from developed markets, however, the contagion from developed markets to emerging markets varies across different markets (Worthington and Higgs, 2004) Developing equity markets are less likely to be affected by shocks from their developed counterparts if the linkages between emerging markets and developed markets are weak Inversely,

if emerging equity markets are absolutely dependent on advanced equity markets, the volatility of emerging markets is also pointed out in the developed markets These recent studies address the impact of the global financial crisis in 2008 on

the relationships between international equity markets (Lien et al., 2018; Hung,

2019; Vo and Tran, 2020), overall, their empirical results show that financial shocks expose substantially the contagion between stock markets

On the other hand, Li and Giles (2015) show the volatility spillovers from emerging markets in Asia area to developed markets during the currency crisis (1997), their findings imply that the volatility effects are also likely to exist bidirectionally between equity markets but do not only appear from advanced markets to emerging markets Most recently, the Covid-19 pandemic sourced from China market at the end of 2019 impacts terribly and rapidly on the entire

international market (Zhang et al., 2020), it occurs the worst in the US market

The major stock indexes in the world simultaneously bottomed out at the end of Q1-2020

Despite the great changes in the global text, the positions of “big players” in the international stock market have not yet varied The US stock market accounts for nearly 44.33% total market value of the international stock1 While the closest competitor, China, is only still 1/5th of the market value of the US, however, China

is the largest emerging stock market of both the Asian stock market and the global stock market According to the STATISTA2, the first and the second-largest stock exchanges in the world come from the US stock market, listed by the market capitalization of listed firms in 20203 The fourth position belongs to the Shanghai Stock Exchange of China stock market China’s economy is distant from other developing countries and China’s GDP has been continuously growing up from

1990 to the current year Following the Purchasing Power Parity (PPP) Index, China has been become the second-largest economy in 2004, but, by 2010 according to the GDP Index Also, the China stock market is a promised emerging market with potential linkages to the international markets With its developed speed, China is expected to get over the US equity market and turn into the largest

stock market in 2030 (Liu et al., 2013) The US is a large exporting market of

China, also, China is the second-biggest foreign creditor of the US country (Morrison, 2010) On the other hand, the bilateral nexus between the US and China are quite complex and tense during the Korean War, the Vietnamese War,

1 Data is published by World Bank, calculated at the end of 2018

2 STATISTA is German enterprise operating in providing market database

The figures are updated until 31 th March, 2020

Taiwanese, and Hong Kong issues The most noticeable is the trade war between the US and China in 2018 The mutually commercial allegations between the US and China are on the intensively risen The US has suffered criticisms from its Chinese partner due to its importing restrictions on the high-tech products of China Inversely, the US side expresses views related the intellectual property rights, commercial surplus Moreover, both the US and China severely competitive to increase their influence on the Asian markets The government intervention likelihoods to impact the open market and the integration between equity markets (Uludag and Khurshid, 2019), hence the integrated degree between the US and China stock market is more likely to be significantly dominated by the government policies from two sides Additionally, due to the restrictions on China’s foreign investment, the volatility of Chinese stock market returns is less likely to be significantly impacted by the market volatility of counterparts (Zhou et al., 2012).

In the first study, we provide comprehensive evidence on the bidirectional volatility spillovers between the US and China stock markets over nearly 20 years

by applying a new methodology on large samples We mainly survey the bidirectional volatility spillovers between the Standard & Poor’s 500 (S&P500) Index of the US stock market and the Shanghai Composite (SSEC) Index of the China stock market in long term Moreover, we use two other US stock indexes (Nasdaq Composite Index, Down Jones Industrial Average Index) and another Chinese stock index (Shenzhen Composite Index) to reinforce our empirical results Our research contents are shortly illustrated in Figure.1 Data is collected from Thomson Reuters Eikon in the period January 2001-October 2020 The research period consists of the global financial crisis in 2008 derived from the US market and the Covid-19 pandemic originated from China country Firstly, we employ the ICSS algorithm to detect structural breakpoints of the volatility source (Inclan and Tiao, 1994) Secondly, we use a variant form of the EGARCH (1,1) model with the detected breakpoints to model the volatility spillovers from the US

to China stock market (Vo and Tran, 2020), and vice versa in the period January 2001- December 2020 In brief, empirical results show that China stock market is the largest developing market in the Asia-Pacific area, however, its volatility was

not frequently affected by the volatility of the US stock market returns On otherwise, we find out the substantial volatility spillovers from the US to China stock market appear during the global financial crisis in 2008 and perform obviously on the day of Leman Brother group’s collapse In the opposite direction, the volatility of China stock market has impacted persistently but weakly on the volatility of US stock markets for the period from 2004 to 2020 We detected that the Covid-19 pandemic that originated from China considerably promoted the volatility spillovers from China to the US stock market at the end of March 2020 Our findings are robust to a large volume of stock indexes in both the US and China markets In addition, we provide certainty evidence by another volatility measure of S&P500’s returns, as well as, SSEC’s returns

[Insert Figure 1.1 here]

1.1.2 Research motivations and Research contributions

This research is motivated by a large number of the following reasons Surveying the volatility in emerging markets becomes more and more important

in the financial field because these markets are young and likely to be more highly sensitive to fluctuations from developed markets Emerging markets have been asserted their position in the international market, an outstanding example is the China market The volatility of developing markets has a high ability to influence advanced equity markets Secondly, the US and China stock markets are the two largest stock markets in the world, they respectively represent a developed market and an emerging market Therefore, it’s quite essential to examine in detail the bidirectional volatility spillovers from the US to China stock market in the long-term based on the barriers in bilateral nexus between the US and China, as well as, foreign investment’s restrictions in China’s financial market in the recent two decades Thirdly, the international equity market witnesses two great crashes in the period post-2000, the first shock is the global financial crisis in 2008 sourced from the US and the second one is the Covid-19 pandemic originated from China

at the end of March 2020, both of them might significantly promote the volatility spillovers between the US-China equity markets Fourth, the majority of previous research related to the volatility effects between the US and China stock markets

turn around the currency crisis (1997) and the global financial crisis (2008) in the period 1994-2015 while the recent impact of the Covid-19 has not yet mentioned Therefore, our study uses a new methodology compared with previous studies on similar topics to solve a list of the research hypotheses regarding the bidirectional volatility spillovers between the US and China stock markets in the post-2000 period, as follows:

- Research question 1: Whether the volatility spillovers from the US to China

stock market in the period January 2001-October 2020? and vice versa?

- Research question 2: Which degrees of volatility spillovers from the US to

China stock market and vice versa are at different moments?

- Research question 3: Does the global financial crisis in 2008 promote the

spillover effect from the US to China stock market to become more powerful? and vice versa?

- Research question 4: Whether the recent Covid-19 pandemic substantially

motivate the volatility spillovers from China to the US stock market? and vice versa?

More specifically, we implement the ICSS algorithm (Inclan and Tiao, 1994) to detect excessive volatility breakpoints of daily stock market returns covering January 2001 to October 2020 Then, we estimate a variant form of EGARCH (1,1) with breakpoint dummy variables (Vo and Tran, 2020) to survey the volatility spillovers from the US to China stock market and vice versa Our research overcomes weak points of the previous studies and robustly responds to the entire our research hypotheses as follows: Investigating a large sample with different stock indexes in both the US and China stock markets; Expanding the research period that covers the global financial crisis in 2008 which is originated from the

US market and the Covid-19 pandemic derived from China country; Clearly indicating the source of volatility, the broken volatility moments as well as their degrees of volatility spillovers from the US to China stock market and vice versa; Emphasizing the dominant role of the huge shocks in the volatility spillovers between equity markets; Confirming of the integrated level between the US and China stock markets for nearly two decades

1.1.3 Research structure

The introduction is presented in the first part The remainder of this study includes Part 2 summarizes the literature review of volatility spillovers Sample, Methodology, and Empirical models are introduced in Part 3 In the fourth part,

we analyze the main results on the bidirectional volatility spillovers between the

US and China stock markets using the S&P500 Index of the US stock market and the SSEC Index of China stock market, as well as, the empirical results of robust

tests The final part gives conclusions and recommendations

1.2 Literature review

The volatility spillovers and interdependence between equity markets in the world are undeniable Besides the existence of volatility spillovers from developed markets to developing markets, the volatility contagion is largely found among the

affiliate stock markets such as European stock markets (Kanas, 1998), East Asia

stock markets (Yilmaz, 2010), Asian stock markets (Joshi, 2011), North American,

European and Asian stock markets (Singh et al., 2010) or even volatility

transmission from East Asia stock markets to Southeast Asia stock markets (Wu, 2020) suggesting that the global integration sets the stage for volatility spillovers Especially, the spillover effects originating from the US stock market have been popularly demonstrated in European stock markets (Baele, 2002), Islamic stock markets (Majdoub and Mansour, 2014), BRICS stock markets (Sui and Sun, 2016;

Mensi et al., 2016), ASEAN stock markets (Vo and Tran, 2020) A lot of evidence

state that the volatility of US stock market is related strongly to the volatility of Asian stock markets For instance, Liu and Pan (1997) show the significant volatility linkages between the US stock market and Asian stock markets such as Hong Kong, Singapore, Taiwan, and Thailand Li and Giles (2015) indicate the volatility spillovers from the US to Asian emerging stock markets (including China) are considerably unidirectional during the Asian crisis in 1997

Additionally, Lien et al (2018) find out the strong volatility spillovers from the

US stock market to Southeast Asian stock markets and East Asian stock market

(excluding China) during two crises in 1997 and 2008

Meanwhile, there is little direct evidence regarding the volatility spillovers

between the US and China stock markets The first study investigates the linkage among the US and China stock markets from January 2000 to August 2005 and insists that there is no direct spillover effect from the US to China stock market by using a multivariate GARCH model (Li, 2007) Then, Moon and Yu (2010) extent their research period from January 1999 to June 2007, in order to survey the volatility spillovers from the US to China stock market Different from Li's (2007) study, they use Andrews (1993)’s method to find out a single structural breakpoint

in the mean of Chinese stock market returns Then, they employ GARCH-m (1,1) model and prove that the volatility spillover from the US stock market to China stock market only exists in the post-breakpoint period (December 2, 2005) but it doesn’t appear in the pre-breakpoint period In addition, the volatility spillovers from the US to China stock markets are not also clearly indicated in the period 1994-2004 (Wang and Wang, 2010) The later studies mostly emphasize the role

of the global financial crisis Zhou et al (2012); Uludag and Khurshid (2019); Mensi et al (2016) largely focus on the dominated volatility spillovers from the

US to China stock market and other equity markets in two years (2007 and 2008)

On the other hand, since 2004, China’s economy has been the second-largest economy (after the US) defined by the PPP index that event proves the efficiency

of Chinese innovative policy during three decades Although China is only an emerging market, its equity market has also substantial impacts on other equity

markets Employing the VAR model, Zhou et al (2012) show that since 2005, the

volatility transmissions from the China stock market to Hong Kong and Taiwan stock markets have been more remarkable than to European and other Asian equity markets Hung (2019) proves the energetic volatility effects from China stock market to the Southeast Asian stock market in the period July 2000 – July 2018

using the GARCH-BEKK model Allen et al (2013) use a variety of time series

models and find out some evidence on the volatility spillovers from China stock market to advanced stock markets in the period pre-global financial crisis On the other hand, they indicate that the volatility of the US stock market returns strongly impacts forward on China stock market during the global financial crisis in 2008 Investigating a sample in the period 1993 to 2012, Li and Giles (2015) show that

the volatility of China stock market has significant spillovers to the US stock market during the currency crisis in 1997 Additionally, Uludag and Khurshid (2019) prove that the volatility spillovers bi-directionally appear between China stock market and G7 equity markets, as well as, E7 stock markets from 1995 to

2015 by the VAR (1) – GARCH (1) model However, in their study, the bidirectional volatility effects between the US and China stock markets are not clearly shown while empirical results largely focus on the volatility spillovers

between the US and other equity markets

In odds with the classical theory on the volatility transmission from the advanced markets to emerging counterparts, the recent results suggest that the volatility of China stock market not only influences the volatility of emerging partners but also developed counterparts However, there haven’t had any studies that directly focus on the bidirectional volatility spillovers between the US and China equity markets during the near two decades Most importantly, the role of the Covid-19 pandemic in the volatility spillovers from China stock market to other equity markets is not still mentioned due to the objective reason while the effects of the global financial crisis get more attention In other respects, the previous studies regarding the volatility spillovers between the US and China stock market exist several limits For instance, the volatility source originates from the US stock market but most of the literature didn’t clearly indicate that these volatilities sourced from the US stock market, and which their spillover degrees

to China stock market across different moments Inversely, a single structural breakpoint in the mean of Chinese stock market returns is found and prove that this structural breakpoint is related to the volatility of the US stock market returns (Moon and Yu, 2010) Secondly, Andrews's (1993) method only helps to detect a single structural breakpoint based on the average stock returns While multiple structural breakpoints in the variance of the volatility source are more likely to be detected by the ISCC algorithm (Inclan and Tiao, 1994), they are closely related

to the market risks Hence, if the time series is observed in the long term, the degree of volatility spillovers at different moments and the continuity of volatility spillovers between equity markets are likely to be more exactly considered

Thirdly, the US stock market is usually surveyed by a representative stock index (S&P500 Index) and omitted the rest stock indexes (Down Jones Industrial Average, Nasdaq Composite) while the Shenzhen Composite Index is also an important indicator of China stock market

1.3 Sample, Methodology and Empirical models

1.3.1 Sample

The purpose of the first study is to examine the bidirectional volatility spillovers between the US and China stock markets from January 2001 to October 2020 Our research period includes the global financial crisis in 2008 that originated from the

US market and the Covid-19 pandemic derived from the China stock market We use three US stock indexes including the Standard & Poor’s 500 Index (hereafter S&P500), Nasdaq Composite Index, and Down Jones Industrial Average Index (hereafter DJIA), among them, the S&P500 Index is the best representative index

of the US stock market Regarding the China stock market, both the Shanghai Composite Index and Shenzhen Composite Index are employed, therein, the Shanghai Composite Index as a major proxy for China stock market Daily stock prices are download from Thomson Reuters Eikon in the period January 2001-October 2020

1.3.2 ICSS algorithm to detect the structural breakpoints in the variance of volatility source’s returns

According to the first dimension, we examine the volatility spillovers from the

US to China stock market implying that the US stock market is the volatility source In this direction, the US stock market is defined to be the volatility source

We employ the iterative cumulative sum of squares (ICSS) algorithm so as to determine the structural breakpoints in the variance of the US stock market returns based on IT statistics (Inclan and Tiao, 1994) Then, we test their effects on the volatility of Chinese stock market returns by a variant form of the EGARCH (1,1) model (Vo and Tran, 2020)

The ICSS algorithm gives an alternative hypothesis that unconditional variance

is stationary over the period which is separated by the volatility breakpoints We summary briefly of steps in the ICSS algorithm to detect structural breakpoints, as

where j = 1,2, …, N; CRSSj is the cumulative residual sum of squares from the starting of the time series to the jth date, presented in Formula (I.2) Assuming that

j equals to N, CN is the residual sum of squares of the whole observations Dj is used for testing statistics at jth break date is defined by Formula (I.3):

If the absolute value of Dj exceeds the critical values meaning that we reject the Null hypothesis (H0) meaning that volatility breakpoint exists We repeat this process over sub-samples to maybe discover multiple breakpoints in a long time series That is an advantage of the ICSS algorithm compared with the Andrews's (1993) method, however, the ICSS algorithm is used only in unconditional variance (Smith, 2008) In our research, MATLAB software is used to implement the ICSS algorithm to recognize the volatility breakpoints in the volatility of the

US stock market returns For the second dimension, we also carry out the ICSS algorithm to detect the structural breakpoints in the variance of Chinese stock market returns

After that, we survey the volatility effects from the US to China equity market through a variant form of EGARCH (1,1) model with the US volatility breakpoints

in the volatility equation of US stock market returns In the opposite direction, we also control the volatility breakpoints of Chinese stock market returns in the volatility equation of US stock market returns by the variant model of EGARCH (1,1) The details of experimental models are introduced in Section 1.3.3

1.3.3 Modeling the bidirectional volatility spillovers between the US and China stock markets

Nelson (1991) succeeded in building the EGARCH (Exponential Generalized ARCH) model to measure and forecast the volatility of stock returns The original EGARCH (1,1) model has a following form:

log(ℎ𝑡) = 𝜔 + 𝛼 |𝑢𝑡−1

√ℎ 𝑡−1| + 𝛽 𝑢𝑡−𝑖

√ℎ 𝑡−𝑖+ 𝜃log(ℎ𝑡−1) Eq (I)

where: ℎ𝑡 is the variance of stock returns, 𝜔 is constant, 𝛼 expresses the ARCH effect, 𝜃 shows the GRACH effect, 𝛽presents the asymmetric effect In the case, ß<0 that means the bad news (negative shocks) generates larger volatility than good news (positive shocks)

Reyes (2001) uses a bivariate AR (1)-EGRCH (1,1) model to detect the volatility transmission in the Tokyo stock market between different size stock indexes Krause and Tse (2013) also employ a bivariate EGARCH (1,1) model to find out the spillover effect from the US stock market to the Canadian stock market The AR (1) model is adjusted for the return series Hence, we use the combination

AR (1) model and a variant form of EGARCH (1,1) model so as to investigate whether the volatility of Chinese stock market returns is affected by the volatility breakpoints of the US stock market returns, and vice versa The experimental models are respectively built in Equation (I.1) and Equation (I.2), as follows:

r_CNt = α0 + α1r_CNt−1+ ϵt ~N(0, v_CNt) Eq (I.1)

log (v_CNt) = β0+ β1| ϵt−1

√v_CNt−1| + γjUSbreakpointj+ β2log (v_CNt−1) r_USt= а0+ а1r_USt−1+ ϵt ~N(0, v_USt) Eq (I.2)

log (v_USt) = ϐ0+ ϐ1| ϵt−1

√v_USt−1| + πjCNbreakpointj+ log (ϐ2v_USt−1)

where: r_CNt and r_USt are the stock returns at (t) day, respectively, in China stock market and the US stock market; v_CNt and v_USt are the variance of stock returns at (t) day, respectively, in China stock market and the US stock market; USbreakpointj is a volatility breakpoint dummy variable (j) at a structural breakpoint (jth) in the variance of US stock market returns CNbreakpointj is a volatility breakpoint dummy variable (j) at a structural breakpoint (jth) in the variance of Chinese stock market returns A volatility breakpoint dummy variable (j) equals 1 the beginning from a breakpoint (j − 1)th to breakpoint (jth) and equals 0 in other where The coefficients (γj) and (πj) respectively indicate the impacts of volatility breakpoint (jth) of the US stock market on China stock market, and vice versa The ICSS algorithm and two empirical Equations (I.1), (I.2) are applied for three US stock market indexes (S&P500, DJIA, Nasdaq Composite) and two China stock market indexes (Shanghai Composite, Shenzhen Composite)

By another volatility measure of the stock market returns, we mainly focus on the S&P500 Index of the US stock market and the SSEC Index of China stock market In Equation (I.3) and Equation (I.4), the variance of the US stock market returns (VOL_S&P500) and the variance of Chinese stock market returns (VOL_SSEC) are included in the variance equation of the EGARCH (1,1) model

to replace respectively for breakpoint dummy variables in Equation (I.1) and in Equation (I.2), as follows:

r_SSECt = α0+ α1r_SSECt−1+ ϵt ~N(0, v_SSECt) Eq (I.3)

log (v_SSECt) = β0+ β1| ϵt−1

√v_SSECt−1| + ∅VOL_S&P500 + β2log (v_SSECt−1)

where: r_SSECt is the stock return of SSEC Index at (t) day; v_SSECt is the variance of SSEC’s returns at (t) day; VOL_S&P500 is the variance of the S&P500’s returns and the ∅ coefficient represents the impact of the variance of

US stock market returns on the volatility of Chinese stock market returns

To enhance the robustness of empirical results, we use sub-samples corresponding with the different sub-periods regarding the global financial crisis

in 2008 including the pre-financial crisis (January 2001-June 2007), during the financial crisis (July 2007-July 2009) and post-financial crisis (August 2009–

October 2020)4

r_S&P500t = а0+ а1r_S&P500t−1+ ϵt ~N(0, v_S&P500t) Eq (I.4)

log (v_S&P500t) = ϐ0+ ϐ1| ϵt−1

√v_&P500t−1| + ΩVOL_SSEC + log (ϐ2v_S&P500t−1)

r_S&P500t is the stock return of the S&P500 Index at (t) day; v_S&P500 is the variance of S&P500’s returns at (t) day VOL_SSEC is the variance of the Shanghai Composite’s returns and the Ω coefficient represents the impact of variance of Chinese stock market returns on the volatility of US stock market returns

We use the sub-samples corresponding to the pre-2004 period5 and post-2004 period to clearly examine the degree of volatility spillovers from China to the US stock market after the China economy become the second-largest economy in

2004 according to the PPP Index In addition, we use a sub-sample regarding the global financial crisis in 2008 to affirm the volatility effects from China to the US stock market in this period We use the EVIEW 10.0 software to estimate the AR (1)-EGARCH (1,1) models

1.4 Analyzing empirical results on the bidirectional volatility spillovers between the US and China stock markets

1.4.1 Basic analyses

Table 1.1 presents the descriptive statistics for the whole of stock market returns

in both the US and China markets including the S&P500’s returns (R_S&P500), Nasdaq Composite’s returns (R_IXIC), DJIA’s returns (R_DIJ), Shanghai Composite’s returns (R_SSEC), and Shenzhen Composite’s returns (R_SZSC) Overall, the mean values of stock returns in the US and China stock markets positively range from 0.0001 to 0.0003 The standard deviation (Std dev) indicators show that the Chinese stock market returns (SSEC’s returns, SZSC’s returns) are less stable than the US stock market returns (the S&P500’ returns, Nasdaq Composite’s returns, DJIA’s returns) implying that the emerging stock markets are more volatile than developed stock markets

[Insert Table 1.1 here]

4 Following Lien et al (2018), the global financial crisis period took place from July 2007 to July 2009

5 The pre-2004 period is determined from January 2001 to December 2003

Table 1.2 reports the results of stationary tests for the stock return series in both the US and China stock markets We employ three tests for the stationarity of time series including the Augmented Dickey–Fuller (1979) – ADF test, Phillip – Perron

(1988) – PP test, and Kwiatkowski et al (1992) – KPSS test In which, the results

of ADF and PP tests reject the non-stationarity of the Null hypothesis (H0) for both the US stock market returns and Chinese stock market returns at a 1% significant level Moreover, the results of the KPSS test can’t dismiss the stationarity of the Null hypothesis (H0) for both the US stock market returns and Chinese stock market returns at any significant level All stationary tests give a consensus conclusion that all stock returns in both the US and China stock markets are stationary

[Insert Table 1.2 here]

Next, we implement the ICSS algorithm by MATLAB software to detect structural breakpoints in the variance of stock returns in both the US and China markets In the period from January 2001-October 2020, we observe a total of 28 structural breakpoints in the variance of the S&P500’s returns, 36 structural breakpoints in the variance of the DJIA’s returns, 23 structural breakpoints in the variance of the Nasdaq Composite’s returns, 26 structural breakpoints in the variance of the Shanghai Composite’s returns and 22 structural breakpoints in the variance of the Shenzhen Composite’s returns Additionally, all break dates corresponding to the detected breakpoints are respectively presented in Table 1.3

[Insert Table 1.3 here]

Figure 1.2 illustrates the whole of structural breakpoints in the variance of US stock market returns and Figure 1.3 describes the whole of structural breakpoints

in the variance of Chinese stock market returns by the red vertical lines

[Insert Figure 1.2 here]

[Insert Figure 1.3 here]

Table 1.4 respectively reports the estimated results of the AR (1)-EGARCH (1,1) model for the volatility of US stock market returns (S&P500 Index, DJIA Index, Nasdaq Composite Index) and the volatility of Chinese stock market returns (SSEC Index, SZSC Index) without using the volatility breakpoints detected in

Table 1.3, as well as, the asymmetric effect

[Insert Table 1.4 here]

1.4.2 Empirical results on the volatility spillovers from the US to China stock market

1.4.2.1 Modeling the volatility of Shanghai Composite Index’s returns by using breakpoints in the variance of US stock market returns

Firstly, we investigate the volatility spillovers from the US stock market to the Shanghai Composite (SSEC) Index of the China stock market Table 1.5 respectively reports the estimated results of the AR (1)-EGARCH (1,1) model with

28 volatility breakpoints of the S&P500’s returns in Column (1), 36 volatility breakpoints of DJIA’s returns in Column (2), and 23 volatility breakpoints of Nasdaq Composite’s returns in Column (3)

[Insert Table 1.5 here]

Overall, empirical results in Column (1) indicate that the volatility of SSEC’s returns was not frequently affected but strongly by the volatility of the S&P500’s returns In the period 2001-2004, the volatility breakpoints of the US stock market returns have no impact the volatility of SSEC’s returns that is consistent with the previous findings (Li, 2007; Moon and Yu, 2010; Wang and Wang, 2010) During the global financial crisis in 2008, the volatility of the SSEC Index seemed to be strongly impacted by the volatility of the S&P500 Index Clearly, in the variance equation, dummy variables corresponding to the volatility breakpoints of the S&P500’s returns in 2008 are significantly positive and have higher magnitudes than the rest dummy variables These empirical results suggest that the volatility shocks of the S&P500’s returns in the recession period are involved significantly

in the volatility of the SSEC’s returns A notable break date is on 15th September

2008, the coefficient of the dummy variable reaches the highest value and is significant at a 1% level in the variance equation It’s not surprising because, at this time, the Lehman Brothers group declared collapse as one of the biggest securities companies in the US market It seems the biggest bankruptcy in US economic history, hence, this event is more likely to impact the whole of the international stock market

Besides the S&P500 Index, the DJIA Index and Nasdaq Composite Index also are highly representative of the US stock market Hence, we also apply the ICSS algorithm for both the DJIA Index and the Nasdaq Composite Index to detect volatility breakpoints of their returns Then, we use the AR (1)-EGARCH (1,1) model to estimate the volatility transmission of two US stock indexes to the Shanghai Composite Index, respectively, in Columns (2) and (3) In summary, empirical results in both Columns (2) and (3) are mostly similar to the showed findings in Column (1) Some coefficients of dummy variables corresponding to the volatility breakpoints of both the Nasdaq Composite Index and the DJIA Index are significant in the variance equation These empirical results reinforce our findings that the volatility of SSEC’s returns is less likely to be continuously affected by the volatility of the US stock market returns More specifically, the negative coefficients or positive coefficients with slight intensity appear from

2001 to 2004 implying that the volatility of SSEC’s returns is weakly affected by the volatility of DJIA’s returns and Nasdaq Composite’s returns On the other hand, the global financial crisis in 2008 creates favorable conditions to increase the volatility spillovers from the US to China stock market The concrete evidence is the overwhelming magnitude of dummy variables during the global financial crisis

breakpoint on 15th September 2008 when the collapse event of the Lehman Brothers group engulfed the whole of the US stock market, investors rushed to sell stocks in the US stock exchanges This event also influenced remarkably the volatility of SSEC’s returns and securities investors on the Shanghai Stock Exchange Otherwise, it clearly exposes the energetic volatility transmission from the US to China stock market in the crisis period

In brief, our findings based on using the S&P500 Index and the Shanghai Composite Index are consistent with the empirical results found by the different representative indexes of both the US and China stock markets

1.4.2.2 Modeling the volatility of Shenzhen Composite Index’s returns by using breakpoints in the variance of US stock market returns

Secondly, we investigate the volatility spillovers from the US stock market to the Shenzhen Composite (SZSC) Index of the China stock market which is also

an important stock index of China market Table 1.6 respectively reports the estimated results of the AR (1)-EGARCH (1,1) model with 28 volatility breakpoints of the S&P500’s returns in Column (1), 36 volatility breakpoints of DJIA’s returns in Column (2), and 23 volatility breakpoints of IXIC’s returns in Column (3)

From the coefficient of dummy variables in the variance equation of SZSC’s returns, we see that the volatility breakpoints of three US stock indexes influenced infrequently the volatility of SZSC’s returns in the period January 2001-October

2020 Compared to the SSEC Index, the volatility breakpoints of the US stock market have fewer significant impacts on the volatility of SZSC’s returns in the same period In three columns, some negative coefficients of dummy variables appear in the pre-financial crisis that means the volatility of the US stock market returns weakly affected the volatility of SZSC’s returns Noteworthy, the volatility breakpoints of the US stock market return’s volatility in 2008 positively impacted and more substantially on the volatility of SZSC’s returns In the other words, our empirical results are robust to the different US and China stock indexes

Overall, empirical results in both Table 1.5 and Table 1.6 show that the volatility

of the US stock hasn’t continuously impacted the volatility of China stock market covering the period 2001 to October 2020 It’s possible owing to the impact of foreign investment policy in China’s financial market or the restrictions in the bilateral policy of the US-China The best evidence for the volatility effects from the US to China stock market is majorly exposed in the global financial crisis On the other hand, in both Table 1.5 and Table 1.6, we do not find explicit evidence

on the volatility effects from the US to China stock market in the Covid-19 pandemic in the first quarter of 2020

[Insert Table 1.6 here]

1.4.2.3 Modeling the volatility of Chinese stock market returns by using the variance of US stock market returns

Table 1.7 shows the volatility spillovers from the US stock market to China stock market by using another volatility measure of the US stock market returns The volatility of the US stock market returns is measured by the variance of the S&P500’s returns covering the period January 2001-October 2020 Then, we estimate Equation (I.3) to test the volatility spillovers from the S&P500 Index of the US stock market to the SSEC Index of China stock market in the different sub-periods regarding the global financial crisis (pre-financial crisis, during the financial crisis, post-financial crisis) and the whole of the research period

[Insert Table 1.7 here]

Estimated results of Equation (I.3) are presented in Columns (1), (2), (3), and (4) corresponding to the separate periods regarding the global financial crisis in

2008 The coefficients of VOL_S&P500 variable positively related to the volatility of SSEC’s returns in all periods But, it’s not significant in the pre-financial crisis and post-financial crisis periods In the whole sample, the positive coefficient of VOL_S&P500 significantly exists with weaker intensity while it has

a stronger intensity in the global financial crisis period These results suggest that the strong volatility spillovers from the US to China stock market are more pronounced during the global financial crisis They are accorded with the empirical results in Table 1.5 and Table 1.6

In summary, the results are presented in Table 1.7 strongly support the reported findings in both Table 1.5 and Table 1.6 that the volatility of Chinese stock market returns was strongly impacted but infrequently by the volatility of the US stock market returns In large part of volatility spillovers significantly appear due to the terrible impact of the global financial crisis in 2008 In other words, our empirical results are robust when we use another measure to estimate the volatility spillovers from the US to China stock market

From the empirical results in Tables 1.5, 1.6 and 1.7, we noticed that the volatility transmission from the US equity market to emerging partners is a natural issue, but the infection degree mostly depends on the bilateral policies of both

sides In addition, we also determined the huge role of the global financial crisis

in motivating the volatility spillovers from the US to other equity markets

1.4.3 Empirical results on the volatility spillovers from the China to US stock market

1.4.3.1 Modeling the volatility of S&P500’s returns by using breakpoints in the variance of Chinese stock market returns

Firstly, we investigate the volatility spillovers from the China stock market to the S&P500 Index of the US stock market using the volatility breakpoints of the Shanghai Composite’ returns and the Shenzhen Composite’s returns Table 1.8 respectively reports the estimated results of the AR (1)-EGARCH (1,1) model with

26 volatility breakpoints of the SSEC’s returns in Column (1), 22 volatility breakpoints of SZSC’s returns in Column (2)

[Insert Table 1.8 here]

Broadly speaking, empirical results in Column (1) indicate that the volatility of S&P500’s returns was persistently affected but weakly by the volatility of the SSEC’s returns in the post-2004 period In the variance equation, most of the dummy variables corresponding to volatility breakpoints in the variance of the SSEC’s returns are significantly negative since 2004 A notable breakpoint is on

26th March 2020, its coefficient is significant at a 1% level and larger than 0.1 in the volatility equation of the S&P500’s returns That means this volatility breakpoint of the SSEC returns has the strongest impact on the volatility of S&P500’s returns In early 2020, the global economy was heavily influenced by the Covid-19 pandemic which onset from China country at the end of 2019, the international equity market was no exception The stock market crash occurred from 20th February 2020 to 7th April 2020, it event is comparable to the Wall Street crash in 1929 Most of the largest stock indexes in the world coevally went down

Nasdaq Composite Index, KOSPI Index Thus, it’s no surprise that a volatility breakpoint of SSEC’s returns at the end of March 2020 has the greatest on the volatility of the US stock market

Aside from the SSEC Index, Shenzhen Composite (SZSC) Index is also highly

representative of the China stock market We also apply the ICSS algorithm for the Shenzhen Composite (SZSC) Index to detect the structural breakpoints in the variance of its returns Then, we use the AR (1)-EGARCH (1,1) model to estimate the volatility spillovers from SZSC Index to the S&P500 Index In summary, empirical results in Column (2) are quite similar to the showed findings in Column (1) More specifically, a majority of dummy variables in the variance equation are significantly negative with the volatility of S&P500’s returns implying that the volatility of Chinese stock market returns has impacted continuously but faintly

on the volatility of the US stock market in the period post-2004 Similar to the SSEC Index, a volatility breakpoint of SZSC’s stock returns on 26th March 2020

is significant with the volatility of S&P500’s stock returns and its coefficient is more than 0.1 Empirical results in Table 1.8 provide strong evidence on the terrible impact of Covid-19 pandemic derived from China enhances the volatility spillovers from China to the US stock market at the end of March 2020

On the other hand, the empirical results in Columns (1) and (2) don’t clearly indicate the existence of the volatility spillovers from China to the US equity market during the global financial crisis (July 2007-July 2009)

1.4.3.2 Modeling the return volatility of other US stock indexes by using breakpoints in the variance of Chinese stock market returns

To clear our empirical results, we carry out two robust tests with other US stock market indexes, namely, DJIA Index and Nasdaq Composite Index Firstly, we repeat the ICSS algorithm with the Shanghai Composite Index (SSEC) and the Shenzhen Composite Index (SZSC) to detect their volatility breakpoints Then, in Table 1.9, we respectively model the volatility of the DJIA Index with detected volatility breakpoints corresponding to the SSEC Index in Column (1) and SZSC Index in Column (2) This process similarly implements for the Nasdaq Composite Index and the estimated results from the AR (1)-EGARCH (1,1) model are presented in Table 1.10

Results in Table 1.9 and Table 1.10 are mostly identical to the showed findings

in Table 1.8 To put it simply, the empirical results using the DJIA Index and Nasdaq Composite Index are robust to the findings of the S&P500 Index that the

volatility of the SSEC Index, as well as, the SZSC Index have weakly impacted and persistently on the volatility of both the DJIA Index and Nasdaq Composite Index since 2004 Most important, the coefficient of a breakpoint on 23rd March

2020 is significant with the volatility of both the DJIA and Nasdaq Composite Index and is larger than 0.1 That means the volatility effects from China to the

US equity market is substantially exposed when the Covid-19 pandemic outbreak Empirical results on the volatility spillovers from China to the US stock market are robust to the different stock indexes of both the US and China markets

[Insert Table 1.9 here]

[Insert Table 1.10 here]

1.4.3.3 Modeling the volatility of US stock market returns by using the variance of Chinese stock market returns

After nearly three decades of reform and opening economy, China's economy insists on its position in the international market as a powerful economy along with the US economy in 2004 The volatility of China stock market is more likely

to affect the volatility of other equity markets (Allen et al., 2013) Using another

volatility measure of the Chinese stock market returns, we investigate the volatility impact of the Chinese stock market returns on the US stock market returns therein the volatility of Chinese stock market returns is calculated by the variance of the SSEC’s returns Also, the volatility of the US stock market returns is represented

by the volatility of S&P500’s returns More specifically, we test the volatility spillovers from China to the US stock market in different periods (pre-2004 and post-2004), respectively, in Columns (1) and (2) Furthermore, we test these transmissions during the global financial crisis (July 2007-July 2009) in Column (3) to shed light on our findings in Tables 1.8, 1.9, and 1.10

[Insert Table 1.11 here]

The coefficient of VOL_SSEC variable is significantly negative with the volatility of S&P500’s returns in the entire sample (January 2001-October 2020) implying that the volatility of the Chinese stock market returns has influenced weakly on the volatility of the US stock market returns In addition, the negative coefficient of VOL_SSEC variable is only significant in the post-2004 period but

it not significant in the post-2004 period These empirical results in different periods suggest that the volatility of the Chinese stock market returns has significantly impacted and continuously on the volatility of the US stock market returns since 2004

sub-In Column (3), the coefficient of VOL_SSEC is not significant with the volatility of S&P500’s returns proving that the volatility of Chinese stock market returns didn’t affect the volatility of the US stock market returns during the global financial crisis

Said differently, our findings in Tables 1.8, 1.9, and 1.10 are robust, even we use another measure of the volatility of the Chinese stock market returns

1.5 Conclusion and Recommendation

While the US represents the biggest advanced market, China is typical of the largest emerging market in the world In this research, we investigate the bidirectional volatility transmissions between the US and China stock markets in the long-term based on the predictions of bilateral relations between the US and China, as well as, the limits of foreign investment policy in China’s financial market We use the daily stock returns in both US and China markets from January

2001 to October 2020 to embrace the global financial crisis in 2008 sourced from the US and the Covid-19 pandemic originated from China In addition, we apply

a new research methodology compared the previous studies on the same topics to survey the bidirectional volatility spillovers between the US and China stock markets

Our research verifies that the level of spillovers depends on the characteristics

of bilateral relations between equity markets Nevertheless, globalization certainly facilitates the volatility infection between equity markets The tremendous crashes

in the international market like a catalyst for the contagion It's odds with the ancient view that the volatility spillovers usually sourced from the 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 mostly exposed during the global financial crisis in 2008 Inversely, the volatility of China stock market continuously affected with weak intensity on the volatility of the US stock market in the period post-

2004 During the Covid-19 pandemic, we discover the strongest spillover of China stock market’s volatility to the volatility of the US stock market appears at the end

of March 2020

Our findings imply that for the recent 20 years, the integrated level between the

US and China stock markets is not tight entirely It might be influenced by the restrictions in government policies from both sides, as well as, limitations of foreign investment policy in China’s financial market Our findings are essential for securities investors in the two largest equity markets to hedge the predicted risks in their investment decisions

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Appendix A I

LIST OF FIGURES IN STUDY I

US stock market

China stock market

Figure 1.1 Examining the bidirectional volatility spillovers between the US and China stock markets

STANDARD AND POOR’S 500 INDEX

(SSEC)

28 breakpoints are detected in the variance of S&P500’s returns

36 breakpoints are detected in the variance of DJIA’s returns

23 breakpoints are detected in the variance of Nasdaq Composite’s returns Figure 1.2 The structural breakpoints in the variance of US stock market returns

are detected by the ICSS algorithm (2001 – 10/2020)

26 breakpoints are detected in the variance of SSEC’s returns

22 breakpoints are detected in the variance of SZSC’s returns

Figure 1.3 The structural breakpoints in the variance of Chinese stock market

returns are detected by the ICSS algorithm (2001 – 10/2020)

Appendix B I

LIST OF TABLES IN STUDY I

Table 1.1: Summarize statistics

R_S&P500 R_DJIA R_IXIC R_SSEC R_SZSC

to October 2020