Content uploaded by Judit Sági
Author content
All content in this area was uploaded by Judit Sági on Nov 25, 2024
Content may be subject to copyright.
Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Heliyon 10 (2024) e29413
Available online 9 April 2024
2405-8440/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Research article
Chinese stock market integration with developed world: A
portfolio diversication analysis
Azmat Sher
a
, An Haizhong
a
, Muhammad Kaleem Khan
b
,
*
, Judit S´
agi
c
a
School of Economics and Management, China University of Geosciences, Beijing, 100083, China
b
Asia-Australia Business College, Liaoning University, Shenyang, 110136, China
c
Faculty of Finance and Accountancy, Budapest Business University, H-1055, Budapest, Hungary
ARTICLE INFO
Keywords:
Cointegration
Portfolio diversication
Major trading partners
Stock market integration
VECM-based granger causality test
Short-run granger causality
ABSTRACT
This study investigates integration dynamics between the Chinese stock market and major
developed counterparts—Australia, Germany, Japan, the UK, and the US—focusing on portfolio
diversication. Using a comprehensive analytical approach from 2012 to 2022, encompassing
events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and
the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration
analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration,
endorsing diversied portfolios for developed country investors due to higher returns in China
with acceptable risk. Unit root analysis conrms cointegration with developed indices, indicating
relatively low integration. Granger Causality Tests reveal bidirectional causality, emphasizing
mutual inuence. Notably, no causal link exists between the US and China, possibly due to
regulatory disparities and the trade war. The study enhances understanding of Chinese stock
market dynamics, supporting global economic intertwining and urging further openness of
China’s domestic shares for economic growth.
1. Introduction
The integration and portfolio diversication between Chinese and developed world stock markets represent a nuanced and intricate
relationship [1]. This study specically explores the linkages between China’s stock markets and its signicant trading partners:
Australia, Germany, Japan, the UK, and the USA. These countries were selected based on their substantial trade volume with China and
their role as major sources of foreign investment. As the world’s leading exporter, boasting a positive trade balance of $458.93 billion,
China’s economic fortunes are closely entwined with these key partners [2–4].
The integration of China’s economy with developed nations, particularly in the nancial realms, introduces potential challenges
stemming from varying regulatory systems and market structures [5]. However, investors from developed countries have already
demonstrated substantial exposure to Chinese equities, emphasizing the need for a nuanced understanding of global equity market
integration, moving beyond broad generalizations [6]. The relationships between China’s equity market and those of its counterparts
uctuate across periods, demanding an exploration of the underlying factors inuencing these dynamics. Regulatory forces, economic
performance, and nancial aspects directly shape the extent of integration and diversication among these markets, with foreign
investment playing a pivotal role facilitated through offshore funds and direct participation in Chinese stocks [7]. The ongoing
* Corresponding author.
E-mail address: MuhammadKaleem.Khan@vu.edu.au (M.K. Khan).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2024.e29413
Received 21 November 2023; Received in revised form 31 March 2024; Accepted 8 April 2024
Heliyon 10 (2024) e29413
2
internationalization of China’s market has injected new capital, enhanced liquidity, and broadened equity ownership structures [8].
However, the study emphasizes the necessity to strike a balance between the benets of foreign investment and potential vulnera-
bilities to sudden macroeconomic and regulatory shifts.
Globalization, a prominent trend fostering economic growth and investment ows, has signicantly increased connectivity in the
world economy [9]. This trend, benecial for emerging economies like China, has propelled the nation into a central role in the in-
ternational nancial landscape [10–12] The integration of the Chinese stock market with other nations has been further fortied by
trade openness and increased investment [13]. Despite this growth, challenges, including heightened volatility, have emerged [14].
While economic globalization has witnessed an overall slowdown since the 2008 nancial crisis, China’s “Going Global” policy has
played a pivotal role in expanding Chinese businesses globally, establishing it as the world’s largest emerging market with rapidly
growing stock markets [15–17]. Alongside other BRICS economies, China has experienced substantial growth rates, averaging 6.2 %
annually compared to 1.9 % in the US [18,19]. This growth prompted a closer examination of interrelationships between world
nancial markets, with advancements in information technology contributing to increased market correlation [20–22]. In recent
years, the Chinese government has undertaken signicant steps to liberalize its capital markets, encouraging foreign investment
through various reforms, including issuing B- and H-shares, improving accounting standards, and linking the Shanghai and Shenzhen
markets to Hong Kong [23,24]. These reforms, initiated in 2012, aimed to deepen China’s nancial markets and enhance its role in
nancing investments, contributing to the “going global” process of Chinese stock markets [13,24]. As a result, China’s stock market
secured inclusion in the MSCI Global Stock Index, generating interest from foreign institutional investors [25].
China’s emergence as a global leader in promoting openness and integration, as demonstrated by initiatives such as the “One Belt
and One Road,” which involves infrastructure development and investments in 70 countries, highlights its expanding involvement in
international trade [26,27]. While skepticism exists regarding the primary benets of this initiative for China, the inclusion of the
Chinese currency (RMB) in the basket of Special Drawing Rights by the IMF in 2015 acknowledges China’s increasing role in inter-
national trade [28]. Advances in China’s monetary, foreign exchange, and nancial systems demonstrate its growing inuence and
positive strides in implementing reform measures [29]. Over the past decade, China’s revolutionary reforms have opened up its global
market, fostering economic growth, development, and increased reserves. This transformation has elevated China’s global inuence,
positioning it as the world’s second-largest economy and a signicant recipient of foreign direct investment (FDI). The World In-
vestment Report 2021 reveals a 6 % increase in FDI in China in 2020, reinforcing China’s reform agenda and growth strategy [30]. The
global economy’s shift towards China has created opportunities for enterprises to enter the Chinese market, signaling anticipated
changes in economic integration [31].
The concept of cointegration among equity markets has recently gained attention due to its profound impact on portfolio diver-
sication. As capital markets become increasingly integrated, achieving diversication weakens, necessitating domestic investors to
comprehend the relationship between international nancial markets and their domestic counterparts for favorable portfolio per-
formance [6]. The growing interconnectedness of stock markets further diminishes the effect of diversication, compelling foreign
investors to understand how Chinese equity markets respond to global shocks [32]. The crashes of the Shanghai stock market in 2007
and 2016 underscored the potential impact of emerging economies’ stock markets on a global scale, causing temporary nancial
instability worldwide, including in the United States, Australia, Japan, and several ASEAN countries [13,33,34]. Given China’s sig-
nicant role in the global economy, a nancial crisis could considerably inuence its trading partners, particularly in the East Asia
Pacic region and countries integrated into China’s supply chains [13,35]. Factors contributing to the integration of international
stock markets include mutual trade, globalization of nancial markets, increased capital mobility, and advancements in communi-
cation and transportation technologies [36].
Extensive research has been conducted on the dynamic relationship between China and the world’s developed and developing
stock markets. These studies, yielding mixed ndings, have contributed to early perceptions that the Chinese equity market may not
necessarily correlate with global market trends [37,38]. However, other arguments assert its increasing integration with the global
market [39–43]. Research by Refs. [13,44–52] have also indicated growing integration between China and its Asian neighbors’ stock
markets. International fund managers recognize the potential for diversication and increased returns by including investments in
China and ASEAN countries within their portfolios [38,53–56]. As the Chinese authorities continue their economic reforms, nancial
integration between China and the rest of the globe is expected to continue its upward trajectory [5]. Examining stock market inte-
gration in nance is paramount, directly inuencing asset allocation and portfolio diversication strategies. Highly integrated stock
markets may lead to reduced advantages of international diversication, while fragmented markets offer opportunities for diversi-
cation and potential market variation gains [57,58]. Thus, the exploration of the benets of international portfolio diversication has
sparked signicant interest in the realm of international nance.
This study comprehensively examines the integration and portfolio diversication between Chinese stock markets and its major
trading partners in the developed world. Understanding the dynamics between these markets is crucial for investors and policymakers.
The research delves into the changing landscape of Chinese nancial markets, analyzing price and volatility transmissions before and
during the 2015–2016 crash and the impact of subsequent events like the US-China tariff war of 2018 and the COVID-19 pandemic. A
key objective is assessing potential portfolio diversication opportunities within these alliances. This research distinguishes itself by
focusing on how Chinese stocks respond to evolving market dynamics in the countries they are traded with. Additionally, the study
explores the inuence of nancial turmoil on Chinese and other stock markets, analyzing a sample period that includes these pivotal
events. This research makes a noteworthy contribution to the existing literature through its correlation research of multivariate time
series analysis of nancial markets. Notably, it represents the rst attempt to investigate the integration and portfolio diversication
between Chinese stock markets and these selected developed world markets. The research aims to bridge a gap by examining both
short- and long-term aspects of stock market integration and the potential for portfolio diversication between Chinese and developed
A. Sher et al.
Heliyon 10 (2024) e29413
3
world stock markets. Moreover, while existing research has focused on comovements during the global nancial crisis, this study
uniquely investigates the consequences of signicant post-crisis shocks. Specically, the study analyzes the impact of the global crash
of Shanghai’s stock exchange during 2015–2016, the US-China tariff conicts in 2018, and the outbreak of COVID-19 on stock market
disruptions.
2. Literature review
This paper endeavors to scrutinize stock market comovements and explore portfolio diversication opportunities within collab-
orative alliances, aiming to contribute valuable insights to the existing academic literature. The integration of the Chinese stock market
with the developed world is a dynamic area of research intricately linked to economic integration and interdependence. Understanding
the current dynamics of these markets and the potential for efcient capital allocation through international diversication is of
paramount signicance to investors. Consequently, this study seeks to provide a comprehensive analysis of stock market relationships,
shedding light on potential portfolio diversication strategies.
2.1. Introduction to portfolio diversication and integration
The stock market’s pivotal role in economic development cannot be overstated, providing a crucial avenue for businesses to secure
funds and fostering overall economic growth [59,60]. Within the nancial realm, diversication and integration are vital strategic
tools involving astute fund allocation across diverse assets or markets to mitigate risk [61]. Integration, a key element of this strategy,
entails interlinking stock markets, exchanging crucial information, and sharing experiences of market shocks [12,62].
This review analyzes the intricate connection between Chinese stock markets and major trading partners, including Australia,
Germany, Japan, the UK, and the US. Understanding the evolving relationships between Chinese and global stock markets is
imperative, especially in an era where interconnectedness may threaten diversication advantages [63–65]. The current state of the
global economy, especially within stock markets, showcases varying degrees of interdependence, with some markets exhibiting high
levels of interconnectedness while others maintain more limited connections [66–70].
Empirical literature scrutinizes the escalating trend of nancial integration among nations, particularly evident in China’s stock
markets after liberalization policies in 2001 [71–74]. This review tracks the evolving relationship between Chinese stock markets and
counterparts in the developed world, recognizing the imperative need for making informed decisions and implementing effective risk
management strategies.
2.2. Review of Chinese stock market developments
The journey of China’s stock market since its initiation in 1990 unfolds a narrative of profound transformations shaped by strategic
nancial reforms [24]. These reforms, not merely administrative, embody a narrative of evolution with regulatory measures attracting
foreign investors. These deliberate steps trigger substantial shifts in interconnectivity and diversication potential between Chinese
and developed world stock markets, marking a paradigmatic shift [37,65,75].
In the intricate tapestry of global nance, emerging markets like China present a mosaic of varying integration degrees with global
counterparts, necessitating further research [76,77]. Peeling back the layers of this complex relationship becomes essential to unveil
the factors shaping integration dynamics, providing a roadmap for investors and policymakers navigating these uncharted waters.
China’s gradual embrace of international nancial markets has expanded economic horizons and woven tighter connections be-
tween its stock markets and major trading partners. Understanding the ebb and ow of these relationships is crucial for effective risk
management and seizing investment opportunities within this evolving landscape [13,34,42,78].
2.3. Comparative analysis: Chinese and developed world stock markets
The integration of the Chinese stock market with developed economies has been a focal point of academic research, especially
concerning portfolio diversication. Empirical studies investigating the correlation and diversication of investment portfolios
involving Chinese stock markets and their developed world counterparts reveal a growing integration and interdependence between
China and the global nancial markets [76,77,79].
In the dramatic storyline of the COVID-19 pandemic [80], uncovered a riveting chapter, exposing the transmission of nancial
contagion between China and G7 countries. China and Japan emerged as central characters, acting as both transmitters and recipients
of spillovers, casting a spotlight on their pivotal roles in the interconnected web of global nance. Positive connections and bidi-
rectional causality between Chinese and developed markets signify a reciprocal inuence [81,82]. Moreover, the study conducted by
Ref. [74] on market integration and interdependence following the implementation of nancial liberalization policies identied a
notable increase over the study period. Notably, empirical research has delved into the integration between China’s stock market and
various counterparts in the developed world, encompassing Australia, Germany, Japan, the UK, and the US [83]. A study conducted by
Refs. [84,85] suggests a demand for increased integration between China and Japan’s stock markets, possibly indicating a form of
market isolation for China. While existing studies suggest potential integration needs between China and Japan, these ndings are not
exhaustive, necessitating further research for a comprehensive understanding of the relationship between Chinese stock markets and
their major trading partners.
A. Sher et al.
Heliyon 10 (2024) e29413
4
2.4. Exploring the connection between the Chinese and Australian stock market
The integration between the Chinese and Australian stock markets is a subject of extensive scholarly inquiry driven by the
increasing interconnectedness of global nancial markets. This analysis aims to navigate the intricacies of their integration and assess
implications for portfolio diversication, fortifying our arguments for the chosen research analysis method. Empirical studies have
explored the evolving integration between these markets, inuenced by economic, regulatory, and geographical factors. Economic ties
and China’s global prominence deepen the interdependence of their nancial markets [86]. found co-movements, indicating inte-
gration, while [87,88] highlight interdependence, especially compared to the United States. Geographical proximity, notably with
nearby Asian countries, has a substantial impact on the dependence effect. The importance of portfolio diversication arises from the
fundamental principle of risk management, leveraging correlations between markets. The integration between the Chinese and
Australian markets directly inuences the effectiveness of diversication strategies [74]. research explores integration’s extent and
potential benets for portfolio diversication, emphasizing existing opportunities and impact on correlation dynamics. A robust
research analysis method is imperative, with cointegration analysis, as employed by Refs. [86,87], offering valuable insights into the
long-term relationship between nancial markets. Drawing parallels to existing research methodologies, such as [74] enhances
comprehensiveness, ensuring robust ndings across different statistical approaches. Identifying gaps and areas necessitating further
investigation is crucial. Regulatory changes, economic policy shifts, and global market dynamics signicantly impact integration [83].
research explores integration between China’s stock market and various developed counterparts, underlining the need to address these
gaps for a comprehensive understanding of Chinese-Australian stock market integration. In conclusion, the integration between the
Chinese and Australian stock markets is complex with profound implications for portfolio diversication. Referencing scholarly in-
sights and drawing parallels to diverse research methodologies forties our analytical approach. The selected research analysis
method, encompassing cointegration analysis, dynamic conditional correlation models, and insights from studies like [86,89], posi-
tions us to unravel integration’s intricacies comprehensively. Ongoing research and methodological renement remain imperative for
a nuanced understanding of Chinese-Australian stock market dynamics and their implications for portfolio management as global
nancial markets continue to evolve.
2.5. Analysis of Chinese-German market correlations
Empirical studies extensively explore the evolving correlation between the Chinese and German stock markets, considering eco-
nomic ties, regulatory frameworks, and global economic dynamics [90–92]. An examination of German stock markets after China’s
nancial liberalization suggests a strengthening correlation, indicating increased interconnectedness over time. Similarly, studies by
Refs. [93–95] provide evidence supporting a growing relationship between the Chinese and German stock markets. However, con-
trasting perspectives from Refs. [96–98] propose a limited impact of China’s stock market on Germany’s market performance,
implying a weaker correlation.
In a recent study [99], investigated the dynamics of the Chinese-German stock market relationship. Using wavelet analysis, they
identied time-varying correlations, suggesting evolving linkages. The study proposed that Chinese stocks could serve as effective
diversiers for German investors, especially during times of economic uncertainty. Despite these insights, the study emphasized the
need for further research to understand short-term and long-term dynamics in this integration. Consequently, whether a signicant
correlation or connection exists remains uncertain, necessitating further research to gain a comprehensive understanding of their
relationship.
In conclusion, the correlation between the Chinese and German stock markets is a complex phenomenon with signicant impli-
cations for portfolio diversication. Referencing existing scholarly insights and drawing parallels to diverse research methodologies
forties our analytical approach. The selected research analysis method, encompassing cointegration analysis, dynamic conditional
correlation models, and insights from studies by Refs. [27,39] and others, positions us to understand the intricacies of this relationship
comprehensively. As global nancial markets continue to evolve, ongoing research and methodological renement are imperative for
a nuanced understanding of Chinese-German stock market dynamics and their implications for portfolio management.
2.6. In-depth study: Chinese market’s relationship with Japan’s stock market
China and Japan share a signicant trading relationship, leading to extensive academic research on their stock market connections.
While early studies suggested a limited correlation between the Chinese equity market and global trends, recent research, including
works by Refs. [39,41–43] indicates an increasing integration with the worldwide market, reecting the evolving nature of this
relationship [100,101]. study reveals information transfer from the Chinese to the Japanese stock market, indicating integration and
inuence [102]. have emphasized understanding dynamics among neighboring markets, highlighting stock market interdependence
[38]. In a recent study by, Recent study by Ref. [99] focused on the linkages between the Chinese and Japanese stock markets.
Employing a dynamic conditional correlation model, they found short-term contagion during COVID-19 but remained low during
normal periods. The study suggested that Japanese investors could benet from diversifying their portfolios with Chinese assets,
especially during turbulent market conditions. However, the research indicated the need for further investigation into the impact of
economic events on this integration.
Existing literature presents a consensus on the growing integration between the Chinese and Japanese stock markets. Studies by
Refs. [13,38,46,47,49,51,52,103] demonstrate the increasing interdependence of stock markets within Asia, indicating reduced
diversication benets as integrated markets tend to move in tandem. Integrated markets might pose challenges to diversication
A. Sher et al.
Heliyon 10 (2024) e29413
5
strategies, a pivotal element in risk management.
The economic ties between China and Japan, substantial global roles, and geographical proximity contribute to the deepening
interdependence of their nancial markets [104]. The integration of China and Japan, as major economic players in the Asian region,
has been accentuated by strengthening economic ties. Understanding this relationship is crucial for comprehending bilateral relations
and broader dynamics in the Asian region, with signicant implications for investors and policymakers [49,52].
The implications of Chinese-Japanese stock market co-movements are crucial for portfolio diversication. Integrated stock markets
tend to reduce diversication benets, while fragmented markets offer variation gains and diversication opportunities. Global in-
vestors need to understand the dynamics of co-movements between these markets to formulate effective diversication strategies [46,
49].
Identifying gaps and areas for further investigation is integral to advancing our understanding of Chinese-Japanese stock market
co-movements. Changes in regulatory environments, geopolitical events, and macroeconomic shifts may signicantly impact market
dynamics over time. Continuous monitoring of these factors and rening research methodologies are essential for staying ahead of
evolving trends in the Chinese-Japanese stock market integration landscape.
2.7. Exploring the UK as a major trading partner in China’s stock market
The exploration of the integration between the Chinese and UK stock markets and its implications for portfolio diversication
stands as a critical domain within the scholarly inquiry, driven by the escalating interconnectedness of global nancial markets.
Empirical studies have extensively delved into the correlation between Chinese and UK stock markets, particularly in terms of yield
and volatility [105,106]. identied a link between US and UK stock markets post-European Monetary Union, suggesting a connection
with China, especially in yield and volatility [72,93]. highlighted interdependence among China, Hong Kong, Japan, Germany, the UK,
and the US during the 2008 nancial crisis, indicating a connection between the UK and Chinese markets through volatility trans-
mission [103]. identied long-term relations between the Greater China region and the UK and US stock markets. However, the
strength of these relationships may vary. Diverse ndings, such as those by Ref. [107] exploring the impact of global nancial crises on
UK-China stock market connections, and [88] showing that the UK and US stock markets do not signicantly impact China’s market
performance, underscore the complexity and variability of the relationship, necessitating a nuanced and thorough analysis.
[103] employed a conditional correlation GARCH model to investigate the integration between the Chinese and UK stock markets,
revealing time-varying correlations and suggesting potential benets for UK investors through portfolio diversication. This approach
emphasizes the importance of considering economic events in understanding integration dynamics. However, the research also noted
the need for further investigation into the impact of geopolitical factors and market regulations, underlining the continuous evolution
and complexity of market relationships.
The implications of Chinese-UK stock market integration for portfolio diversication are pivotal, as integrated markets may reduce
diversication benets while fragmented markets offer variation gains and diversication opportunities [13,44,46,49]. demonstrate
through their studies on Asian stock markets that integrated markets tend to move in tandem, indicating reduced diversication
benets. Global investors must comprehend the dynamics of integration between Chinese and UK stock markets to formulate effective
diversication strategies.
2.8. Chinese stock market and US stock market: an analytical relationship
The examination of Chinese-US stock market integration reveals a complex landscape shaped by economic, regulatory, and
geopolitical factors [108,109]. Empirical studies shed light on the profound interdependence arising from substantial economic ties
and China’s ascendance as a global economic force. Noteworthy variations in research ndings, with [4] challenging a long-term
connection and contrasting perspectives presented by Refs. [41,96], and [80], underscore the evolving nature of this relationship,
demanding a sophisticated analysis.
Understanding the portfolio diversication implications stemming from Chinese-US stock market integration is indispensable for
crafting effective risk management strategies. A profound comprehension of correlation dynamics between these markets is paramount
for global investors seeking to diversify risks strategically. Credible references, such as [110], illuminate the intricate association
between China’s stock market and its global counterparts, emphasizing the interconnected nature of portfolios. Insights from
Ref. [108] accentuate that changes in China’s stock market conditions could intricately inuence sales and subsequent movements,
introducing layers of complexity to portfolio management strategies.
The chosen research analysis method should be robust and tailored to capture the nuanced dynamics of integration. Methodologies
like cointegration analysis, as employed by Ref. [41] prove essential for scrutinizing the long-term relationship between these nancial
markets. Dynamic conditional correlation models and GARCH models emerge as valuable tools for gaining insights into the evolving
correlations over time. Aligning our approach with existing research methodologies, especially those utilized by Ref. [110], enhances
the depth and comprehensiveness of our analysis, ensuring the robustness of our ndings across diverse statistical approaches.
Acknowledging valuable insights from existing research is pivotal in identifying gaps and areas necessitating further investigation.
Regulatory changes, geopolitical events, and macroeconomic shifts emerge as critical factors impacting market dynamics. References
such as [108,109] underscore dissimilar regulatory environments and ownership structures between Chinese and US markets,
underscoring the continuous reassessment of the chosen research analysis method in light of evolving market conditions.
In conclusion, the integration between Chinese and US stock markets unfolds as a multifaceted and evolving phenomenon with
profound implications for portfolio diversication. Anchoring our analysis with existing scholarly insights and aligning with diverse
A. Sher et al.
Heliyon 10 (2024) e29413
6
research methodologies fortify our analytical approach. The selected research analysis method, incorporating cointegration analysis,
dynamic conditional correlation models, and insights from studies like [110], positions us to unravel the intricacies of this integration
comprehensively. As global nancial markets continue to evolve, ongoing research and methodological renement remain imperative
for a nuanced understanding of Chinese-US stock market dynamics and their implications for portfolio management.
2.9. Implications of the integrated stock market for global traders
The correlation between Chinese stock markets and those in developed countries holds profound implications for global traders
[83,111]. However, it is essential to recognize that Chinese stock markets exhibit unique dynamics that only partially align with
developments observed in developed countries [76]. Consequently, global traders must conduct a nuanced analysis of the Chinese
market, accounting for specic regulatory policies and unique economic indicators. Relying solely on global market trends may not
capture the intricacies of investing in Chinese stocks effectively.
The diverse levels of integration and interdependencies underscore the strategic advantages of portfolio diversication between
Chinese and developed world stock markets. Allocating investments across these markets can effectively reduce risk and potentially
enhance returns, given the varying degrees of integration observed [37,112,113]. However, it is crucial to acknowledge that inte-
gration with developed markets may introduce heightened volatility and contagion effects. Therefore, close monitoring of both do-
mestic and international market conditions is imperative for global traders to manage risks effectively [65,114–117].
The integration with developed markets opens up appealing opportunities for implementing cross-border investment strategies.
Global traders can explore arbitrage opportunities by capitalizing on price discrepancies between markets [76,113,118–120].
Moreover, this integration enables investors to diversify portfolios across different regions, potentially mitigating risk and optimizing
returns [121–124].
To effectively navigate the implications of integrating stock markets with developed ones, global traders must meticulously
consider the level of integration and associated risks and opportunities [122]. Access to comprehensive data on the relationship be-
tween Chinese stock markets and signicant trading partners is crucial for making informed investment decisions and implementing
effective portfolio management strategies in today’s interconnected global nancial system [118].
2.10. Future trends and predictions of Chinese stock market integration
The exploration of future trends and predictions in Chinese stock market integration stands as a pivotal area of research, partic-
ularly in light of ongoing globalization and the increasing openness of China’s nancial markets to international investors [50,122].
The heightened accessibility of the Chinese stock market to global markets, alongside cross-border investments, technological ad-
vancements, and nancial liberalization initiatives by Chinese authorities, has substantially fortied its linkages with crucial trading
partners [24,83,122]. As China’s economy expands and exerts global inuence, investor interest in incorporating Chinese stocks into
their portfolios has surged [122]. The deepening interconnectivity between Chinese stock markets and developed nations magnies
the impact of international market uctuations on the Chinese market [125]. This investigation contributes valuable insights to the
literature on global stock market integration by scrutinizing the relationship between China and its primary trading partners in
developed countries. A pivotal objective is to assess potential opportunities for portfolio diversication within these alliances,
concentrating on the behavioral aspects of Chinese stocks amid evolving market dynamics in their trading markets with other
countries. This underscores the imperative for further research to comprehensively fathom the relationship between Chinese stock
markets and their major trading partners.
The examination of future trends and predictions in Chinese stock market integration with global investors assumes signicance
due to China’s expanding economic inuence and its heightened receptivity to international markets. The augmented connectivity
with key trading partners, propelled by cross-border investments and nancial reforms, necessitates vigilant monitoring of global
trends for effective risk management. Empirical evidence suggests varying degrees of integration and interdependencies with devel-
oped nations, underscoring the necessity for nuanced analyses grounded in specic trading partnerships. This study adds valuable
insights by evaluating existing evidence of both long-term associations and short-term connections, utilizing diverse methodologies
that extend beyond traditional sample periods. Sustained scholarly attention and methodological innovation are imperative for
comprehending the evolving dynamics of Chinese stock market integration.
3. China’s global trade: a study of top partners in developed countries
As the world’s largest trading nation, China has established solid nancial collaborations with key global partners, such as
Australia, Germany, Japan, the UK, and the USA. These nations’ signicant roles in various industries and their diverse geographical
locations across America, Europe, the Pacic, and Asia make them crucial inuencers in shaping the Chinese economy. As a result,
these partnerships have far-reaching effects, contributing to stable economic conditions on a global scale.
One of the ways these collaborations achieve mutual benets is evident in the trade between China and Australia. The increasing
demand for Australian goods, particularly raw materials, creates a robust market for Chinese exports, fostering a mutually benecial
relationship. German engineering and manufacturing products also contribute to modernizing Chinese infrastructure and industries,
driven by increased German investment initiatives. Likewise, Japan, the UK, and the USA serve as vital benchmarks for the progress of
developing economies while promoting competitive policies that benet all stakeholders involved.
In summary, China’s nancial ties with these vital global partners are pivotal in shaping local and international economic
A. Sher et al.
Heliyon 10 (2024) e29413
7
conditions, fostering growth and stability across various sectors.
Fig. 1 depicts the bilateral trade dynamics, encompassing both imports and exports, between China and its respective trading
partners. In 2022, China’s global trade reached an impressive $6.3 trillion, boasting a positive trade balance of $458.93 billion, making
it the world’s leading exporter despite trade tensions and COVID-19’s economic impact. The United States is China’s top export
destination, while other signicant trading partners include Australia, Germany, Japan, and the UK, which were China’s major Foreign
Direct Investment (FDI) sources in 2019. Notably, the United States invested $14.3 billion, followed by Japan with $10.8 billion, and
Germany with $9.9 billion, highlighting the signicant economic connections between China and these developed countries. However,
it is crucial to recognize that current circumstances may have been inuenced by the COVID-19 pandemic and the emergence of trade
tensions [126].
Integrating stock markets among nations is pivotal, demonstrating economic interdependence and facilitating informed decision-
making for investors, traders, and policymakers. Understanding these linkages is essential for identifying promising investment op-
portunities and contributing to a stable global nancial system.
4. Methodology
The study employed the following research methodologies to achieve the results:
•Descriptive statistics
•Unit root tests (Augmented Dickey-Fuller test, Phillips and Perron test)
•Cointegration tests (Johansen’s bivariate and multivariate tests)
•Granger Causality tests Based on VECM (Vector Error Correction Model)
The empirical steps of this paper are organized and written serially in Figure 2.
4.1. Calculation of the mean
The mean, denoted as x, signies the average value within a set of observations. This is determined by dividing the sum of all values
Fig. 1. China’s Trade Dynamics with its Major trading Partners in the Developed World (2020–21).
CHINA IMPORTS-EXPORTS UNDER STUDY COUNTRIES 2020–21.
Source: China trade balance, exports, imports by country and region 2020 | WITS Data (worldbank.org)
Fig. 2. The process of cointegration, Granger causality, and VECM model. (Source: Created by the author).
A. Sher et al.
Heliyon 10 (2024) e29413
8
by the total number of observations (n). The formula for the mean (x) is expressed as:
x=1
n
n
i=1
xi(1)
Here, In Eq. (1) x represents the sample mean, xi denotes the ith observation, n indicates the sample size, and the summation
notation n
i=1
′
signies the accumulation or summation of all observations from the rst (i=1) to the last (n).
4.2. Calculation of standard deviation
The standard deviation (SD) is a statistical measure that quanties the extent to which individual data points within a dataset
deviate from the mean, serving as an indicator of the data’s dispersion. The formula for standard deviation is given by:
σ
=
1
n
n
i=1
(x1−x)2
(2)
Here, In Eq. (2)
σ
denotes the standard deviation, x represents the sample mean, x1 is the i th observation, n is the sample size, and
the summation notation n
i=1
′
signies the sum of squared deviations from the sample mean for each observation, ranging from the rst
(i=1) to the last (nth) observation.
4.3. Analysis of skewness
In the realm of nancial analysis, skewness stands as a pivotal metric for discerning the asymmetry inherent in a distribution. At its
core, skewness reveals which side of the distribution’s tail is more extended. When skewness is positive, often referred to as rightward
skewness, it signies that the distribution boasts a lengthier tail on the right. Conversely, negative skewness, or leftward skewness,
indicates a more extensive tail on the left. This nuanced conceptualization of skewness provides profound insights into the contours
and tendencies of the distribution, thereby enriching our comprehension of its asymmetrical characteristics. In the context of our
investigation into the integration of the Chinese stock market with developed economies, understanding the skewness of the portfolio
diversication potential becomes imperative, offering a rened lens through which to assess the market dynamics and optimize in-
vestment strategies.
4.4. Examination of kurtosis
Kurtosis, a vital statistical measure, unveils the “tailedness” of a probability distribution, enriching our understanding of nancial
data. A mesokurtic distribution aligns with normalcy (kurtosis =3), while shifts in kurtosis alter the distribution’s shape—decreasing
kurtosis broadens the peak and thickens tails, and increasing kurtosis (>3) crafts a thin-bell distribution. Recognizing leptokurtic (>3)
and platykurtic (<3) distinctions is pivotal. This nuanced grasp of kurtosis amplies our analytical prowess, which is crucial in
interpreting probability distribution intricacies within the Chinese Stock Market Integration with the Developed World framework.
4.5. Unit root tests
Assessing the stationarity of the data series is imperative to analyze cointegration. Stationarity refers to when a time series’s mean
and standard deviation remain constant over time. Conversely, non-stationary data exhibits a time-varying mean or standard deviation
[127]. To determine stationarity, it is crucial to specify the order of integration. A time series that evolves stationary after differencing
d times is considered integrated of order d, or I(d). A variable with an integration order equal to or higher than 1 will exhibit
non-stationarity. The unit root test is commonly utilized to examine an index series’s stationarity and cointegration relations’ exis-
tence. In this research, the ADF test (Augmented Dickey-Fuller 1981) and PP test (Phillips-Perron, 1988) will be used, as supported by
previous studies conducted by Refs. [41,128–131].
Below are the ADF test equations for log level and rst Difference:
yt=
ρ
yt−1+ut(3)
Δyt=δyt−1+ut(4)
In Equation (3), " yt" represents the value at the time ‘t’, " yt−1" means the value from the previous time point (’t-1
′
), "
ρ
" is a projected
parameter utilized to decide the stationarity of the series, and " u
t
” is a random noise series with an average of 0 and a variance of 2.
Rejecting the null hypothesis indicates stationarity. In practice, method (3) is rarely utilized, and instead, method (4) is employed to
test for the existence of a unit root in the data, where δ =0 is tested instead of
ρ
=1.
A. Sher et al.
Heliyon 10 (2024) e29413
9
4.6. Cointegration test
According to economic theory, two or more economic variables are believed to have a long-term connection, despite short-term
uctuations. These variables are assumed to be cointegrated, with economic forces eventually restoring the original equilibrium
between them. To decide the cointegration connection between the selected variables, both integrated in the same order, two
commonly used tests are the Engle and Granger two-step cointegration test and the Johansen cointegration test. The Engle and Granger
test is less effective when analyzing multivariate models. This analysis will utilize the Johansen cointegration test to investigate the
long-term association among the data series. This procedure includes two types of tests: the trace test statistic and the maximum
eigenvalue test statistic. Many previous studies have also used these techniques [106,130].
Following is the equation for the Johansen test:
xt=A0+A1Δxt−1+A2Δxt−2+.. …….+Ak−1Δxt−k+xt−k+
ε
t(5)
In the rst step, the order of integration is tested; in the second, the lag length is selected; nally, in the third stage, the Cointegrating
vectors are decided. This test presents two likelihood ratio tests for this objective, the “trace test” and the “maximum Eigenvalue test".
λtrace(r) = − T
n
i=r+1
ln (1+λ)et(6)
λmax(r,r+1) = − T ln (1−λr+1)(7)
H
0
: П =r.
H
1
: П =r+1.
In this context, In Eq. (6) the symbol “r" signies the count of cointegrating vectors assumed in the null hypothesis, and "λ_i" denotes
the estimated value of the “ith” ordered eigenvalue of the matrix "П." Notably, as the magnitude of "λ_i" increases, the value of ln (1- λ_i)
becomes more negative, leading to a higher test statistic.
Equation (6) presents a test that considers all eigenvalues collectively and is based on the null hypothesis (H0) that the number of
cointegrating vectors is equal to or less than “r.” In contrast, the alternative assumption suggests a greater number of cointegrating
vectors. On the other hand, equation (7) is applied to each eigenvalue individually. It assumes a null hypothesis that the number of
cointegrating vectors equals “r,” with the alternative assumption indicating that it is equal to “r+1.” When the test statistic exceeds the
critical level determined by Johannsen’s tables, the null hypothesis is rejected for both tests in favor of the alternative hypothesis.
The process of checking follows a sequential approach starting with the null hypothesis of “r =0
″
and then proceeding with “r =1,
…, r =g-1,” representing a rank of zero for the matrix "П." If this null hypothesis is not rejected, it concludes that there are no
cointegrating relations among the variables in the VAR model, and the assessment is considered complete. However, if the null hy-
pothesis is rejected, it leads to investigating whether the matrix "П" rank equals 1. Accepting this hypothesis suggests only one
cointegrating connection among the variables in the model, and the process continues for subsequent hypotheses.
4.7. Granger Causality Tests based on VECM
The advantages of employing Granger Causality Tests based on VECM for studying integration and portfolio diversication in
nancial markets are manifold. Firstly, these tests are particularly suitable for analyzing time series data commonly observed in stock
markets, allowing the exploration of temporal interactions between variables over different periods. Secondly, the combination of
VECM and Granger Causality Tests enables the examination of dynamic relationships between stock markets, revealing short-term and
stable long-term causal links. Such insights help in understanding the direction of causality between markets, essential for informed
decision-making by investors and policymakers.
Moreover, the tests offer valuable information on portfolio diversication opportunities. Detecting signicant causality between
certain markets indicates potential benets in diversifying investments to reduce overall portfolio risk. Additionally, Granger Causality
Tests assist in assessing the level of interdependence between stock markets, indicating the extent of market integration and guiding
investment strategies and risk management.
VECM with Granger Causality Tests also proves benecial for analyzing the impact of specic events on stock market interactions,
such as economic shocks or policy changes. Understanding how global events inuence market behavior is crucial for decision-makers.
Moreover, the model allows researchers to test specic hypotheses related to stock market integration and portfolio diversication,
investigating if one country’s market movements cause changes in another country’s market.
In addition, VECM accounts for cointegration among variables, particularly vital in nancial markets where some assets may have
long-term relationships. By distinguishing short-term causality from long-term relationships, the model provides robust and reliable
results.
A. Sher et al.
Heliyon 10 (2024) e29413
10
In conclusion, Granger Causality Tests based on VECM offer valuable insights into causal relationships between stock markets,
aiding researchers, investors, and policymakers in understanding market integration and portfolio diversication dynamics. By
identifying interconnectedness and causality between markets, this model supports well-informed investment decisions and risk
management in a globalized nancial landscape. However, it is essential to interpret the results cautiously, considering other factors
and theories to draw meaningful conclusions about stock market behavior.
The Granger representation theorem states that in the case of cointegrated series CI (1,1), it is essential to incorporate an error
correction term into the model to account for the long-term association between the variables; failure to include this term leads to
model misspecication as posited by Engle and Granger (1987) and Toda and Phillips (1993). Previous studies have also used these
techniques [130–133].
Hence, this model, commonly known in the literature as a VECM (Vector Error Correction Model), is utilized:
ΔYit =
α
+ξ
′
Zt−1+
m
t=1
aiΔY1,t−i+
m
t=1
biΔY2,t−i+
m
t=1
ciΔY3,t−i+
m
t=1
diΔY4,t−i+
m
t=1
eiΔY5,t−1+
ε
t(8)
In Eq. (8) the stock price index series for China and its trading partners, including Australia, Germany, Japan, the UK, and the USA, are
denoted as Y. The long-term equilibrium connection between these ve stock markets is reected in the ξ
′
Zt−1, which encompasses r
cointegrating terms. Granger-causality tests are performed by assessing whether the coefcients of ΔY1,t−i,ΔY2,t−i,ΔY3,t−i,ΔY4,t−i,and
ΔY5,t−i signicantly deviate from zero using an F-test. The signicance of the error correction term is evaluated using a T-test. When
the variables are cointegrated, OLS regression provides “super-consistent” estimates of the cointegrating parameters, as Enders (1995)
indicated. Furthermore, Stock (1987) determines that OLS estimates of parameters converge more rapidly than in OLS models
involving stationary variables.
5. Data preliminaries
This research paper analyzed the stock markets of China, Australia, Japan, Germany, the United States, and the United Kingdom
from January 04, 2012, to December 30, 2022, using publicly available daily data such as stock market indices and nancial reports.
The study looked at how these markets’ cointegration and portfolio diversication were affected by four events that happened after the
global nancial crisis: The Belt and Road Initiative, the Shanghai stock market crash, US-China trade tensions, and the COVID-19
pandemic, with a focus on how these events affected the Chinese stock market. The study used the Shanghai and Shenzhen stock
exchange composite indices as the dependent variable and the AORD (Australia), DAX 30 (Germany), Nikkei 225 (Japan), FTSE 100
(UK), and S&P 500 (USA) indices as the independent variables. The indexes used in this study are denominated in local currency and
were sourced from the database www.econstats.com. After adjusting for certain factors, each country’s total observations amounted to
2266. Furthermore, all the indexes were measured in natural logarithms. The stock prices for each country appear to move together
over time, which is conrmed through cointegration. Source: [41].
Fig. 3 show that the stock prices of all indices are non-stationary and that Chinese markets, such as Shanghai and Shenzhen, tend to
move together over time. In 2014, market capitalization in these markets saw a 150 % increase, making them some of the highest-
performing in the world [134]. However, in July and February of 2015 and 2018, respectively, there were signicant drops in
stock prices. The decline in China’s markets also affected global markets, resulting in a 30 % decrease in Chinese shares since June
2016. Despite these drops, the market has frequently returned to a stable range with government intervention.
5.1. Graphical representation of stationarity
Fig. 3 demonstrates non-stationarity across all data series at the level, while Fig. 4 reveals stationary time series after the rst
difference. This also supports the ADF and PP test results, indicating integration of order (I (1)).
Fig. 3. Stock price indexes for selected countries: January 04, 2012–December 30, 2022.
A. Sher et al.
Heliyon 10 (2024) e29413
11
6. Results and discussions
The descriptive statistics provide a comprehensive exploration of the performance of key stock indices, offering nuanced insights
into the dynamics of Chinese Stock Market Integration with Developed World: A Portfolio Diversication Analysis. Daily returns were
calculated by taking the logarithmic Difference of the indices, as shown in Table 1. Examining the mean returns, it is evident that the
Shenzhen Stock Exchange in China exhibits the highest mean return, signaling a potential area of interest for investors seeking
favorable returns within the context of the broader study. However, the modest mean returns across all indices underscore the need for
a diversied approach to portfolio construction.
Turning to the volatility, as captured by standard deviations, a notable contrast emerges. Developed countries, exemplied by the
USA and Germany, showcase lower volatility in comparison to China’s stock exchanges. This variance in volatility levels underscores
the importance of understanding and managing risk when considering portfolio diversication strategies, especially in the context of
Chinese stocks.
The skewness values provide additional depth to our analysis, revealing distinctive distribution shapes. Australia and the UK
display negative skewness, indicative of distributions skewed towards lower returns. On the contrary, China’s stock exchanges exhibit
negative skewness, suggesting distributions skewed towards higher returns. This skewness pattern suggests potential opportunities and
risks associated with investments in these respective markets, contributing valuable information for investors aiming to diversify their
portfolios.
Delving further into the statistical measures, kurtosis values illuminate the extent of outliers and heavy tails in return distributions.
The diverse kurtosis values across all indices underscore the varying degrees of risk and potential outliers present in each market. This
information is crucial for investors considering the implications of extreme events and tail risks in the context of their portfolio
Fig. 4. Volatility clustering plot of daily returns. (Source: author’s calculations).
Table 1
Descriptive statistics.
Rtn- SSE China Rtn-SZSE China Rtn-Australia Rtn-Germany Rtn-Japan Rtn-UK Rtn-USA
Mean 0.000229 0.000509 0.000235 0.000342 0.000486 0.000145 0.000481
Std Error 0.000288 0.000369 0.000208 0.000271 0.000285 0.000218 0.000242
Median 0.000494 0.001229 0.000748 0.000807 0.00079 0.000631 0.000635
Std Deviation 0.013724 0.017562 0.009891 0.012897 0.013556 0.010368 0.011527
Kurtosis 6.279371 6.202872 10.54584 7.631257 3.790094 9.653318 15.5248
Skewness −0.712960 −1.007209 −0.745004 −0.517863 −0.163192 −0.865092 −0.66447
Minimum −0.084906 −0.126653 −0.095247 −0.122386 −0.079216 −0.108745 −0.11984
Maximum 0.078403 0.072123 0.065611 0.086417 0.080381 0.056598 0.093828
A. Sher et al.
Heliyon 10 (2024) e29413
12
management strategies.
In summary, the descriptive statistics unveil a multifaceted landscape in the integration of the Chinese stock market with the
developed world. The high mean returns in the Shenzhen Stock Exchange captures attention, while the nuanced volatility, skewness,
and kurtosis patterns across different markets provide a holistic understanding of the risks and opportunities associated with portfolio
diversication. These detailed ndings contribute to the overarching objective of our research, guiding investors in formulating
informed decisions within the evolving dynamics of Chinese stock market integration.
To examine cointegration, we rst assess the stationarity of the data series to avoid spurious regression effects. The unit root test,
including the Augmented Dickey-Fuller and Phillips-Perron tests, is employed for this purpose. Table 2 indicates that all series have
unit roots at a level I (0), requiring transformation into stationary series through rst-order differencing (I (1)). Consequently, the daily
closing values of stock price indices become stationary in their rst difference form, enabling the application of the cointegration
procedure. Moreover, the Durbin-Watson statistic for each variable is approximately 2.0, indicating no autocorrelation issues in the
time series data.
When employing the Johansen-Juselius cointegration technique, two potential issues should be considered. Firstly, the test results
may be sensitive to the chosen lag order. Including at least two lags in cointegration analysis with daily stock market data in EViews is
generally recommended. The optimal number of lags can vary based on the data and research question, aiming to conrm uncorrelated
residuals without unnecessary complexity. In this study, ve different criteria were used to determine the optimal lag length, with the
Akaike Information Criteria (AIC) guiding the selection for conducting the cointegration test. Visual inspection of residuals is also
advised to detect correlations or patterns, leading to the inclusion of additional lags if necessary.
The researchers found that using “6
″
lags in the VAR system yielded the most accurate results for all selected equity markets.
Therefore, all further analyses were conducted with “6
″
lags (see Table 3).
The multivariate cointegration test conducted by Johansen (1988) and Johansen and Juselius (1990) reveals one cointegration
between China, Australia, Germany, Japan, the UK, and the US stock markets over the studied period. The analysis employs original
data, considering the long-term relationship’s presence. Both the Trace and Maximum Eigenvalues likelihood ratio tests in Table 4
conrm this result, and different information criteria for lags yield the same outcome of one cointegration vector. These ndings
suggest a relatively low degree of integration among these markets, presenting opportunities for long-term portfolio diversication in
Chinese stock markets for investors from Australia, Germany, Japan, the UK, and the US. Additionally, Chinese investors can manage
risk by investing in these developed stock markets. However, it’s crucial to consider the cointegration test results within the sample
period and account for any economic or political events during that time. Further analysis using different methods and an extended
sample period is recommended to validate these results.
Table 5 part I: The Vector Error Correction Model (VECM) Granger causality test reveals signicant relationships between the
Shanghai Stock Exchange in China and the stock markets of Australia (AORD), Germany (DAX 30), Japan (Nikkei 225), the UK (FTSE
100), and the USA (S&P 500). Notably, AORD, DAX 30, and Nikkei 225 exhibit strong bidirectional causality with Shanghai, indicating
mutual inuence. Additionally, FTSE 100 shows signicant causality, while the S&P 500 does not exhibit a signicant impact, likely
Table 2
Unit root analysis.
Test Variables
Augmented Dickey-Fuller Phillip-Perron
Level 1st Difference Durbin-Watson statistic Level 1st Difference Durbin-Watson statistic
t-statistic t- statistic t- statistic t- statistic
SHANGHAI (CHINA) −2.164087 −49.28640 1.993744 −2.199320 −49.31016 1.993944
SHENZHEN (CHINA) −2.444015 −49.85401 1.997429 −2.574793 −49.99252 1.997429
AORD (AUSTRALIA) −1.342416 −35.79309 1.997665 −1.357988 −54.99936 1.994019
DAX30 (GERMANY) −1.532239 −51.33699 1.999421 −1.555206 −51.33559 1.999421
NIKKEI225 (JAPAN) −1.203697 −34.98652 1.993933 −1.134905 −54.09697 1.995145
FTSE100 (UK) −2.680014 −52.17009 1.999621 −2.606269 −52.24529 1.997902
S&P500 (USA) −0.734322 −35.86244 1.995824 −0.699444 −58.37252 1.981263
The critical values for t-ratio at 1 %, 5 %, and 10 % are −3.432547, −2.862396, and −2.567270, respectively.
Table 3
Optimal lag-lengths of the VAR.
Lag LogL LR FPE AIC SC HQ
0 14722.93 NA 8.94e-14 −13.01807 −13.00288 −13.01253
1 41614.29 53616.21 4.31e-24 −36.77336 −36.66704 −36.73457
2 42125.83 1017.213 2.83e-24 −37.19402 −36.99656* −37.12196
3 42239.35 225.1143 2.64e-24 −37.26258 −36.97400 −37.15727*
4 42309.82 139.3860 2.56e-24 −37.29307 −36.91336 −37.15451
5 42338.04 55.66149 2.58e-24 −37.28619 −36.81534 −37.11437
6 42382.27 87.02530 2.56e-24* −37.29347* −36.73150 −37.08841
7 42404.49 43.58548 2.59e-24 −37.28128 −36.62817 −37.04296
8 42441.54 72.48946* 2.59e-24 −37.28221 −36.53797 −37.01063
* Indicates lag order selected by the criterion
A. Sher et al.
Heliyon 10 (2024) e29413
13
due to differences in regulatory environments, ownership structures, and the ongoing impact of the trade war. These results suggest a
complex yet interconnected relationship between China and these developed markets, with varying degrees of inuence.
Moving to the AORD Australia results, bidirectional causality is observed between AORD and all other markets, including
Shanghai. The signicance levels are generally high, indicating a robust inuence. However, the USA’s S&P 500 displays a relatively
lower signicance level compared to other markets, suggesting a nuanced relationship. Overall, the ndings in Table 1 highlight both
similarities and differences in the causal relationships between Shanghai, AORD, and other major developed markets.
Turning to Table 5 Part II, the Granger Causality Tests for Shenzhen Stock Exchange China demonstrate signicant relationships
Table 4
Johansen Cointegration test.
Shanghai Stock market Unrestricted Cointegration Rank Test (Trace) Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Max-Eigen Statistic 0.05 Critical Value
None
a
0.025125 142.0023*** 117.7082 57.66050*** 44.49720
At most 1 0.014281 84.34185 88.80380 32.59438 38.33101
At most 2 0.011520 51.74747 63.87610 26.25473 32.11832
At most 3 0.006461 25.49273 42.91525 14.68855 25.82321
At most 4 0.003384 10.80419 25.87211 7.681645 19.38704
At most 5 0.001377 3.122542 12.51798 3.122542 12.51798
Shenzhen Stock market Unrestricted Cointegration Rank Test (Trace) Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Max-Eigen Statistic 0.05 Critical Value
None
a
0.023305 140.0135*** 117.7082 53.43483*** 44.49720
At most 1 0.016051 86.57872 88.80380 36.66583 38.33101
At most 2 0.010314 49.91289 63.87610 23.49225 32.11832
At most 3 0.006394 26.42064 42.91525 14.53432 25.82321
At most 4 0.003531 11.88632 25.87211 8.014591 19.38704
At most 5 0.001707 3.871732 12.51798 3.871732 12.51798
**MacKinnon-Haug-Michelis (1999) p-values.
Max-eigenvalue indicates 1 cointegrating eqn(s) at the 0.05.
**MacKinnon-Haug-Michelis (1999) p-values.
* denotes rejection of the hypothesis at the 0.05 level.
* denotes rejection of the hypothesis at the 0.05 leve
Note: *** denotes rejection of null hypothesis at 5 % level of signicance.
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level.
a
denotes rejection of the hypothesis at the 0.05 level.
Table 5
Granger causality tests based on VECM.
VEC Granger Causality/Block Exogeneity Wald Tests
ΔShanghai China ΔAustralia ΔGermany ΔJapan ΔUK ΔUS
Wald X
2
Statistics
ΔShanghai – 0.0021
18.78903***
0.0656
10.36221
0.7511
2.667581
0.5488
4.004568
0.6644
3.231089
ΔAustralia 0.0000
28.54278***
– 0.0738
10.05247
0.8144
2.244484
0.1166
8.816930
0.0005
22.19963***
ΔGermany 0.0001
25.49772***
0.0000
38.76741***
– 0.0000
89.80590***
0.0110
14.86405***
0.0104
14.99161***
ΔJapan 0.0000
30.35006***
0.0000
75.77771***
0.0198
13.40960***
– 0.0024
18.48404***
0.0036
17.50525***
ΔUK 0.0032
17.82507***
0.0000
152.1695***
0.2830
6.246059
0.0383
11.75209***
– 0.0000
47.60979***
ΔUS 0.3797
5.305873
0.0000
55.83943***
0.0000
29.46571***
0.1499
8.117875
0.0000
38.65470***
–
ΔShenzhen – 0.0756
9.986449
0.8907
1.685505
0.3660
5.427376
0.9837
0.686176
0.7960
2.369644
ΔAustralia 0.0371
11.83433***
– 0.0474
11.20752***
0.8184
2.216638
0.1265
8.592545
0.0017
19.31654***
ΔGermany 0.0041
17.21066***
0.0000
38.17678***
– 0.0000
85.03285***
0.0168
13.81295***
0.0055
16.51223***
ΔJapan 0.0009
20.76192***
0.0000
76.43182***
0.0162
13.91608***
– 0.0020
18.88733***
0.0026
18.24943
ΔUK 0.7144
2.906629
0.0000
146.9467***
0.3056
6.006367
0.0375
11.80758***
– 0.0000
45.54983***
ΔUS 0.6535
3.302032
0.0000
51.58269***
0.0000
28.88466***
0.1777
7.632843
0.0000
38.38782***
–
Note: ***,* *and * indicates signicance at the 1, 5 and 10 % levels.
A. Sher et al.
Heliyon 10 (2024) e29413
14
with AORD, DAX 30, Nikkei 225, FTSE 100, and S&P 500. Similar to Shanghai, Shenzhen exhibits bidirectional causality with DAX 30,
Nikkei 225, and FTSE 100, indicating potential mutual inuence. However, the USA’s S&P 500 displays a lower signicance level,
suggesting a less pronounced impact.
In the case of AORD Australia, bidirectional causality is observed with Shenzhen, emphasizing reciprocal inuence. The signi-
cance levels are generally high, but, similar to Table 1, the USA’s S&P 500 shows a relatively lower impact compared to other markets.
The outcomes derived from the Granger Causality Tests utilizing VECM to assess the integration of the Chinese stock market with
developed countries reveal several noteworthy implications. The observed signicant interdependencies between the Shanghai and
Shenzhen stock exchanges and global markets suggest that these Chinese markets are intricately linked to the broader international
nancial landscape rather than being isolated. The consistent causality patterns across both exchanges, particularly with the Australian
and German markets, underscore a coherence that could be advantageous for investors seeking diversied portfolios responsive to
global economic trends.
Furthermore, the variations in signicance levels and causality strength underscore nuanced relationships between the Chinese
exchanges and individual developed countries. Notably, the Japanese market exerts a substantial impact on both exchanges, with
differing signicance levels. Similarly, the inuence of the UK, as measured by the FTSE 100, is more pronounced on the Shanghai
Stock Exchange compared to Shenzhen. These distinctions emphasize the importance of a nuanced approach to portfolio diversi-
cation, considering the specic dynamics between the Chinese markets and each developed country.
The limited observed causality between the Chinese exchanges and the S&P 500 suggests a potential divergence in their responses
to US market movements. This nding could be particularly relevant for investors considering exposure to the Chinese market for
diversication against the backdrop of the US market. In summary, the comparative analysis of the study provides valuable insights for
investors and policymakers, emphasizing the necessity for a nuanced and globally informed approach to decision-making in the
interconnected landscape of international nancial markets.
As indicated by the Granger causality test, the absence of a causal relationship between the US and Chinese stock markets is un-
expected and contradicts intuitive assumptions. Economic theory would posit that events and developments in the US economy should
inuence the Chinese stock market, and vice versa, given the close economic interactions between the two countries.
Several factors may potentially explain this result, including differing regulatory environments, ownership structures, and the
impact of the ongoing trade war between the US and China. These complexities and unique dynamics may contribute to the lack of
observable causality between their stock markets, contrary to conventional economic expectations.
It is crucial to note that the absence of causality between the US and Chinese stock markets does not imply a complete lack of
relationship or correlation; rather, it suggests that changes in one market do not consistently predict changes in the other market over
the examined period. Further research and analysis are necessary to comprehend the factors contributing to this unexpected nding
and assess the potential implications for investors and policymakers.
The relationship between China and Australia, Germany, Japan, the UK, and the USA holds unique signicance. Australia serves as
a crucial supplier of natural resources, while Germany provides opportunities for technology collaboration. Japan stands as a vital
trade partner, and the UK functions as a nancial center for China. Despite existing political tensions, the USA remains a signicant
market for Chinese exports and a source of investment. Collectively, these countries signicantly impact China’s economic growth,
industrial development, and global engagement strategies. Fig. 5 illustrates the Granger Causality Tests Based on VECM, shedding light
on temporal relationships between all the Stock Markets.
Fig. 5. | illustrates the Chinese Stock Market’s links with trade partners using VECM Granger Causality Results (Table 5).
A. Sher et al.
Heliyon 10 (2024) e29413
15
7. Summary and conclusion
In conclusion, our research aimed to shed light on the integration dynamics between the Chinese stock market and major developed
world counterparts: Australia, Germany, Japan, the UK, and the US, with a specic focus on portfolio diversication. The ndings,
derived from a comprehensive analysis of descriptive statistics, unit root tests, cointegration analysis, and Granger Causality Tests
based on VECM, provide valuable insights for investors and policymakers. Analyzing daily data from 2012 to 2022, this study examines
the impact of key events, such as the Belt and Road Initiative, the Shanghai stock market crash, US-China trade tensions, and the
COVID-19 pandemic, on market dynamics and diversication opportunities.
In the rst phase, the descriptive statistics unveiled a multifaceted landscape, emphasizing the need for a diversied approach to
portfolio construction. Notably, the Shenzhen Stock Exchange exhibited the highest mean return, drawing attention as a potential area
of interest for investors seeking favorable returns. The nuanced volatility, skewness, and kurtosis patterns across different markets
underscored the complex risks and opportunities associated with portfolio diversication.
The unit root analysis conrmed the presence of cointegration among the Chinese stock market and major developed world indices.
The optimal lag lengths determined through the VAR system facilitated accurate cointegration tests. The results revealed one coin-
tegration vector, indicating a relatively low degree of integration among these markets. This nding presents opportunities for long-
term portfolio diversication in Chinese stock markets for investors from developed countries, while Chinese investors can manage risk
by investing in these developed stock markets. However, it is essential to acknowledge that while cointegration testing plays a vital role
in analyzing stock market performance and informing investment decisions, practitioners must also consider other critical factors,
including economic variables such as political stability and rm-specic information, to develop a comprehensive strategy. In
conclusion, our ndings align with previous research conducted by Refs. [37,135–137].
In the second phase, Granger Causality Tests based on VECM further illuminated the interconnected relationships between the
Shanghai and Shenzhen stock exchanges with global markets. The bidirectional causality observed with major developed markets,
such as Australia, Germany, and Japan, suggested mutual inuence, providing consistent patterns across both Chinese exchanges.
However, distinctions in the strength and signicance of causality underscored the need for a nuanced approach to portfolio diver-
sication, accounting for specic dynamics between the Chinese markets and each developed country.
The observed limited causality between the Chinese exchanges and the S&P 500 highlighted potential divergence in their responses
to US market movements. This insight is crucial for investors considering exposure to the Chinese market for diversication against the
backdrop of the US market. The study ndings suggest that the Chinese, Japanese, and US economic systems are so intertwined with
the global economic system that any changes in these nancial markets would signicantly affect each other’s stock market. The study
ndings support [137–139].
In essence, our research contributes to a deeper understanding of Chinese stock market dynamics and their implications for global
investors. By emphasizing the importance of nuanced decision-making and a globally informed approach, our ndings empower
stakeholders to navigate the ever-interconnected landscape of international nancial markets effectively. Further research, especially
considering extended sample periods and alternative methodologies, is recommended to validate and build upon these insights.
The interconnection between China’s stock markets and those of developed countries highlights signicant disparities, including
regulations, market structures, and risk levels within a complex framework. Despite strong economic relations, these markets exhibit
variations in returns. Foreign investment plays a vital role in supporting diversication and strengthening integration efforts.
Continual efforts to further open China’s domestic shares will improve transparency and governance and provide valuable insights for
policymakers. Lifting restrictions will enhance the prospects of increased Chinese listings on developed platforms, fostering bilateral
understanding and contributing to a thriving economy.
7.1. Policy implications
Our study presents compelling policy implications to foster international collaboration and enhance investment strategies. Poli-
cymakers should actively encourage cross-border investments, leveraging the identied opportunities for long-term portfolio diver-
sication in Chinese stock markets for investors from developed countries. Initiatives aimed at promoting information sharing and
regulatory cooperation can contribute to market transparency and facilitate a conducive environment for global investors. Moreover,
crafting tailored risk management guidelines based on the nuanced relationships revealed in the study can empower investors to make
more informed decisions in an ever-interconnected nancial landscape.
7.2. Limitations
Despite its valuable insights, our study has certain limitations that should be considered in future research. The ndings are
sensitive to the selected sample period, emphasizing the need for studies that explore the implications of extending the analysis
timeframe to encompass a broader range of market conditions. Additionally, while the methodologies employed are widely accepted,
researchers are encouraged to explore alternative approaches for cointegration and causality analysis to ensure a more robust un-
derstanding. The inuence of economic and political factors, not fully captured in our analysis, should be acknowledged, prompting
future studies to incorporate a broader set of variables for a comprehensive view.
A. Sher et al.
Heliyon 10 (2024) e29413
16
7.3. Future directions
Our study suggests key paths for future research to enhance comprehension of the complex dynamics between Chinese and
developed world stock markets. Extending analysis periods can yield a more comprehensive view of market dynamics, capturing the
inuence of economic cycles, geopolitical events, and policy changes. Researchers are urged to explore alternative methodologies,
fostering comparative studies with diverse statistical techniques. Additionally, integrating macroeconomic factors, such as interest
rates and trade policies, into the analysis can provide a more holistic perspective. In the evolving landscape of nancial markets,
investigating the impact of innovations like blockchain and digital currencies on integration dynamics is essential. In conclusion, our
study not only illuminates the current state of integration but also propels future research, addressing limitations and guiding re-
searchers toward a nuanced understanding. This contributes actionable insights for investors, policymakers, and stakeholders navi-
gating the complexities of global nance.
Data availability statement
The data employed in this research paper have been sourced from publicly available reputable platforms including Bloomberg Inc.
and investing.com, both renowned for their comprehensive and reliable nancial datasets.
CRediT authorship contribution statement
Azmat Sher: Formal analysis. An Haizhong: Project administration. Muhammad Kaleem Khan: Conceptualization. Judit S´
agi:
Investigation.
Declaration of competing interest
The authors declare there is no conict of interest.
Acknowledgement
We gratefully acknowledge the support of the National Natural Science Foundation of China for funding our research under Grant
Nos. 71991481 and 71991480.
References
[1] E. Bouri, S.J.H. Shahzad, D. Roubaud, L. Kristoufek, B. Lucey, Bitcoin, gold, and commodities as safe havens for stocks: new insight through wavelet analysis,
Q. Rev. Econ. Finance 77 (2020) 156–164.
[2] R. Garnaut, L. Song, C. Fang, China’s 40 Years of Reform and Development 1978-2018, ANU Press, 2018.
[3] T.T.L. Chong, X. Li, Understanding the China–US trade war: causes, economic impact, and the worst-case scenario, Economic and Political Studies 7 (2) (2019)
185–202.
[4] W.M. Morrison, China’s economic rise: History, trends, challenges, and implications for the United States, Current Politics and Economics of Northern and
Western Asia 28 (2/3) (2019) 189–242.
[5] P. Bowles, G. White, The Political Economy of China’s Financial Reforms: Finance in Late Development, Routledge, 2019.
[6] L. He, L. Huang, L. Fang, Institutional conditions, economic policy uncertainty and foreign institutional investment in China, Emerg. Mark. Rev. 50 (2022)
100823.
[7] G.T. Chin, K.P. Gallagher, Coordinated credit spaces: the globalization of Chinese development nance, Dev. Change 50 (1) (2019) 245–274.
[8] M.H. Li, L. Cui, J. Lu, Marketized state ownership and foreign expansion of emerging market multinationals: leveraging institutional competitive advantages,
Asia Pac. J. Manag. 34 (2017) 19–46.
[9] M. Kandil, M. Shahbaz, M.K. Mahalik, D.K. Nguyen, The drivers of economic growth in China and India: globalization or nancial development? Int. J. Dev.
Issues 16 (1) (2017) 54–84.
[10] A. Korinek, Regulating capital ows to emerging markets: an externality view, J. Int. Econ. 111 (2018) 61–80.
[11] R. Caferra, D. Vidal-Tom´
as, Who raised from the abyss? A comparison between cryptocurrency and stock market dynamics during the COVID-19 pandemic,
Finance Res. Lett. 43 (2021) 101954.
[12] P. Williamson, De-Globalisation and decoupling: post-COVID-19 myths versus realities, Manag. Organ. Rev. 17 (1) (2021) 29–34.
[13] A.D. Ahmed, R. Huo, Impacts of China’s crash on Asia-Pacic nancial integration: volatility interdependence, information transmission and market
comovement, Econ. Modell. 79 (2019) 28–46.
[14] M.A. Haider, M.A. Khan, S. Saddique, S.H. Hashmi, The impact of stock market performance on foreign portfolio investment in China, Int. J. Econ. Financ.
Issues 7 (2) (2017) 460–468.
[15] W.M. Morrison, China’s economic rise: history, trends, challenges, and implications for the United States: Congressional Washington, DC, 2013. https://
ecommons.cornell.edu/bitstream/handle/1813/77812/CRS_Chinas_economic_rise.pdf?sequence=1.
[16] K.F. Luchtenberg, Q.V. Vu, The 2008 nancial crisis: stock market contagion and its determinants, Res. Int. Bus. Finance 33 (2015) 178–203.
[17] R.H.D. Manzi, Economic globalization in the global post-crisis of 2008: limits and deadlocks, Brazilian Journal of Political Economy 39 (3) (2019) 470–484.
[18] A. Aslund, Why Growth in Emerging Economies Is Likely to Fall, Peterson Institute for International Economics, 2013.
[19] Yayeh, F. A. (n.d.). COVID 19 Impacts on Global Order in Case of Africa Political Economy..
[20] M. Umar, X. Ji, D. Kirikkaleli, Q. Xu, COP21 Roadmap: do innovation, nancial development, and transportation infrastructure matter for environmental
sustainability in China? J. Environ. Manag. 271 (2020) 111026.
[21] G. Song, Z. Xia, M.F. Basheer, S.M.A. Shah, In Comovement dynamics of US and Chinese stock market: evidence from COVID-19 crisis, Economic Research-
Ekonomska Istraˇ
zivanja (2021) 1–17.
[22] K.H. Wang, M. Umar, R. Akram, E. Caglar, Is technological innovation making world" Greener"? An evidence from changing growth story of China, Technol.
Forecast. Soc. Change 165 (2021) 120516.
A. Sher et al.
Heliyon 10 (2024) e29413
17
[23] X. Zhong, Z. Xuechun, C. Yuanyuan, T. Yingwei, W. Tailei, Promoting China’s nancial market reform and innovation with opening policies, The Jingshan
Report: Opening China’s Financial Sector 229 (2020).
[24] S. Chen, S.X. Ni, J.Y. Tong, Impact of recent Chinese stock market liberalization: history and literature review, Int. J. Account. 57 (1) (2022) 2250006.
[25] C. Zhenzhen, P. Xuanming, China’s pivot to global portfolio investments through index inclusion: a new chapter of currency internationalisation. China, Int. J.
19 (2) (2021) 67–87.
[26] K. Siddiqui, One Belt and One Road, China’s massive infrastructure project to boost trade and economy: an overview, Int. Crit. Thought 9 (2) (2019) 214–235.
[27] Z. Liu, S. Schindler, W. Liu, Demystifying Chinese overseas investment in infrastructure: port development, the Belt and Road Initiative and regional
development, J. Transport Geogr. 87 (2020) 102812.
[28] M. Harrison, G. Xiao, China and special drawing rights—towards a better international monetary system, J. Risk Financ. Manag. 12 (2) (2019) 60.
[29] K.E. Chernilevskaya, Internationalization of renminbi as a function of China’s foreign exchange policy, RUDN Journal of Political Science 23 (2) (2021)
233–242.
[30] J. Fang, A. Collins, S. Yao, On the global COVID-19 pandemic and China’s FDI, J. Asian Econ. 74 (2021) 101300.
[31] B.J. Naughton, The Chinese Economy: Adaptation and Growth, Mit Press, 2018.
[32] J. Cui, M. Goh, B. Li, H. Zou, Dynamic dependence and risk connectedness among oil and stock markets: new evidence from time-frequency domain
perspectives, Energy 216 (2021) 119302.
[33] X. Jin, D.D. Li, S. Wu, How will China shape the world economy? China Econ. Rev. 40 (2016) 272–280.
[34] Y. Shi, What inuences stock market comovements between China and its Asia-Pacic trading partners after the Global Financial Crisis? Pac. Basin Finance J.
72 (2022) 101722.
[35] S. Horn, C.M. Reinhart, C. Trebesch, China’s overseas lending, J. Int. Econ. 133 (2021) 103–539.
[36] W. Yang, B. Zhao, The transmission mechanism of China -Japan economic comovement and stabilizing measures for China’s economy, Sage Open 11 (1)
(2021) 21582440211001372.
[37] S. Salahuddin, M. Kashif, M.U. Rehman, Cross-country stock market integration and portfolio diversication opportunities evidence from developed, emerging
and frontier countries, Studies of Applied Economics 39 (2) (2021).
[38] T.D. Nguyen, P. Elisabeta, Financial integration and diversication benets: China and ASEAN4 countries, Manag. Finance 42 (5) (2016) 496–514.
[39] H. Li, Integration versus segmentation in China’s stock market: an analysis of time-varying beta risks, J. Int. Financ. Mark. Inst. Money 25 (2013) 88–105.
[40] J.N. Carpenter, R.F. Whitelaw, The development of China’s stock market and stakes for the global economy, Annual Review of Financial Economics 9 (2017)
233–257.
[41] T. Chang, O. Ranjbar, C. Jooste, Stock market interactions between the BRICS and the United States: evidence from asymmetric granger causality tests in the
frequency domain, Iran. Econ. Rev. 21 (2) (2017) 297–320.
[42] S.J. Yao, H.B. He, S. Chen, J.H. Ou, Financial liberalization and cross-border market integration: evidence from China’s stock market, Int. Rev. Econ. Finance
58 (2018) 220–245.
[43] K. Shimizu, The ASEAN Economic Community and the RCEP in the world economy, Journal of Contemporary East Asia Studies 10 (1) (2021) 1–23.
[44] S.S. Rizavi, B. Naqvi, S.K.A. Rizvi, Global and regional nancial integration of Asian stock markets, Int. J. Bus. Soc. Sci. 2 (9) (2011).
[45] R.C.K. Burdekin, P.L. Siklos, Enter the dragon: interactions between Chinese, US and Asia-Pacic equity markets, 1995–2010, Pac. Basin Finance J. 20 (2012)
521–541.
[46] B.H. Kim, H. Kim, B.S. Lee, Spillover effects of the US nancial crisis on nancial markets in emerging Asian countries, Int. Rev. Econ. Finance 39 (2015)
192–210.
[47] S.W. Kim, Y.M. Kim, M.J. Choi, Asia-Pacic stock market integration: new evidence by incorporating regime changes, Emerg. Mark. Finance Trade 51 (sup4)
(2015) 68–88.
[48] H. Yu, L. Fang, W. Sun, Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market, Phys. Stat. Mech. Appl. 505
(2018) 931–940.
[49] W. Mohti, A. Dionísio, I. Vieira, P. Ferreira, Regional and global integration of Asian stock markets, Res. Int. Bus. Finance 50 (2019) 357–368.
[50] F. Wu, Stock market integration in East and Southeast Asia: the role of global factors, Int. Rev. Financ. Anal. 67 (2020) 101416.
[51] I. Younis, C. Longsheng, M.F. Basheer, A.S. Joyo, Stock market comovements among Asian emerging economies: a wavelet-based approach, PLoS One 15 (10)
(2020) 0240472.
[52] H. Al-Hajieh, Predictive directional measurement volatility spillovers between the US and selected Asian Pacic countries, Cogent Economics & Finance 11 (1)
(2023) 2173124.
[53] C. Jiang, Y. Ma, Y. An, International diversication benets: an investigation from the perspective of Chinese investors, China Finance Rev. Int. 3 (3) (2013)
225–249.
[54] S. Narayan, S. Sriananthakumar, S.Z. Islam, Stock market integration of emerging Asian economies: patterns and causes, Econ. Modell. 39 (2014) 19–31.
[55] M.S. Chien, C.C. Lee, T.C. Hu, H.T. Hu, Dynamic Asian stock market convergence: evidence from dynamic cointegration analysis among China and ASEAN-5,
Econ. Modell. 51 (2015) 84–98.
[56] B.A. Karim, A. Abdul-Rahman, J.Y.T. Hwang, N. Kadri, Portfolio diversication benets of cryptocurrencies and ASEAN-5 stock markets, The Journal of Asian
Finance, Economics and Business 8 (6) (2021) 567–577.
[57] J.A. Batten, H. Kinateder, P.G. Szilagyi, N.F. Wagner, Can stock market investors hedge energy risk? Evidence from Asia, Energy Econ. 66 (2017) 559–570.
[58] F. Aliu, F. Aliu, A. Nuhiu, N. Preniqi, Diversication perspectives of a single equity market: analysis on the example of selected CEE countries. Comparative
Economic Research, Central and Eastern Europe 24 (4) (2021) 85–104.
[59] I.N. Yakubu, A.H. Abokor, I. Gedik Balay, Re-examining the impact of nancial intermediation on economic growth: evidence from Turkey, J. Econ. Dev. 23
(2) (2021) 116–127.
[60] R. Abaidoo, E.K. Agyapong, Financial development and institutional quality among emerging economies, J. Econ. Dev. 24 (3) (2022) 198–216.
[61] G. Gusni, N. Nugraha, Portfolio asset allocation decisions: a meta-analysis, SRIWIJAYA INTERNATIONAL JOURNAL OF DYNAMIC ECONOMICS AND
BUSINESS 4 (2) (2021) 95–102.
[62] S. Kakran, A. Sidhu, P.K. Bajaj, V. Dagar, Novel evidence from APEC countries on stock market integration and volatility spillover: a Diebold and Yilmaz
approach, Cogent Economics & Finance 11 (2) (2023) 2254560.
[63] L. Wallenius, E. Fedorova, S. Ahmed, M. Collan, Surprise effect of euro area macroeconomic announcements on CIVETS stock markets, Prague Econ. Pap. 26
(1) (2017) 55–71.
[64] R. Hass, Stronger: Adapting America’s China Strategy in an Age of Competitive Interdependence, Yale University Press, 2021.
[65] P. Joshi, J. Wang, M. Busler, A study of the machine learning approach and the MGARCH-BEKK model in volatility transmission, J. Risk Financ. Manag. 15 (3)
(2022) 116.
[66] B. Chowdhury, M. Dungey, M. Kangogo, M.A. Sayeed, V. Volkov, The changing network of nancial market linkages: the Asian experience, Int. Rev. Financ.
Anal. 64 (2019) 71–92.
[67] J.H. Claver, B. Dinga, F. Louis, S. Felix, A. Gabriel, Cointegration analysis of major African stock markets, Int. J. 7 (1) (2019) 37–45.
[68] B. Candelon, L. Ferrara, M. Jo¨
ets, Global nancial interconnectedness: a non-linear assessment of the uncertainty channel, Appl. Econ. 53 (25) (2021)
2865–2887.
[69] M. Raddant, D.Y. Kenett, Interconnectedness in the global nancial market, J. Int. Money Finance 110 (2021) 102280.
[70] W.M. Ahmed, On the higher-order moment interdependence of stock and commodity markets: a wavelet coherence analysis, Q. Rev. Econ. Finance 83 (2022)
135–151.
[71] K.U. Ehigiamusoe, H.H. Lean, Do economic and nancial integration stimulate economic growth? A critical survey. Economics 13 (1) (2019) 20190004.
A. Sher et al.
Heliyon 10 (2024) e29413
18
[72] C. Spulbar, J. Trivedi, R. Birau, Investigating abnormal volatility transmission patterns between emerging and developed stock markets: a case study, J. Bus.
Econ. Manag. 21 (6) (2020) 1561–1592.
[73] W. Zhang, Z. Wang, T.S. Adebayo, M. Altuntas
¸, Asymmetric linkages between renewable energy consumption, nancial integration, and ecological
sustainability: moderating role of technology innovation and urbanization, Renew. Energy 197 (2022) 1233–1243.
[74] C. Yang, L. Chen, B. Mo, The spillover effect of international monetary policy on China’s nancial market, Quantitative Finance and Economics 7 (4) (2023)
508–537.
[75] C. Wang, J. Hao, Does the stock market affect the real economy? An empirical analysis based on China’s stock market liquidity, Discrete Dynam Nat. Soc. 2022
(2022).
[76] T.H. Le, L.H. Vo, F. Taghizadeh-Hesary, A study on the Nonlinear Dynamics of ASEAN Financial Integration, J. Asian Bus. Econ. Stud. 31 (1) (2024) 2–14.
[77] D.P.A.D. Abreu, M.A.D. Camargos, A.A. Bressan, The (in) efciency of emerging and developed markets: an analysis from fractal theory, BAR-Brazilian
Administration Review 20 (2023) e220051.
[78] H. Fan, Z. He, L. Chaohui, China’s version of globalisation. www.business-standard.com, 2017, October 14. https://www.business-standard.com/article/
international/china-s-version-of-globalisation-117101400679_1.html.
[79] J. Petry, Same same, but different: varieties of capital markets, Chinese state capitalism and the global nancial order, Compet. Change 25 (5) (2021) 605–630.
[80] M. Akhtaruzzaman, S. Boubaker, A. Sensoy, Financial contagion during COVID–19 crisis, Finance Res. Lett. 38 (2021) 101604.
[81] G. Song, Z. Xia, M.F. Basheer, S.M. Ali Shah, Co-movement dynamics of US and Chinese stock market: evidence from COVID-19 crisis, Economic research-
Ekonomska istraˇ
zivanja 35 (1) (2022) 2460–2476.
[82] Y. Zhong, J. Liu, Correlations and volatility spillovers between China and Southeast Asian stock markets, Q. Rev. Econ. Finance 81 (2021) 57–69.
[83] D.H. Vo, A.T. Vo, C.M. Ho, Does nancial integration enhance economic growth in China? Economies 8 (3) (2020) 65.
[84] T.K. Tuan, Y.S. Hwa, C.S. Yean, Synchronisation of stock market cycles: the importance of emerging and developed markets to ASEAN-5, Prague Econ. Pap. 22
(4) (2013) 435–458.
[85] Q. Huang, M. Li, B. Wang, The dynamic causality between Chinese and ASEAN stock markets, Heliyon 9 (12) (2023) e22975.
[86] L. Wu, Q. Meng, K. Xu, ‘Slow-burn’spillover and ‘fast and furious’ contagion: a study of international stock markets, Quant. Finance 15 (6) (2015) 933–958.
[87] F. Liu, N. Wang, D. Wei, Analysis of Chinese stock market by using the method of visibility graph, Open Cybern. Syst. J. 11 (1) (2017).
[88] S.I. Hussain, S. Li, The dependence structure between Chinese and other major stock markets using extreme values and copulas, Int. Rev. Econ. Finance 56
(2018) 421–437.
[89] M. Hanif, Portfolio selection in Asia/Pacic region-Islamic markets, Journal of Islamic Accounting and Business Research 11 (4) (2020) 905–928.
[90] A. Do, R. Powell, J. Yong, A. Singh, Time-varying asymmetric volatility spillover between global markets and China’s A, B and H-shares using EGARCH and
DCC-EGARCH models, N. Am. J. Econ. Finance 54 (2020) 101096.
[91] Y. Fang, Z. Jing, Y. Shi, Y. Zhao, Financial spillovers and spillbacks: new evidence from China and G7 countries, Econ. Modell. 94 (2021) 184–200.
[92] Y. Song, R. Huang, S.R. Paramati, A. Zakari, Does economic integration lead to nancial market integration in the Asian region? Econ. Anal. Pol. 69 (2021)
366–377.
[93] Y. Jiang, M. Yu, S.M. Hashmi, The nancial crisis and co-movement of global stock markets—a case of six major economies, Sustainability 9 (2) (2017) 260.
[94] H. Liu, A. Manzoor, C. Wang, L. Zhang, Z. Manzoor, The COVID-19 outbreak and affected countries stock markets response, Int. J. Environ. Res. Publ. Health
17 (8) (2020) 2800.
[95] W. Mensi, X.V. Vo, S.H. Kang, Time and frequency connectedness and network across the precious metal and stock markets: evidence from top precious metal
importers and exporters, Resour. Pol. 72 (2021) 102054.
[96] S. Wang, Z. Guo, A study on the co-movement and inuencing factors of stock markets between China and the other G20 members, Int. J. Finance Econ. 25 (1)
(2020) 43–62.
[97] R. Vasilyeva, V. Voytenkov, A. Urazbaeva, Dynamic linkages between stock markets: evidence from USA, Germany, China and Russia, Proceedings of CBU in
Economics and Business 2 (2021) 95–101.
[98] M.U. Rehman, N. Ahmad, S.J.H. Shahzad, X.V. Vo, Dependence dynamics of stock markets during COVID-19, Emerg. Mark. Rev. 51 (2022) 100894.
[99] Ibrahim, B. A., Elamer, A. A., Alasker, T. H., Mohamed, M. A., & Abdou, H. A. Volatility Contagion between Cryptocurrencies, Gold and Stock Markets Pre-and-
during COVID-19: Evidence Using DCC-GARCH and Cascade-Correlation Network..
[100] X. Zhou, W. Zhang, J. Zhang, Volatility spillovers between the Chinese and world equity markets, Pac. Basin Finance J. 20 (2) (2012) 247–270.
[101] I.C. Tsai, The source of global stock market risk: a viewpoint of economic policy uncertainty, Econ. Modell. 60 (2017) 122–131.
[102] A.S. Khoojine, Z. Feng, M. Shadabfar, N.S. Khoojine, Analyzing volatility patterns in the Chinese stock market using partial mutual information-based
distances, Eur. Phys. J. B 96 (12) (2023) 165.
[103] F. Guidi, C.S. Savva, M. Ugur, Dynamic co-movements and diversication benets: the case of the Greater China region, the UK and the US equity markets,
J. Multinatl. Financ. Manag. 35 (2016) 59–78.
[104] F. Qin, J. Zhang, Z. Zhang, RMB exchange rates and volatility spillover across nancial markets in China and Japan, Risks 6 (4) (2018) 120.
[105] O. Aladesanmi, F. Casalin, H. Metcalf, Stock market integration between the UK and the US: evidence over eight decades, Global Finance J. 41 (2019) 32–43.
[106] M. Hanif, A. Sabah, Stock markets’ integration in post nancial crisis era: evidence from literature, Capital Market Review 28 (2) (2020).
[107] S. Jayech, L.J. Mazigh, E. Abdennadher, Stock market interdependence, contagion, the nancial subprime crisis and the European sovereign debt crisis:
evidence from the Chinese’s stock market, Asian Acad. Manag. J. Account. Finance 18 (2) (2022) 109–138.
[108] X.M. Li, L. Peng, US economic policy uncertainty and co-movements between Chinese and US stock markets, Econ. Modell. 61 (2017) 27–39.
[109] F. Pan, C. Fang, Mapping global nancial networks: a spatial analysis of Chinese companies’ overseas listings, Transactions in Planning and Urban Research 1
(1–2) (2022) 32–49.
[110] I. Ghosh, M.K. Sanyal, R.K. Jana, Co-movement and dynamic correlation of nancial and energy markets: an integrated framework of nonlinear dynamics,
wavelet analysis and DCC-GARCH, Comput. Econ. 57 (2021) 503–527.
[111] T.T. Van Anh, D.H. Tuan, N.T. Nhung, Interdependence of stock markets: evidence from Vietnam, Journal of International Economics and Management 22 (3)
(2022) 110–128.
[112] N.A. Bakar, S. Rosbi, Robust statistical portfolio investment in modern portfolio theory: a case study of two stocks combination in kuala lumpur stock
exchange, Int. J. Eng. Adv. Technol. 8 (2019) 214–221.
[113] F. Mathlouthi, S. Bahloul, Co-movement and causal relationships between conventional and Islamic stock market returns under regime-switching framework,
Journal of Capital Markets Studies 6 (2) (2022) 166–184.
[114] W. Cao, W. Zhu, Y. Demazeau, Multi-layer coupled hidden Markov model for cross-market behavior analysis and trend forecasting, IEEE Access 7 (2019)
158563–158574.
[115] Z. Wang, Z. Zhang, Q. Zhang, J. Gao, W. Lin, COVID-19 and nancial market response in China: micro evidence and possible mechanisms, PLoS One 16 (9)
(2021) e0256879.
[116] Z. Liao, H. Zhang, K. Guo, N. Wu, A network approach to the study of the dynamics of risk spillover in China’s bond market, Entropy 23 (7) (2021) 920.
[117] H. Niu, K. Xu, M. Xiong, The risk contagion between Chinese and mature stock markets: evidence from a markov-switching mixed-clayton copula model,
Entropy 25 (4) (2023) 619.
[118] A.W.W. Cheng, N.S.C. Chow, D.K.H. Chui, W.K. Wong, The three musketeers relationships between Hong Kong, Shanghai and Shenzhen before and after
Shanghai–Hong Kong stock connect, Sustainability 11 (14) (2019) 3845.
[119] M. Akbar, A. Akbar, M.A. Qureshi, P. Poulova, Sentiments–risk relationship across the corporate life cycle: evidence from an emerging market, Economies 9 (3)
(2021) 111.
[120] A.K. Mishra, S. Agrawal, J.A. Patwa, Return and volatility spillover between India and leading Asian and global equity markets: an empirical analysis, Journal
of Economics, Finance and Administrative Science 27 (54) (2022) 294–312.
A. Sher et al.
Heliyon 10 (2024) e29413
19
[121] W. Xie, J. Paulo Vieito, E. Clark, W.K. Wong, Could mergers become more sustainable? A study of the stock exchange mergers of NASDAQ and OMX,
Sustainability 12 (20) (2020) 8581.
[122] A. Zaimovic, A. Omanovic, A. Arnaut-Berilo, How many stocks are sufcient for equity portfolio diversication? A review of the literature, J. Risk Financ.
Manag. 14 (11) (2021) 551.
[123] E. L´
opez Zapata, F.E. García Mui˜
na, S.M. García, Analysing the Relationship between Diversication Strategy and Firm Performance: the Role of the Economic
Cycle, 2019.
[124] M. Hosen, M. Imran, M.A.F. Chowdhury, Nexus between sectoral shift and stock return: insights from Bangladesh, Int. J. Asian Bus. Inf. Manag. 12 (1) (2021)
75–93.
[125] H. Zhang, J. Chen, L. Shao, Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19, Int. Rev. Financ. Anal. 77
(2021) 101828.
[126] C. Anstey, US-China Rivalry Will Shape 2023 for the Whole World: New Economy Saturday, 2022, December 31. Bloomberg.com, https://www.bloomberg.
com/news/newsletters/2022-12-31/us-china-rivalry-will-shape-2023-for-the-whole-world-new-economy-saturday.
[127] R. Salles, K. Belloze, F. Porto, P.H. Gonzalez, E. Ogasawara, Nonstationary time series transformation methods: an experimental review, Knowl. Base Syst. 164
(2019) 274–291.
[128] Q. Shao, X. Wang, Q. Zhou, L. Balogh, Pollution haven hypothesis revisited: a comparison of the BRICS and MINT countries based on VECM approach, J. Clean.
Prod. 227 (2019) 724–738.
[129] M.E. Hoq, Co-integration approach: relationship between Asia-Pacic and USA stock markets during the nancial crisis of 2008? An empirical study, AIUB
Journal of Business and Economics 17 (1) (2020) 109–128.
[130] M. Rizwanullah, L. Liang, X. Yu, J. Zhou, M. Nasrullah, M.U. Ali, Exploring the cointegration relation among top eight Asian Stock Markets, Open J. Bus.
Manag. 8 (3) (2020) 1076.
[131] A.G. Khan, M.A. Hossain, S. Chen, Do nancial development, trade openness, economic development, and energy consumption affect carbon emissions for an
emerging country? Environ. Sci. Pollut. Control Ser. 28 (2021) 42150–42160.
[132] B. Rossi, Y. Wang, Vector autoregressive-based Granger causality test in the presence of instabilities, STATA J. 19 (4) (2019) 883–899.
[133] A. Cipollini, I. Mikaliunaite, Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR, Empir. Econ. 61
(2) (2021) 855–881.
[134] T.B. Lee, China’s stock market crash, explained in charts, Vox (2015, August 26). https://www.vox.com/2015/7/8/8911519/china-stock-market-charts.
[135] O.M. Al Nasser, M. Hajilee, Integration of emerging stock markets with global stock markets, Res. Int. Bus. Finance 36 (2016) 1–12.
[136] A. Bhunia, D. Yaman, Is there a causal relationship between nancial markets in Asia and the US? The Lahore Journal of Economics 22 (1) (2017) 71–90.
[137] G.M. Caporale, L.A. Gil-Alana, K. You, Stock market linkages between the ASEAN countries, China and the US: a fractional integration/cointegration approach,
Emerg. Mark. Finance Trade 58 (5) (2022) 1502–1514.
[138] G.X. Xu, W.F. Gao, Financial risk contagion in stock markets: causality and measurement aspects, Sustainability 11 (5) (2019) 1402.
[139] Q. Ying, T. Yousaf, Q.U. Ain, Y. Akhtar, Investor psychology, mood variations, and sustainable cross-sectional returns: a Chinese case study on investing in
illiquid stocks on a specic day of the week, Front. Psychol. 11 (2020) 173.
A. Sher et al.
