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This paper investigates the impact of the US-China Trade War on co-movements between US and Chinese stock markets. It particularly examines the time-varying stock market co-movement between the United States and China at market level, as well as at sector level, over the period from 3rd January 2017 to 23rd January 2020. The ‘event study’ analysis...
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Citations
... Their empirical results show that firms with higher export exposure to the US have stronger negative market reactions, especially among non-state-owned enterprises. Shi et al (2021) investigate time-varying stock market co-movement between the US and China at both market and sector level over the period from 3 January 2017 to 23 January 2020. The study finds that the co-movements between Mainland China, Hong Kong and US stock markets are positively affected by news releases, and the co-movements are significantly enhanced after the official outbreak of the US-China trade war. ...
... The average asset risk is higher in the years preceding the GFC and lower in the following years. This variable also presents two peaks in 2016 and 2020, which may be attributed to a set of events that affected the global economy, including Brexit (Quaye et al., 2016), the Shanghai market crash and US-China tariff war (Shi et al., 2021) in 2016, and COVID-19 pandemic (Ganie et al., 2022;Shi, 2022) during 2020. ...
This study examines whether forcing banks to hold subordinated debt and enforcing market discipline could enhance the effectiveness of capital macroprudential policies in reducing banks' risk and contribute to bank stability. Using the system generalised method of moments and based on a sample of 322 banks across 18 countries during the period 2006–2020, we find that a higher level of subordinated debt leads banks to avoid moral-hazard behaviours and engage in risk shifting when adapting to a tighter macroprudential framework, which in turn leads to a greater effectiveness of these policies. Furthermore, as robustness tests, we show that this effect is stronger in advanced economies and in the United States of America. These results also stand using a different proxy for banks' risk.
... Since the 19th century, all countries' capital markets, especially stock markets, have repeatedly experienced a long bear market after the bull market plunged, and then the bull market and the bear market alternated with each other. [16] After the outbreak of the financial crisis, financial indicators deteriorated rapidly, and market investors began to lose confidence in the financial system and reduce their financial assets. Then, financial market investors continued to be bearish on future expectations and continued to sell off financial assets. ...
... The U.S. securities market has finally reached the position of the largest stock market in the world today through a "problem-solving" thinking mode. 16.09% of China's export market in the whole year, and total imports from the United States accounted for 6.91% of China's total import market in the whole year; The total amount of US exports to China accounts for 5.13% of the annual US export market, and the total amount of imports from China accounts for 17.4% of the annual US import market. In 2015, the United States exported $116.2 billion of goods to China, 30 times that of 1985. ...
The relationship between China and the United States has undergone great changes, from cooperation to competition or even confrontation. It is of great significance to analyze the relationship between them from the perspective of neo-realism. With the deepening of global financial integration and the gradual opening up, the volatility transmission among stock markets in various countries has become more and more obvious. The stock market has become an important part of China's financial system, which is of great significance to improve the efficiency of capital operation and reduce the financing cost of enterprises. And the United States and China have formulated their stock market policies based on neo-realism. The United States needs to face up to China's economic strength and safeguard its interests through global financial hegemony. China needs to face up to the reality and enhance the global financial influence in order to safeguard its stock market.
... 5 Moreover, the trade war between the United States and China also creates uncertainty and affects the movements of the stock markets in the two countries as indicated by the joint movements recorded in mainland China, Hong Kong, and the United States after the release of the United States-China trade news. 6,7 The identification of uncertainty shocks is highly dependent on event constraints, 8 and global sentiment has been discovered to have a huge impact and ability to create uncertainty in the world's economy, thereby leading to the collapse of several countries' economic sectors. The condition usually creates fear and doubts among investors in relation to their investment activities and also makes it difficult to identify the best decision. ...
The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.
... Hence, this study aims to comprehensively analyze the dynamics of stock market order transitions on high and low volatility days during the USA-China trade war across six sectors in the USA: Energy, Finance & Banking, FMCG, Healthcare, IT, and Real Estate. We have chosen these sectors as these sectors were impacted by the trade war [22][23][24] In the stock market, different types of orders, as shown in Table VI of the Appendix, transition from one to another at an extremely rapid pace, even in microseconds 28 . A sequence of categorical data is formed by the transition between different types of orders. ...
Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the US–China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test. We estimate the transition probability matrix of the sequence using maximum likelihood estimation. From the heatmap of these matrices, we found the presence of active participation by different types of traders during high volatility days. On such days, these traders place limit orders primarily with the intention of deleting the majority of them to influence the market. These findings are supported by high stationary distribution and low mean recurrence values of add and delete orders. Further, we found similar spectral gap and entropy rate values, which indicates that similar trading strategies are employed on both high and low volatility days during the trade war. Among all the sectors considered in this study, we observe that there is a recurring pattern of full execution orders in the Finance & Banking sector. This shows that the banking stocks are resilient during the trade war. Hence, this study may be useful in understanding stock market order dynamics and devise trading strategies accordingly on high and low volatility days during extreme macroeconomic events.
... Although some studies can be traced back to the period when the United States executed any mission in a war-torn country such as Iraq (Amihud and Wohl, 2004;Williams, 2006;Wolfers and Zitzewitz, 2009). Recently, some researchers have examined the impacts of the US-China trade war (a non-arm geopolitics tussle) (Carlomagno and Albagli, 2022;Shi et al., 2021;Wang et al., 2021). ...
... Although some studies can be traced back to the period when the United States executed any mission in a war-torn country such as Iraq (Amihud and Wohl, 2004;Williams, 2006;Wolfers and Zitzewitz, 2009). Recently, some researchers have examined the impacts of the US-China trade war (a non-arm geopolitics tussle) (Carlomagno and Albagli, 2022;Shi et al., 2021;Wang et al., 2021). ...
... Furthermore, in the years following the GFC, we can observe two peaks in 2016 (7.00%) and in 2020 (9.85%). The high volatility in these years can be attributed to a series of shocks that hit global financial markets, namely Brexit (Quaye et al., 2016), the Shanghai market crash and the US-China tariff war (Shi et al., 2021) in 2016, and the COVID-19 pandemic in 2020 (Ganie et al., 2022;Matos et al., 2023a;Shi, 2022). ...
This paper analyzes whether the effect of macroprudential policies on bank risk is channeled through investors' protection using panel data from a sample of 624 banks from 40 countries. We show that investors' protection plays a significant role in the effect of macroprudential policies on bank risk, which translates into higher efficiency for macroprudential policies in countries with highly protected creditors, whereas there is a loss of
effectiveness in countries where shareholders are highly protected. We provide further evidence that the impact of loosening a macroprudential policy is asymmetric (and more pronounced) compared with the adoption or tightening of a macroprudential policy.
... In contrast, the scenario is positive in Canada, UK, Australia, Portugal and Colombia and this is similar to the findings of Shi et al. (2021). When a war begins, the prices of shares go down due to fear and cash requirements of investors. ...
This study measures the immediate impact of Russia–Ukraine war on the global stock markets for the first four months since Russia’s first invasion attempt on February 24, 2022. Daily closing stock indices have been used from selected stock markets of six different continents. By applying event study method, it observes mixed impact on different stock markets. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH 1,1) indicates the presence of significant volatility and leverage effect in all the markets. Regression estimates show significantly positive impact of VIX and negative impact of oil on the abnormal returns of the global stock markets. Diversifying energy supply and source, accelerating deployment of renewables and promoting electronic vehicles and machines might bring positive result for the financial market. It is expected that this research will provide policymakers, regulatory authorities, investors and all concerned stakeholders a precise guideline to handle the immediate impact of war on the stock prices and to formulate appropriate strategies to keep investment free from risk and uncertainties.
... Furthermore, fluctuating total, net, and net-pairwise connectedness indices are time-variant and vulnerable to significant crisis events such as the China-US trade dispute, the COVID-19 epidemic, and the Russian-Ukrainian war. This finding has also been confirmed in the studies of Shi et al. (2021), Anwer et al. (2022), Iqbal et al. (2022), Apergis (2023), Maghyereh (2023a, 2023b), and Lee et al. (2023). ...
