Empirical Evidence of Co-Movements between China and Global Stock Markets

Chiang, Thomas C.; Lao, Lan-Jun; Xue, Qingfeng

doi:10.2139/ssrn.2311706

Cited by 2 publications

(3 citation statements)

References 36 publications

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“…Then, the equation [15]: Figure 4 demonstrates that the movement of Chinese stock market can be approximated by fractional Brownian motion [12,14]. The next thing that must be conducted is to analyse the possible oscillations of fBM to have approximate range.…”

Section: Methodology and Modellingmentioning

confidence: 99%

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Analysing the chinese stock market using the hurst exponent, fractional brownian motion and variants of a stochastic logistic differential equation

Vukovic¹

2015

Int. J. DNE

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The Chinese stock market is rapidly developing and is becoming one of the wealth management investment centres. Recent legislation has allowed wealth management products to be invested in the Chinese stock market. By taking data from St. Louis Fed and analysing the Chinese stock market using the Hurst exponent, which was calculated by using two methods, and fractional Brownian motion, it is proved that the Chinese stock market is not efficient. However, further analysis was directed to finding its equilibrium state by using logistic difference and a differential equation. To achieve more precise movement, a stochastic logistic and delayed logistic differential equation have been implemented which are driven by fractional Brownian motion. As there is no explicit solution to a delayed differential equation using the Stratonovich integral, a method of steps has been used. A solution has been obtained that gives the equilibrium state of the Chinese stock market. The following solution is only for a Hurst exponent that is higher than 1/2. If Hurst exponent is close to 1/2, then Brownian motion is an ordinary one and the equilibrium solution is different. Sensitivity analysis in that case has been conducted in order to analyse possible stationary states. The main conclusion is that the Chinese stock market is still not mature enough to be efficient, which represents a hidden risk in wealth management investing. A new approach to valuing the efficiency of the market by using nonlinear dynamics and system analysis has been proposed.

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Section: Methodology and Modellingmentioning

confidence: 99%

“…Results obtained are very interesting as they mimic the movement of Shanghai stock index. The attention should be directed to dynamic hedging as X 0 is constantly changing [12]. To find and apply the following equations, first the uprising trend must be noticed.…”

Section: Methodology and Modellingmentioning

confidence: 99%

Analysing the chinese stock market using the hurst exponent, fractional brownian motion and variants of a stochastic logistic differential equation

Vukovic¹

2015

Int. J. DNE

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“…Chiang's [16] statistical analysis shows a significant change in the correlation of stock returns in various countries after China joined the WTO, so the research period of this paper is set between January 1, 2002 and December 31, 2018. Using the SSE Composite Index and S&P 500 Index to represent the stock markets of China and US, with data from Yahoo Finance, the daily rate of return is calculated as r t =100×(lnP t − lnP t-1 ).…”

Section: Methodsmentioning

confidence: 99%

The Impact of Financial Liberalization and Trade Dependence on Stock Market Linkages between China and the US

Tsai¹,

Wang²,

Huang³

2019

dtem

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Economic globalization is gradually strengthening linkages between capital markets in various countries. The United States and China, as the world's first and second largest economy, we study the linkages between their stock markets and provide significant implications for national regulators and investors. This study selects the Shanghai Stock Exchange Composite Index and the Standard & Poor's 500 Index to respectively represent the stock markets in China and the United States. The DCC-GARCH model is used to capture the dynamic correlation coefficient between the stock markets. The multiple regression analysis and the VAR model are used to explore financial liberalization and trade dependence, and to investigate their impact on the linkages between Chinese and US stock markets. This study found that because China's overall financial liberalization is low, it has an inhibiting effect on the linkages between Chinese and US stock markets. The closer the trade links between China and the US, the stronger the linkages between their stock markets. Three macroeconomic indicators, GDP growth rate difference, CPI difference, and interest rate difference are not good explanations for linkage changes between the stock markets.

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Empirical Evidence of Co-Movements between China and Global Stock Markets

Cited by 2 publications

References 36 publications

Analysing the chinese stock market using the hurst exponent, fractional brownian motion and variants of a stochastic logistic differential equation

Analysing the chinese stock market using the hurst exponent, fractional brownian motion and variants of a stochastic logistic differential equation

The Impact of Financial Liberalization and Trade Dependence on Stock Market Linkages between China and the US

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