Research on differences of spillover effects between international crude oil price and stock markets in China and America

2017 ◽  
Vol 88 (1) ◽  
pp. 575-590 ◽  
Author(s):  
Zhenhua Liu ◽  
Zhihua Ding ◽  
Rui Li ◽  
Xin Jiang ◽  
JyS. Wu ◽  
...  
Kybernetes ◽  
2018 ◽  
Vol 47 (6) ◽  
pp. 1242-1261 ◽  
Author(s):  
Can Zhong Yao ◽  
Peng Cheng Kuang ◽  
Ji Nan Lin

Purpose The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets. Design/methodology/approach The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method. Findings The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices. Originality/value China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.


2018 ◽  
Vol 54 (8) ◽  
pp. 1706-1719 ◽  
Author(s):  
Durmuş Çağrı Yıldırım ◽  
Seyfettin Erdoğan ◽  
Emrah İsmail Çevik

Significance US President Donald Trump’s decision last month to intensify the US-China conflict by raising the tariff rate and targeting Chinese tech firms is straining stock markets and making government bonds more attractive. Marking a dangerous new phase, sentiment towards the tech sector is deteriorating, after powering the stock market 'bull run' for a decade. Impacts Uncertainty over both US policy and geopolitics globally will continue to make the dollar more attractive, outweighing Fed dovishness. Emerging markets enjoyed a surge in inflows from January-April 2019, but suffered sharp outflows in May, and investors will remain cautious. The VIX Index, Wall Street’s so-called ‘fear gauge’, has surged by around 50% since May 3, and is likely to remain elevated. Rising US output means that the Brent crude oil price is likely to stabilise rather than rebound, having fallen by about 20% since April.


2017 ◽  
Vol 12 (2) ◽  
pp. 352-365 ◽  
Author(s):  
Bhaskar Bagchi

Purpose The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times. Design/methodology/approach The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects. Findings For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries. Originality/value The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.


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