Modelling dependency structures of crude oil prices and stock markets of developed and developing countries: A C-vine copula approach

Geopolitical factors are considered a crucial factor that makes a difference in crude oil prices. Over the last three decades, many political events occurred frequently, causing short-term fluctuations in crude oil prices. This paper aims to examine the dynamic correlation and causal link between geopolitical factors and crude oil prices based on data from June 1987 to February 2020. By using a time-varying copula approach, it is shown that the correlation between geopolitical factors and crude oil prices is strong during periods of political tensions. The GPA (geopolitical acts) index, as the real factor, drives the rise in prices of crude oil. Moreover, the dynamic correlation between geopolitical factors and crude oil prices shows strong volatility over time during periods of political tensions. We also found unidirectional causality running from geopolitical factors to crude oil prices by using the Granger causality test.

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Dynamic Linkages between Crude Oil Prices, Exchange Rates, and Stock Markets of Other Emerging Economies

Dynamic Linkages and Volatility Spillover ◽

10.1108/978-1-78635-554-620161006 ◽

2016 ◽

pp. 111-141

Keyword(s):

Exchange Rates ◽

Crude Oil ◽

Emerging Economies ◽

Stock Markets ◽

Oil Prices ◽

Crude Oil Prices ◽

Dynamic Linkages

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The optimal thermal causal path analysis on the relationship between international crude oil price and stock market

Kybernetes ◽

10.1108/k-04-2017-0139 ◽

2018 ◽

Vol 47 (6) ◽

pp. 1242-1261 ◽

Cited By ~ 2

Author(s):

Can Zhong Yao ◽

Peng Cheng Kuang ◽

Ji Nan Lin

Keyword(s):

Stock Market ◽

Crude Oil ◽

Stock Markets ◽

Oil Prices ◽

Oil Price ◽

Chinese Stock Market ◽

Crude Oil Price ◽

Content Type ◽

Crude Oil Prices ◽

Causal Path

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.

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