Based on daily returns, we comprehensively characterize the lead-lag relationship between Brent and WTI crude oil spot markets from 1987 to 2017 with the non-parametric symmetric thermal optimal path (TOPS) method. The empirical results indicate that WTI spot price leads Brent spot price slightly, which provides support to the price leadership of WTI over Brent. However, the lead-lag relationship is volatile and sensitive to extreme events like geopolitical conflict and policy shift. Due to the concerns about future oil supply triggered by the two Gulf wars, both WTI and Brent experienced extreme uncertainty and co-moved closely during wartime. Notably, the TOPS method captures the structural break in the WTI-Brent price spread in 2011 which is influenced by the U.S. oil export ban and transportation bottleneck. After the lift of the ban, the two benchmark prices have reconnected. The lead-lag signals basically coincide with major influential changes in the oil markets, which suggests that the TOPS method provides a viable approach to reflecting the impact of extreme events on the crude oil prices motion.
This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newlylaunched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.
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