Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting

Highlights: • Apply the Markov switching analysis to uncover the regime switching of the INE crude oil futures market. • Investigate the dynamic connectedness of INE, WTI, Brent crude oil futures. • Forecast the realized volatility of INE crude oil futures. • The outbreak of COVID-19 pandemic has switched the crude oil futures market from a stable to a volatile regime. • Highly recommend forecasting volatility by incorporating intraday realized meaures into MIDAS approach. The launch of the China's Shanghai International Energy Exchange (INE) oil futures market in 2018 has shed new light on the role of China in international crude oil market. Understanding the dynamics of the newly arrived RMB denominated crude oil futures market not only facilitates the international market participants in risk management and hedging, but also provides helpful information for policy-makers, especially for those from emerging countries, to financialize its energy market, along with the currency internationalization and financial market liberalization. However, the literature on the China crude oil futures is quite scant compared with abundant literature on international benchmarks. In this regard, we make attempts to capture the dynamics of the China crude oil futures by: (1) adopting Markov switching analysis to uncover the regime switching of the INE crude oil futures market; (2) investigating the dynamic connectedness of INE, WTI, and Brent crude oil futures; (3) forecasting the realized volatility of INE crude oil futures. The results have shown that: (1) the outbreak of global pandemic at the beginning of 2020 has switched the crude oil futures market from a stable regime to a volatile regime; (2) the increasing financial uncertainty originated from the world, U.S., other advanced countries and emerging countries could significantly negatively affect the movement of crude oil futures. However, China suffers the least; (3) the dynamic conditional correlations between INE vs WTI, and INE vs Brent are high but lower and more volatile than that of WTI vs Brent; (4) Brent crude oil futures contribute to improving the accuracy of volatility forecasting of INE crude oil futures; and more importantly (5) we highly recommend carrying out volatility forecasting by incorporating intraday realized measures into mixed data sampling approach, in particular, when intraday transactions present extremely different behavior across the time.