Abstract
Connectedness is the key to modern risk measurement and management. This study investigates the international connectedness of crude oil prices and explores its time-varying characteristics based on a connectedness measurement framework using daily international crude oil prices. The international connectedness of crude oil prices is investigated from three perspectives: total connectedness, total directional connectedness, and pairwise directional connectedness. We find that the total connectedness of crude oil prices is 67.3%. We also find that the crude oil prices of Tapes, Daqing, Dubai and Minas are highly affected by Brent and WTI (West Texas Intermediate) crude oil prices. Furthermore, WTI and Brent are the price makers of international crude oil prices, while Tapes, Daqing, Dubai and Minas are price takers. From the perspective of pairwise directional connectedness, we find that the degree of pairwise directional connectedness between Brent and WTI are high. Finally, the structure of international crude oil markets stays the same even after market shocks. The main contributions of this study are identification of dynamic connectedness and presentation of the network connectedness of international crude oil prices.
Highlights
Over the years, international crude oil prices have experienced sharp rises and falls and have attracted extensive attention from policymakers, researchers, and investors [1]
Six of the international crude oil prices were at the bottom in August 2011, June 2012 and March 2013, while they peaked in February 2012, August 2012 and September 2013
The connectedness measures are based on the “non-own” or “cross”, which means that we focus on the off-diagonal entries of the variance decomposition matrix DH
Summary
International crude oil prices have experienced sharp rises and falls and have attracted extensive attention from policymakers, researchers, and investors [1]. The directions of connectedness of international crude oil prices are as important as its levels for risk measurement and control of crude oil markets. To address these problems, Diebold and Yilmaz developed and applied a unified framework for conceptualizing and empirically measuring connectedness at various levels, from pairwise through system-wide [20]. Based on assessing shares of forecast error variation in different crude oil prices due to shocks arising elsewhere, this method measures the levels of connectedness and identifies the directions of connectedness [21].
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