Dynamic Spillovers Between International Crude Oil Market and China's Commodity Sectors: Evidence From Time-Frequency Perspective of Stochastic Volatility

Zhenghui Li,Yaya Su

doi:10.3389/fenrg.2020.00045

Abstract

We study the time-frequency dynamics of stochastic volatility spillovers between international crude oil markets and China’s commodity sectors in the spectral representation framework of generalized forecast error variance decomposition (GFEVD). We find evidence that international crude oil markets has significant volatility spillover effects on China's bulk commodity markets, and the volatility spillovers are sensitive to extreme geopolitical or financial events. The net spillovers of international oil markets are almost positive and driven mainly by short-term components (within a week). However, uncertain financial factors from China such as the market-oriented reform in 2013 and the stock disaster in 2015 adversely affect the net oil-commodity volatility spillovers through the medium-term components (week to a month) and long-term components (month to a year). Moreover, the volatility spillover effects of crude oil prices on different commodity sectors in China are heterogeneous. Metal, coal coke and steel ore and energy commodity sectors are more affected by crude oil prices, while non-metal building materials and agricultural commodities are less affected. These outcomes implement necessary implications for investors and policymakers.

Highlights

IntroductionThe linkages between oil and other commodity prices (including metals, industries, and agriculture) have increased (Ji and Fan, 2012; Wang et al, 2014; Luo and Ji, 2018) owing to the “financialization of commodities” (Vivian and Wohar, 2012; Creti et al, 2013; Fattouh et al, 2013; Adams and Glück, 2015; Basak and Pavlov, 2016; Liu K. et al, 2019; Liu Y. et al, 2019)
We mainly focus on the time-frequency dynamic spillovers among crude oil prices and China’s bulk commodity sectors
Because the static results of the generalized forecast error variance decomposition (GFEVD) framework over the entire sampling period may smooth out the results when the relationship between the variables changes over time (Lovcha and Perez-Laborda, 2020), this paper considers both the static and dynamic spillover effects to obtain more comprehensive estimations

Summary

Introduction

The linkages between oil and other commodity prices (including metals, industries, and agriculture) have increased (Ji and Fan, 2012; Wang et al, 2014; Luo and Ji, 2018) owing to the “financialization of commodities” (Vivian and Wohar, 2012; Creti et al, 2013; Fattouh et al, 2013; Adams and Glück, 2015; Basak and Pavlov, 2016; Liu K. et al, 2019; Liu Y. et al, 2019). In China, the world’s largest oil consumption economy, crude oil consumption has been increasing significantly. In 2018, China’s annual oil import volume reached 462 million tons, whereas domestic oil production is only 189 million tons. As early as 2013, the United States Energy Information Administration (EIA) has announced that China is the largest net importer of crude oil in the world economy. China has maintained high economic growth rates in the past 40 years since reform and opening-up. The considerable energy demand brought about by the rapid growth of China’s economy has affected the world’s energy and financial markets, attracting widespread attention from international investors. Research on the volatility spillovers between international crude oil and China’s commodity markets is of paramount importance for both policymakers and financial market participants

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