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Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China

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  • Guo, Yanfeng
  • Zhao, Huanyu

Abstract

In the structure of energy consumption of China, coal and oil rank the top and the second place. It is obviously important to better understand the dynamics of the price volatility between crude oil and coal in China, and then to build a reasonable energy portfolio. As the listing of China's crude oil futures offers a new sample to study the volatility contagion of oil and coal prices, by using daily data from 2nd May 2018 to January 23, 2020, four different multivariate GARCH models are compared and contrasted in this paper. Then, based on the results of the four MGARCHs, we investigate the oil-coal portfolios' optimal weights and hedge ratios. The results indicate that, first, a long-run co-integration relationship exists in China's crude oil and coal. Second, in short run, futures price of crude oil leads that of coal, the volatility spillover effect of crude oil on coal is stronger than that of coal on crude oil. Third, the DCC-GARCH model fits best for data. Last, weights of optimal portfolio and ratios of hedge show that the crude oil is a tool to make the risk of portfolio minimal in energy markets. These results are expected to have implications for energy producers, users and trading enterprises to inform their decision-making.

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  • Guo, Yanfeng & Zhao, Huanyu, 2024. "Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 446-457.
    • Handle: RePEc:eee:reveco:v:89:y:2024:i:pb:p:446-457
    DOI: 10.1016/j.iref.2023.10.004
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    More about this item

    Keywords

    China's crude oil; Coal; MGARCH; Volatility spillovers; Diversification and hedging;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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