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Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models

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  • Shiferaw, Yegnanew A.

Abstract

This article investigates the dependence structure between the agricultural commodity prices (white maize, yellow maize, wheat, sunflower and soya) and energy prices (oil, natural gas and coal) dynamics of South Africa based on the Bayesian multivariate GARCH (MGARCH) model with skewness and heavy tails. A computationally intensive Markov chain Monte Carlo (MCMC) algorithm was adopted and implemented for both parameter estimation and model comparison. Based on the information criteria, the Bayesian DCC-MGARCH model with the error skewed-mvt distribution assumption performed better than other competitive methods. Moreover, the correlation between the agricultural commodity and energy price returns is dynamic (time-varying) in South Africa, indicating that the prices of agricultural commodities and energy prices exhibit strong co-movement. The findings have significant implications in the domain of agricultural commodity policy and financial sector.

Suggested Citation

  • Shiferaw, Yegnanew A., 2019. "Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119304157
    DOI: 10.1016/j.physa.2019.04.043
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    Citations

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    Cited by:

    1. Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
    2. Xinyu Yuan & Jiechen Tang & Wing-Keung Wong & Songsak Sriboonchitta, 2020. "Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    3. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    4. Khalfaoui, Rabeh & Shahzad, Umer & Ghaemi Asl, Mahdi & Ben Jabeur, Sami, 2023. "Investigating the spillovers between energy, food, and agricultural commodity markets: New insights from the quantile coherency approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 63-80.
    5. Jian Ni & Yue Xu, 2023. "Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 35-55, January.
    6. Ifeacho Christopher I & Choga Ireen, 2023. "Analysis of the Nature and Determinants of Energy Price Dynamics in Sub-Saharan Africa (SSA)," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 33(2), pages 27-48, June.
    7. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    8. Yegnanew A. Shiferaw, 2023. "Mapping Disaggregate-Level Agricultural Households in South Africa Using a Hierarchical Bayes Small Area Estimation Approach," Agriculture, MDPI, vol. 13(3), pages 1-17, March.
    9. Yu, Xing & Li, Yanyan & Lu, Junli & Shen, Xilin, 2023. "Futures hedging in crude oil markets: A trade-off between risk and return," Resources Policy, Elsevier, vol. 80(C).
    10. Miroslava Ivanova & Lilko Dospatliev, 2023. "Effects of Diesel Price on Changes in Agricultural Commodity Prices in Bulgaria," Mathematics, MDPI, vol. 11(3), pages 1-22, January.
    11. Zeng, Sheng & Liu, Xinchun & Li, Xiafei & Wei, Qi & Shang, Yue, 2019. "Information dominance among hedging assets: Evidence from return and volatility directional spillovers in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    12. Shahzad, Farrukh & Bouri, Elie & Mokni, Khaled & Ajmi, Ahdi Noomen, 2021. "Energy, agriculture, and precious metals: Evidence from time-varying Granger causal relationships for both return and volatility," Resources Policy, Elsevier, vol. 74(C).
    13. Xiong, Shi & Chen, Weidong, 2022. "A robust hybrid method using dynamic network analysis and Weighted Mahalanobis distance for modeling systemic risk in the international energy market," Energy Economics, Elsevier, vol. 109(C).
    14. Chaofeng Tang & Kentaka Aruga & Yi Hu, 2023. "The Dynamic Correlation and Volatility Spillover among Green Bonds, Clean Energy Stock, and Fossil Fuel Market," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    15. Hedi Ben Haddad & Imed Mezghani & Abdessalem Gouider, 2021. "The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties," Economies, MDPI, vol. 9(2), pages 1-22, June.
    16. Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).
    17. Hung, Ngo Thai, 2021. "Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Resources Policy, Elsevier, vol. 73(C).
    18. Tetsuji Tanaka & Jin Guo, 2020. "International price volatility transmission and structural change: a market connectivity analysis in the beef sector," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    19. Ahlrichs, Jakob & Rockstuhl, Sebastian & Tränkler, Timm & Wenninger, Simon, 2020. "The impact of political instruments on building energy retrofits: A risk-integrated thermal Energy Hub approach," Energy Policy, Elsevier, vol. 147(C).
    20. Jia-Lang Xu & Ying-Lin Hsu, 2022. "The Impact of News Sentiment Indicators on Agricultural Product Prices," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1645-1657, April.
    21. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    22. Hao Chen & Zhixin Liu & Yinpeng Zhang & You Wu, 2020. "The Linkages of Carbon Spot-Futures: Evidence from EU-ETS in the Third Phase," Sustainability, MDPI, vol. 12(6), pages 1-18, March.

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