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Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice

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  • Chia-Lin Chang

    (Department of Applied Economics, Department of Finance, National Chung Hsing University, 40227 Taichung, Taiwan)

  • Yiying Li

    (Department of Quantitative Finance, National Tsing Hua University, 30013 Hsinchu, Taiwan)

  • Michael McAleer

    (Department of Finance, Asia University, 41354 Taichung, Taiwan
    Discipline of Business Analytics, University of Sydney Business School, Sydney, NSW 2006, Australia
    Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands
    Department of Economic Analysis and ICAE, Complutense University of Madrid, 28040 Madrid, Spain)

Abstract

Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

Suggested Citation

  • Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1595-:d:153161
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    Cited by:

    1. Chia-Lin Chang & Chia-Ping Liu & Michael McAleer, 2016. "Volatility Spillovers for Spot, Futures, and ETF Prices in Energy and Agriculture," Tinbergen Institute Discussion Papers 16-046/III, Tinbergen Institute.
    2. Chia-Lin Chang & Michael McAleer & Guangdong Zuo, 2017. "Volatility Spillovers and Causality of Carbon Emissions, Oil and Coal Spot and Futures for the EU and USA," Sustainability, MDPI, vol. 9(10), pages 1-22, October.
    3. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    4. Zhicheng Liang & Junwei Wang & Kin Keung Lai, 2020. "Dependence Structure Analysis and VaR Estimation Based on China’s and International Gold Price: A Copula Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 169-193, February.
    5. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    6. Chang, C-L. & Hsieh, T-L. & McAleer, M.J., 2016. "How are VIX and Stock Index ETF Related?," Econometric Institute Research Papers EI2016-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Caporin, Massimiliano & Chang, Chia-Lin & McAleer, Michael, 2019. "Are the S&P 500 index and crude oil, natural gas and ethanol futures related for intra-day data?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 50-70.
    8. Chia-Lin Chang & Michael McAleer & Chien-Hsun Wang, 2017. "An Econometric Analysis of ETF and ETF Futures in Financial and Energy Markets Using Generated Regressors," IJFS, MDPI, vol. 6(1), pages 1-24, December.
    9. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    10. Chang, C-L. & McAleer, M.J. & Wang, Y-A., 2018. "Latent Volatility Granger Causality and Spillovers in Renewable Energy and Crude Oil ETFs," Econometric Institute Research Papers TI 2018-052/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    12. Duc Hong Vo & Tan Ngoc Vu & Anh The Vo & Michael McAleer, 2019. "Modeling the Relationship between Crude Oil and Agricultural Commodity Prices," Energies, MDPI, vol. 12(7), pages 1-41, April.
    13. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2015. "Multivariate Volatility Impulse Response Analysis of GFC News Events," Tinbergen Institute Discussion Papers 15-089/III, Tinbergen Institute.
    14. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2017. "Volatility spillover and multivariate volatility impulse response analysis of GFC news events," Applied Economics, Taylor & Francis Journals, vol. 49(33), pages 3246-3262, July.
    15. Chang, Chia-Lin & Mai, Te-Ke & McAleer, Michael, 2019. "Establishing national carbon emission prices for China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 1-16.
    16. Chang, Chia-Lin & McAleer, Michael & Wang, Yanghuiting, 2018. "Testing Co-Volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances," Energy, Elsevier, vol. 151(C), pages 984-997.
    17. David E. Allen & Chialin Chang & Michael McAleer & Abhay K Singh, 2018. "A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices," Applied Economics, Taylor & Francis Journals, vol. 50(7), pages 804-823, February.
    18. Gaoke Liao & Zhenghui Li & Ziqing Du & Yue Liu, 2019. "The Heterogeneous Interconnections between Supply or Demand Side and Oil Risks," Energies, MDPI, vol. 12(11), pages 1-17, June.
    19. Zolfaghari, Mehdi & Ghoddusi, Hamed & Faghihian, Fatemeh, 2020. "Volatility spillovers for energy prices: A diagonal BEKK approach," Energy Economics, Elsevier, vol. 92(C).
    20. Chia-Lin Chang & Shu-Han Hsu & Michael McAleer, 2018. "Risk Spillovers in Returns for Chinese and International Tourists to Taiwan," Tinbergen Institute Discussion Papers 18-031/III, Tinbergen Institute.
    21. Chia-Lin Chang & Michael McAleer & Jiarong Tian, 2019. "Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China," Energies, MDPI, vol. 12(8), pages 1-24, April.
    22. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2018. "Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK," JRFM, MDPI, vol. 11(4), pages 1-25, September.
    23. Cui, Jinxin & Goh, Mark & Li, Binlin & Zou, Huiwen, 2021. "Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives," Energy, Elsevier, vol. 216(C).
    24. Michael McAleer, 2015. "The Fundamental Equation in Tourism Finance," JRFM, MDPI, vol. 8(4), pages 1-6, December.
    25. Hsu, Shu-Han & Sheu, Chwen & Yoon, Jiho, 2021. "Risk spillovers between cryptocurrencies and traditional currencies and gold under different global economic conditions," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).

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    More about this item

    Keywords

    energy markets; agricultural markets; volatility and covolatility spillovers; univariate and multivariate conditional volatility models; Baba; Engle; Kraft; and Kroner; dynamic conditional correlation; definitions of spillovers;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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