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A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather

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  • Liu, Hsiang-Hsi
  • Chen, Yi-Chun

Abstract

Due to the connections of energy uses, carbon emissions and climate, this study investigates the interactions, volatility spillovers, and long memory effects for carbon, oil, natural gas and coal markets by using FIEC-HYGARCH model. It also discusses the mediating effect of extreme weather. The empirical results verify that the FIEC-HYGARCH model can capture the long-term volatility behavior. The futures returns of carbon and energy have long memory and own-mean spillover effects. Moreover, the conditional variances also have volatility spillovers, long memory effects and amplitudes. Hence, there exist dynamic interrelationships among the futures returns of carbon and energy. Further, it also extends the long memory and causes various spillover effects by incorporating extreme weather into the model, indicating that extreme weather has certain impacts on carbon, oil, natural gas and coal markets.

Suggested Citation

  • Liu, Hsiang-Hsi & Chen, Yi-Chun, 2013. "A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: The impacts of extreme weather," Economic Modelling, Elsevier, vol. 35(C), pages 840-855.
  • Handle: RePEc:eee:ecmode:v:35:y:2013:i:c:p:840-855
    DOI: 10.1016/j.econmod.2013.08.007
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    Citations

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    Cited by:

    1. Salisu, Afees A. & Fasanya, Ismail O., 2013. "Modelling oil price volatility with structural breaks," Energy Policy, Elsevier, vol. 52(C), pages 554-562.
    2. Belbute, José M. & Pereira, Alfredo M., 2015. "An alternative reference scenario for global CO2 emissions from fuel consumption: An ARFIMA approach," Economics Letters, Elsevier, vol. 136(C), pages 108-111.
    3. Yu, Lean & Li, Jingjing & Tang, Ling & Wang, Shuai, 2015. "Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach," Energy Economics, Elsevier, vol. 51(C), pages 300-311.
    4. José Belbute & Alberto Marvão Pereira, 2015. "Do Global CO2 Emissions from Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis," CEFAGE-UE Working Papers 2015_14, University of Evora, CEFAGE-UE (Portugal).
    5. Wen, Xiaoqian & Bouri, Elie & Roubaud, David, 2017. "Can energy commodity futures add to the value of carbon assets?," Economic Modelling, Elsevier, vol. 62(C), pages 194-206.
    6. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.
    7. Rita Sousa & Luís Francisco Aguiar-Conraria & Maria Joana Soares, "undated". "Carbon and Energy Prices: Surfing the Wavelets of California," NIPE Working Papers 19/2014, NIPE - Universidade do Minho.
    8. Suo, Yuan-Yuan & Wang, Dong-Hua & Li, Sai-Ping, 2015. "Risk estimation of CSI 300 index spot and futures in China from a new perspective," Economic Modelling, Elsevier, vol. 49(C), pages 344-353.
    9. Green, Rikard & Larsson, Karl & Lunina, Veronika & Nilsson, Birger, 2016. "Cross-Commodity News Transmission and Volatility Spillovers in the German Energy Markets," Working Papers 2016:2, Lund University, Department of Economics, revised 11 Oct 2017.

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    Keywords

    Long memory; FIEC-HYGARCH; Volatility spillover;

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