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Modeling and Analyzing the Mean and Volatility Relationship between Electricity Price Returns and Fuel Market Returns

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  • Ching-Chun Wei

Abstract

This paper has two objectives. First, we apply the symmetric and asymmetric VAR(1)-BEKK-MGARCH(1.1), VAR(1)-CCC-MGARCH(1,1), VAR(1)-DCC-MGARCH, VAR(1)-VARMA-CCC-MGARCH and VAR(1)- VARMA-DCC-MGARCH models to explore the return and volatility interactions among electricity and other fuel price markets(oil, natural gas, and coal). Second, this paper investigates the importance of not only volatility spillover among energy markets, but also the asymmetric effects of negative and positive shockson the conditional variance of modeling one energy market’s volatility upon the returns of future prices within and across other energy markets. The empirical results display that these models do capture the dynamic structure of the return interactions and volatility spillovers and exhibit statistical significance for own past mean and volatility short-and long-run persistence effects, while there are just a few cross-market effects for each model.

Suggested Citation

  • Ching-Chun Wei, 2016. "Modeling and Analyzing the Mean and Volatility Relationship between Electricity Price Returns and Fuel Market Returns," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(7), pages 1-55, July.
  • Handle: RePEc:ibn:ijefaa:v:8:y:2016:i:7:p:55
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    References listed on IDEAS

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    1. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2010. "Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets," Energy Economics, Elsevier, vol. 32(6), pages 1445-1455, November.
    2. Chia-Lin Chang & Michael McAleer, 2013. "Ranking journal quality by harmonic mean of ranks: an application to ISI statistics & probability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(1), pages 27-53, February.
    3. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
    4. Gary W. Emery & Qingfeng (Wilson) Liu, 2002. "An analysis of the relationship between electricity and natural‐gas futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(2), pages 95-122, February.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Lanza, Alessandro & Manera, Matteo & McAleer, Michael, 2006. "Modeling dynamic conditional correlations in WTI oil forward and futures returns," Finance Research Letters, Elsevier, vol. 3(2), pages 114-132, June.
    7. Lin, Sharon Xiaowen & Tamvakis, Michael N., 2001. "Spillover effects in energy futures markets," Energy Economics, Elsevier, vol. 23(1), pages 43-56, January.
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    Cited by:

    1. Zhong, Yi & Liu, Jiapeng, 2021. "Correlations and volatility spillovers between China and Southeast Asian stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 57-69.
    2. Theodosios Perifanis & Athanasios Dagoumas, 2018. "Price and Volatility Spillovers Between the US Crude Oil and Natural Gas Wholesale Markets," Energies, MDPI, vol. 11(10), pages 1-25, October.

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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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