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Dependence structure in the Australian electricity markets: New evidence from regular vine copulae

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  • Apergis, Nicholas
  • Gozgor, Giray
  • Lau, Chi Keung Marco
  • Wang, Shixuan

Abstract

In this study, regular vine copula was used to investigate the dependence structure of electricity prices at the state level in the Australian National Electricity Market (NEM), during three periods related to the adoption and abolition of the carbon tax. In the pre-carbon period, we found evidence of tail dependence separately in the northern and southern NEM, but not across them. During the carbon period, the joint spike in the northern NEM disappeared, and the tail dependence in the southern NEM decreased. In the post-carbon period, the best dependence structure turned out to be a flexible structure of the regular vine, which exactly matches the geographical infrastructure connectedness of transmission wires. Besides, both upper and lower tail dependences were found in all adjacent states after the abolition of the carbon tax, suggesting a more integrated market regarding tail dependence. Our findings have substantial implications for risk management in the NEM, especially for those participants exposed to multiple states.

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  • Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Dependence structure in the Australian electricity markets: New evidence from regular vine copulae," Energy Economics, Elsevier, vol. 90(C).
  • Handle: RePEc:eee:eneeco:v:90:y:2020:i:c:s0140988320301742
    DOI: 10.1016/j.eneco.2020.104834
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    3. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    4. Naeem, Muhammad Abubakr & Karim, Sitara & Rabbani, Mustafa Raza & Nepal, Rabindra & Uddin, Gazi Salah, 2022. "Market integration in the Australian National Electricity Market: Fresh evidence from asymmetric time-frequency connectedness," Energy Economics, Elsevier, vol. 112(C).
    5. Li, Xiafei & Li, Bo & Wei, Guiwu & Bai, Lan & Wei, Yu & Liang, Chao, 2021. "Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US," Resources Policy, Elsevier, vol. 73(C).
    6. Han, Xuyuan & Liu, Zhenya & Wang, Shixuan, 2022. "An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting," Journal of Commodity Markets, Elsevier, vol. 25(C).
    7. Meng-Shiuh Chang & Meng-Wei Chen & Peijie Ju, 2023. "Asymmetry in Hedges, Safe Havens, Flights and Contagion: Unconditional Quantile Regression Approach," SAGE Open, , vol. 13(4), pages 21582440231, November.
    8. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
    9. Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Wali Ullah, G M & Tiwari, Aviral Kumar & Khan, Isma, 2023. "Tail risk contagion across electricity markets in crisis periods," Energy Economics, Elsevier, vol. 127(PB).
    10. 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).
    11. Tselika, Kyriaki, 2022. "The impact of variable renewables on the distribution of hourly electricity prices and their variability: A panel approach," Energy Economics, Elsevier, vol. 113(C).
    12. Ma, Rufei & Liu, Zhenhua & Zhai, Pengxiang, 2022. "Does economic policy uncertainty drive volatility spillovers in electricity markets: Time and frequency evidence," Energy Economics, Elsevier, vol. 107(C).

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    More about this item

    Keywords

    Australian National Electricity Market; Dependence structure; Tail dependence; R-vine copula;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • 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
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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