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European Gas Markets, Trading Hubs, and Price Formation: A Network Perspective

Author

Listed:
  • David Woroniuk

    (Durham University Business School)

  • Arzé Karam

    (Durham University Business School)

  • Tooraj Jamasb

    (Durham University Business School)

Abstract

We apply network theory to analyse the interactions of trading hub prices, and to assess the harmonisation of the European gas market. We construct dynamic networks, where the nodes correspond to the twelve EU trading hubs, and where the edges weight the causality between the variations of the respective gas prices. Network density dynamically calculates the aggregate quantity of causal interactions recorded within the system, which provides information pertaining to the integration of the European gas network. We document a number of spikes in network density, suggesting short periods of improved connectivity of European gas markets. We argue that these results appear to be driven by exogenous factors, such as unseasonal weather patterns, seismic activity and pipeline capacity reductions or outages. The findings elucidate the time varying nature of European gas market dynamics, and the importance of continual monitoring of market evolution.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • David Woroniuk & Arzé Karam & Tooraj Jamasb, 2019. "European Gas Markets, Trading Hubs, and Price Formation: A Network Perspective," Working Papers EPRG1922, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1922
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    Cited by:

    1. Chen, Yufeng & Wang, Chuwen & Zhu, Zhitao, 2022. "Toward the integration of European gas futures market under COVID-19 shock: A quantile connectedness approach," Energy Economics, Elsevier, vol. 114(C).
    2. Valeriy Prasolov & Valery Bezpalov & Svetlana Doguchaeva & Rodion Rogulin, 2020. "Energy Price Formation and Energy Consumption by Households as a Factor of Ensuring Energy Safety," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 82-93.

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    JEL classification:

    • 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
    • F18 - International Economics - - Trade - - - Trade and Environment
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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