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A seasonal copula mixture for hedging the clean spark spread with wind power futures

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  • Christensen, Troels Sønderby
  • Pircalabu, Anca
  • Høg, Esben

Abstract

The recently introduced German wind power futures have brought the opportunity to address the problem of volume risk in wind power generation directly. In this paper, we study the hedging benefits of these instruments in the context of peak gas-fired power plants, by employing a strategy that allows trading in the day-ahead clean spark spread and wind power futures. To facilitate hedging decisions, we propose a seasonal copula mixture for the joint behavior of the day-ahead clean spark spread and the daily wind index. The model describes the data surprisingly well, both in terms of the marginals and the dependence structure, while being straightforward and easy to implement. Based on Monte Carlo simulations from the proposed model, the results indicate that significant benefits can be achieved by using wind power futures. Moreover, a comparison study shows that accounting for asymmetry, tail dependence, and seasonality in the dependence structure is especially important in the context of risk management.

Suggested Citation

  • Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
  • Handle: RePEc:eee:eneeco:v:78:y:2019:i:c:p:64-80
    DOI: 10.1016/j.eneco.2018.11.002
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    References listed on IDEAS

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    Cited by:

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    2. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
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    4. Spodniak, Petr & Bertsch, Valentin, 2020. "Is flexible and dispatchable generation capacity rewarded in electricity futures markets? A multinational impact analysis," Energy, Elsevier, vol. 196(C).

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    More about this item

    Keywords

    Clean spark spread; Wind power futures; Copula models; Time-varying dependence; Hedging;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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