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

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
JournalEnergy Economics
Volume78
Issue numberFebruary 2019
Pages (from-to)64-80
Number of pages17
ISSN0140-9883
DOIs
Publication statusPublished - 1 Feb 2019

Fingerprint

Electric sparks
Wind power
Risk management
Power generation
Power plants
Hedging
Copula
Gases
Dependence structure

Keywords

  • Clean spark spread
  • Wind power futures
  • Copula models
  • Time-varying dependence
  • Hedging

Cite this

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title = "A seasonal copula mixture for hedging the clean spark spread with wind power futures",
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.",
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A seasonal copula mixture for hedging the clean spark spread with wind power futures. / Christensen, Troels Sønderby; Pircalabu, Anca; Høg, Esben.

In: Energy Economics, Vol. 78, No. February 2019, 01.02.2019, p. 64-80.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

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AU - Pircalabu, Anca

AU - Høg, Esben

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AB - 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.

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