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Managing volumetric risk of long-term power purchase agreements

机译:管理长期购电协议的体积风险

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A negative dependence between wind power production and electricity spot price exists. This is an important fact to consider for risk management of long-term power purchase agreements (PPAs). In this study we investigate this dependence by constructing a joint model using constant as well as time-varying copulas. We propose to use score-driven models as marginal model for the spot price of electricity as these are more robust to extreme events compared to ARMA-GARCH models. We apply the new model to pricing and risk management of PPAs and benchmark it against the ARMA-GARCH specification. Our comparison shows that the score-driven model results in a statistically significant improvement of predicting the Value-at-Risk (VaR), which is of high importance for risk management of long-term PPAs. Further, comparing constant and time-varying copulas we find that all time-varying copulas are significantly better than their constant counterparts at predicting the VaR, hence time-varying copulas should be used in risk management of PPAs. (C) 2019 Elsevier B.V. All rights reserved.
机译:风力发电与电价之间存在负相关关系。对于长期购电协议(PPA)的风险管理,这是要考虑的重要事实。在这项研究中,我们通过使用恒定和随时间变化的copulas构建联合模型来研究这种依赖性。我们建议使用分数驱动模型作为电力现货价格的边际模型,因为与ARMA-GARCH模型相比,它们更能应对极端事件。我们将新模型应用于PPA的定价和风险管理,并根据ARMA-GARCH规范对其进行基准测试。我们的比较表明,分数驱动模型可提高风险价值预测(VaR)的统计显着性,这对于长期PPA的风险管理非常重要。此外,通过比较恒定和时变的copula,我们发现所有时变的copula在预测VaR方面都明显优于其恒定的copula,因此应将时变的copula用于PPA的风险管理中。 (C)2019 Elsevier B.V.保留所有权利。

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