Leopoldo Catania

There exists a negative dependence between wind power production and electricity spot price. 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 using a new generation of 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 a previously published model of the ARMA-GARCH type. 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.