Electricity is a quite unique commodity. Due to the economically non-storable nature of the commodity that electricity is, the constant balance between consumption and production, weather effects, such as temperature, wind speed, solar intensity etc, and the intensity of everyday and business activities, e.g. holi- days, weekends, on- and off-peak hours etc., the price dynamics of this commodity are quite unique as they are not observed in any other market. This extreme price volatility has forced market participants to hedge volumes as well as price risks. Naturally, electricity price forecast models are of great interest to portfolio managers.
Current data-driven models are combined with expertise of traders due to the considerable uncertainty of these models. We aim to subject trader expertise to a transparent methodology using structured expert judgement. During this study, we conduct two elicitations whose variables of interest concern average day- ahead spot prices for 2025, 2030 and 2035. We distinguish between baseload and peakload price. The first elicitation uses assessments regarding current Dutch day-ahead electricity spot prices to forecast forecast the variables of interest. We forecast the average day-ahead electricity spot price for 2025, 2030 and 2035. The second elicitation uses assessments regarding data on past, current and future developments in the Dutch electricity markets to forecast forecast the variables of interest. The second elicitation results in a decision maker with a higher price forecast than the decision maker of the first elicitation. Uncertainty regarding the forecast is higher for 2025 and 2030 during the second study. Uncertainty is equally high regarding the peakload price forecast for 2035 in both studies, however the range shifts.

In this thesis we try to capture the dependence structure of the publications of a scholar and the citations of those publications via copulas. To do so, we will use a sample of Quebec re- searchers for who their publication amount as well as their citation amounts are known. We are provided with multiple variables concerning citation. We study the dependence structure be- tween these variables, with the aim of fitting copulas to this structure, by calculating correlation scores and visualising the structure. Copulas are functions that ”join together” one-dimensional distribution functions with a dependence structure, in order to represent joint distributions. The correlation scores are calculated across various ranges of the variables to provide us with a deeper understanding of the dependence structure between the variables.
Using Sklar’s theorem and some helpful functions in various packages in the software program R, parametric copulas fit the dependence structures of the various pairs of variables. Based on a Goodness-of-fit test, certain parametric copula models are rejected at a 5% significance level. Unsurprisingly, there are also dependence structures that can be well captured with a parametric copula.
Parametric copula families are not only used for fitting the data, but also for prediction. Since a good fitting model does not necessarily imply a good predictive model, we have also performed a validation analysis. The parametric copula models that are not rejected by the test at a 5% significance level are validated via k-fold cross validation. Part of the data have been used to fit the model and the remaining has been validated using a k-fold cross validation. It turns out that the best fitting copula model does not always perform well in term of prediction. That is, these copulas do not always perform best during the cross-validation.