The increasing number of Renewable Sources (RES) in the European electric grid has resulted in the necessity for producers to adjust their position with respect to the change in weather forecasting. Therefore, the European Power Exchange (EPEX SPOT) has seen an expansion of the Intraday Market (ID) where market participants can trade up until a few minutes before delivery. However, literature in Electricity Price Forecasting (EPF) primarily concerns the Day-ahead market (DA). The goal of this thesis is to develop a model to predict the prices of hourly contracts in the ID market using stochastic processes. This would facilitate the obtainment of a probability density function (pdf) for the prices of the contracts at any time during the day. This thesis focuses on the German Energy market which is the most liquid among the European countries. I then propose 4 different Levy processes for the forecasting of the process: a simple Brownian Motion, a Jump Diffusion Process, a Normal Inverse Gaussian (NIG), and a Generalised Hyperbolic model (GH). The Normal Inverse Gaussian model is selected according to the Akaike Information Criterion and the Bayesian Information Criterion.
In order to integrate other variables into the model, I then conducted a fundamental analysis. I find a low correlation between the volume traded during the ID phase and the change in Wind, solar, and con- sumption forecasting. Moreover, the volatility of the contract shows a low correlation with the changes in forecasting and the traded volume.
Additionally, I conduct an augmented Dickey-Fueller test and a Mann-Kendall test on the price of the contracts to verify the presence of either mean reversion or drift. Results show that the latter cannot be refused for most days.
Finally, alongside the NIG model (noise model) I propose one model including a drift component (noise-drift model) and a second model adding both a drift and a volatility component (noise-drift-volatility model). The three models are compared in the accuracy of forecasting the tail probabilities of the ID1 and ID3 indexes. It is found that the noise model and the noise-drift model cast a better prediction than the noise-drift-volatility model. This suggests that the volatility during the first part of a day of trading is a poor predictor of volatility in the hours close to delivery.