Financial product valuation is essential to support trading and risk management decisions. A standard method to perform the valuation of financial products under uncertainty is to estimate their return for alternating scenarios. These scenarios are usually simulated with Monte-Carlo methods. For sophisticated products, this leads to a considerable number of simulations and high computational expenses. Recently, implicit generative machine learning models such GANs have shown promising results in creative tasks, including time-dependent, high dimensional problems. The goal of this study is to utilize the power of GANs to generate realistic market scenarios to depict the uncertainties in market conditions.
The project will be conducted as an external master thesis in collaboration with Energie Baden-Württemberg (EnBW) and the student will be employed at EnBW during the work. The implementation has a significant potential and we aim to publish the outcomes in international scientific journals.
- Proven, strong background in energy market AND machine learning methods
- Python programming skills
Good oral and written communication skills.