Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Price modelling of structured products on the European energy market |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 40 |
| Date | 2021 |
| Abstract Text | There is an established approach for pricing swing (also called take-or-pay) option contracts in academia and industry alike, which consists of using the Longstaff-Schwarz method, which was introduced by the authors in 2001 and only moderately evolved since then. In this thesis, different ways to modify and extend this approach are explored, especially the use of neural networks among other machine learning methods for enhancement. The goal of this thesis is to find whether those techniques, made more available and economically viable to use by current progress in hardware, can deliver better results compared to established approaches. In particular, swing contract pricing on the gas market is researched. First, an overview of the market is given, then classic and other approaches are introduced. Finally, introduced approaches are compared on the basis of computational resources needed and overall performance. In conclusion, original Longstaff-Schwarz procedure is shown to be still relevant for swing option pricing. New approaches were faster in the real world situations, but due to data sourcing problem were unable to provide more accurate results. Novelty in the work is drawn from the extension of the existing techniques already tried for pricing of completely different financial assets or similar products, but in simpler cases to swing option pricing, where path-dependency and optimization of multiple exercise decisions schedule add considerable complexity. |
| Export |
BibTeX
|