| dc.date.accessioned | 2021-06-22T14:38:37Z | |
| dc.date.available | 2021-06-22T14:38:37Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | http://hdl.handle.net/10852/86437 | |
| dc.description.abstract | The production of renewable energy is growing world-wide, and -- as a result -- power production is becoming increasingly dependent on weather factors such as temperature, wind and precipitation. All of these factors are hard to predict, and this causes power prices to change rapidly and unpredictably, and makes the modelling of financial risk in energy markets particularly challenging. This thesis develops new models and tools to be used in this direction.
Energy markets can be divided into three main sectors: there is (1) a spot market for short-term delivery contracts, (2) a forward market for delivery in a future time at a price set today, and (3) an option market where the contracts traded allow, but not oblige, the buyer to buy or sell the asset in a future time at a price set today. Buying and selling electricity in these markets, while managing the financial risk, requires accurate mathematical models.
This thesis is concerned with the modelling of these markets. It both develops concrete models and more abstract mathematical tools which can be used for this challenging task. In particular, it focuses on spot price modelling, by taking into account the dependence between spot price behaviour and weather variables such as wind speed. Moreover, it focuses on forward price modelling and pricing of options written on forward contracts with delivery period, which are typical in the energy markets. Two central challenges which are addressed in this thesis are model accuracy and computational complexity, both of which are improved upon by using deep learning. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.haspart | Paper I F. E. Benth, L. Di Persio and S. Lavagnini. “Stochastic Modelling of Wind Derivatives in Energy Markets”. Published in Risks 6.2 (2018): 56. An author version is included in the thesis. The published version is available at: https://doi.org/10.3390/risks6020056 | |
| dc.relation.haspart | Paper II F. E. Benth and S. Lavagnini. “Correlators of Polynomial Processes”. Published in SIAM Journal on Financial Mathematics 12(4) (2021), 1374–1415. An author version is included in the thesis. The published version is available at: https://doi.org/10.1137/21M141556X | |
| dc.relation.haspart | Paper III S. Lavagnini. “CARMA Approximations and Estimation”. Published in Frontiers in Applied Mathematics and Statistics 6 (2020): 37. An author version is included in the thesis. The published version is available at: https://doi.org/10.3389/fams.2020.00037 | |
| dc.relation.haspart | Paper IV F. E. Benth, N. Detering and S. Lavagnini. “Accuracy of Deep Learning in Calibrating HJM Forward Curves”. Published in Digital Finance (2021): 1-40. An author version is included in the thesis. The published version is available at: https://doi.org/10.1007/s42521-021-00030-w | |
| dc.relation.haspart | Paper V S. Lavagnini. “Pricing Asian Options with Correlators”. Published in: International Journal of Theoretical and Applied Finance (2021). An author version is included in the thesis. The published version is available at: https://doi.org/10.1142/S0219024921500412 | |
| dc.relation.uri | https://doi.org/10.3390/risks6020056 | |
| dc.relation.uri | https://doi.org/10.1137/21M141556X | |
| dc.relation.uri | https://doi.org/10.3389/fams.2020.00037 | |
| dc.relation.uri | https://doi.org/10.1007/s42521-021-00030-w | |
| dc.relation.uri | https://doi.org/10.1142/S0219024921500412 | |
| dc.title | Stochastic Modelling in Energy Markets - From the Spot Price to Derivative Contracts | en_US |
| dc.type | Doctoral thesis | en_US |
| dc.creator.author | Lavagnini, Silvia | |
| dc.identifier.urn | URN:NBN:no-89082 | |
| dc.type.document | Doktoravhandling | en_US |
| dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/86437/3/PhD-Lavagnini-DUO.pdf | |