This thesis enhances rare literature focusing on modeling and forecasting of term structure of crude oil markets. Using dynamic Nelson-Siegel model, crude oil term structure is decomposed to three latent factors, which are further forecasted using both parametric and dynamic neural network approaches. In-sample fit using Nelson-Siegel model brings encouraging results and proves its applicability on crude oil futures prices. Forecasts obtained by focused time-delay neural network are in general more accurate than other benchmark models. Moreover, forecast error is decreasing with increasing time to maturity.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:340230 |
| Date | January 2015 |
| Creators | Malinská, Barbora |
| Contributors | Baruník, Jozef, Polák, Petr |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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