Forecasting Term Structure of Crude Oil Markets Using Neural Networks
| Author: | Mgr. Barbora Malinská |
|---|---|
| Year: | 2015 - winter |
| Leaders: | doc. PhDr. Jozef Baruník Ph.D. |
| Consultants: | |
| Work type: | Finance, Financial Markets and Banking Masters |
| Language: | English |
| Pages: | 89 |
| Awards and prizes: | |
| Link: | https://is.cuni.cz/webapps/zzp/detail/138276/ |
| Abstract: | 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. |
