Work detail

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.

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