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.

Partners

Deloitte

Sponsors

CRIF
McKinsey
Patria Finance
Česká Spořitelna
EY