Forecasting Term Structure of Crude Oil Markets Using Neural Networks
|Author:||Mgr. Barbora Malinská|
|Year:||2015 - winter|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|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.