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Předpovídání výnosové křivky na trhu s ropou pomocí neuronových sítí / Forecasting Term Structure of Crude Oil Markets Using Neural Networks

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.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:340230
Date January 2015
CreatorsMalinská, Barbora
ContributorsBaruník, Jozef, Polák, Petr
Source SetsCzech ETDs
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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