Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/146381
Authors: 
Baruník, Jozef
Malinská, Barbora
Year of Publication: 
2015
Series/Report no.: 
IES Working Paper 25/2015
Abstract: 
The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.
Subjects: 
term structure
Nelson-Siegel model
dynamic neural networks
crude oil futures
JEL: 
C14
C32
C45
G02
G17
Document Type: 
Working Paper
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