Publication detail

Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks

Author(s): doc. PhDr. Jozef Baruník Ph.D.,
Mgr. Barbora Malinská ,
Type: IES Working Papers
Year: 2015
Number: 25
ISSN / ISBN:
Published in: IES Working Papers 25/2015
Publishing place: Prague
Keywords: term structure, Nelson-Siegel model, dynamic neural networks, crude oil futures
JEL codes: C14, C32, C45, G02, G17
Suggested Citation: Barunik J., Malinska B. (2015). “ Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks” IES Working Paper 25/2015. IES FSV. Charles University.
Grants: DYME – Dynamic Models in Economics
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.
Downloadable: wp_2015_25_barunik_malinska

04

December

December 2021
MonTueWedThuFriSatSun
  12345
6789101112
13141516171819
20212223242526
2728293031  

Partners

Deloitte

Sponsors

CRIF
McKinsey
Patria Finance