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Neural network prediction of crude oil futures using B-splines

Sunil Butler, Piotr Kokoszka, Hong Miao and Han Lin Shang ()

Energy Economics, 2021, vol. 94, issue C

Abstract: We propose two ways to improve the forecasting accuracy of a focused time-delay neural network (FTDNN) that forecasts the term structure of crude oil futures. Our results show that a convergence based FTDNN makes consistently more accurate predictions than the fixed-epoch FTDNN in Barunik and Malinska (2016). Further, we suggest using basis splines (B-splines), instead of Nelson-Siegel functions, to fit the term structure curves. The empirical results show that the B-spline expansions lead to consistently better 1 and 3 months ahead predictions compared to the convergence based FTDNN. We also explore conditions under which the B-spline based approach may be better for longer-term predictions.

Keywords: Crude oil futures; Term structure; Neural network; Splines; Functional data; Model confidence set (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:94:y:2021:i:c:s0140988320304205

DOI: 10.1016/j.eneco.2020.105080

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Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

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