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.
Original language | English |
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Article number | 105080 |
Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | Energy Economics |
Volume | 94 |
Early online date | 28 Dec 2020 |
DOIs | |
Publication status | Published - 1 Feb 2021 |
Keywords
- Crude oil futures
- Functional data
- Model confidence set
- Neural network
- Splines
- Term structure