Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

Ahmed Bouteska, Petr Hajek, Ben Fisher and Mohammad Zoynul Abedin

Research in International Business and Finance, 2023, vol. 64, issue C

Abstract: This paper aims to develop an artificial neural networkbased forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural networkbased models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.

Keywords: Energy market; Natural gas; Crude oil; Nonlinear focused time-delayed neural network (search for similar items in EconPapers)
JEL-codes: C45 Q41 Q47 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531922002495

DOI: 10.1016/j.ribaf.2022.101863

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