Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
Research in International Business and Finance
; 64:101863, 2023.
Article
in English
| ScienceDirect | ID: covidwho-2165812
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Language:
English
Journal:
Research in International Business and Finance
Year:
2023
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS