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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920761" target="_blank" >RIV/00216275:25410/23:39920761 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0275531922002495?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0275531922002495?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ribaf.2022.101863" target="_blank" >10.1016/j.ribaf.2022.101863</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

This paper aims to develop an artificial neural network-based 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 network-based 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

This paper aims to develop an artificial neural network-based 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 network-based 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Research in International Business and Finance

ISSN

0275-5319

e-ISSN

1878-3384

Svazek periodika

64

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

101863

Kód UT WoS článku

000916569300001

EID výsledku v databázi Scopus

2-s2.0-85144919650