Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F24%3A100196" target="_blank" >RIV/60460709:41110/24:100196 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s10614-024-10550-3" target="_blank" >https://doi.org/10.1007/s10614-024-10550-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10614-024-10550-3" target="_blank" >10.1007/s10614-024-10550-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach

Popis výsledku v původním jazyce

Amidst a dynamic energy market landscape, understanding evolving influencing factors is pivotal. Accurate forecasting techniques are indispensable for effective energy resource management. This study focuses on illuminating insights into economic uncertainty and commodity price forecasting. A meticulously curated dataset spanning January 2000 to December 2022 forms the foundation, incorporating diverse economic and financial uncertainty metrics. Through an innovative research framework, we discern influential factors and forecast their trajectories. Three deep learning models-Short-Term Memory, Gated Recurrent Units, and Multilayer Perception Network-are deployed. The Multilayer Perception model emerges as the standout, showcasing exceptional predictive capability rooted in its adeptness at decoding intricate market patterns. This finding holds significance for policymakers, industry experts, and energy economists. The Multilayer Perception model's supremacy offers a robust tool for decision-making in crafting economic policies and navigating volatile markets.

Název v anglickém jazyce

Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach

Popis výsledku anglicky

Amidst a dynamic energy market landscape, understanding evolving influencing factors is pivotal. Accurate forecasting techniques are indispensable for effective energy resource management. This study focuses on illuminating insights into economic uncertainty and commodity price forecasting. A meticulously curated dataset spanning January 2000 to December 2022 forms the foundation, incorporating diverse economic and financial uncertainty metrics. Through an innovative research framework, we discern influential factors and forecast their trajectories. Three deep learning models-Short-Term Memory, Gated Recurrent Units, and Multilayer Perception Network-are deployed. The Multilayer Perception model emerges as the standout, showcasing exceptional predictive capability rooted in its adeptness at decoding intricate market patterns. This finding holds significance for policymakers, industry experts, and energy economists. The Multilayer Perception model's supremacy offers a robust tool for decision-making in crafting economic policies and navigating volatile markets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computational Economics

ISSN

0927-7099

e-ISSN

0927-7099

Svazek periodika

64

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

21

Strana od-do

3295-3315

Kód UT WoS článku

001171328100001

EID výsledku v databázi Scopus

—