Forecasting of clean energy market volatility: The role of oil and the technology sector

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F24%3A00139608" target="_blank" >RIV/00216224:14560/24:00139608 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0140988324001592" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0140988324001592</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eneco.2024.107451" target="_blank" >10.1016/j.eneco.2024.107451</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting of clean energy market volatility: The role of oil and the technology sector

Popis výsledku v původním jazyce

This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various decompositions of daily realized volatility, we account for the heterogeneity across clean energy sub-sectors using the dynamic common correlated effect heterogeneous autoregressive (DCCE-HAR) model. Our findings reveal that, in the short term, price variations in technology shares are more informative for future clean energy volatility than fluctuations in oil prices. In an out-of-sample analysis, we individually forecast the volatility of each clean energy sub-index using Lasso, Ridge, and random forest approaches. We identify sub-indices that systematically benefit from technology sector price variation (e.g. Smart Grid, Operators, Energy Management), sub-indices that benefit from oil price variation (e.g. Bio Fuel, Wind and Geothermal), while also sub-indices that show limited sensitivity to price variation in the technology and oil markets.

Název v anglickém jazyce

Forecasting of clean energy market volatility: The role of oil and the technology sector

Popis výsledku anglicky

This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various decompositions of daily realized volatility, we account for the heterogeneity across clean energy sub-sectors using the dynamic common correlated effect heterogeneous autoregressive (DCCE-HAR) model. Our findings reveal that, in the short term, price variations in technology shares are more informative for future clean energy volatility than fluctuations in oil prices. In an out-of-sample analysis, we individually forecast the volatility of each clean energy sub-index using Lasso, Ridge, and random forest approaches. We identify sub-indices that systematically benefit from technology sector price variation (e.g. Smart Grid, Operators, Energy Management), sub-indices that benefit from oil price variation (e.g. Bio Fuel, Wind and Geothermal), while also sub-indices that show limited sensitivity to price variation in the technology and oil markets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA22-27075S" target="_blank" >GA22-27075S: Předpovídání tržního rizika: Role obchodní aktivity, pozornosti a sentimentu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

ENERGY ECONOMICS

ISSN

0140-9883

e-ISSN

1873-6181

Svazek periodika

132

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

001205406600001

EID výsledku v databázi Scopus

2-s2.0-85187222094