Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/317687 
Year of Publication: 
2024
Citation: 
[Journal:] Business Economics and Management (JBEM) [ISSN:] 2029-4433 [Volume:] 25 [Issue:] 3 [Year:] 2024 [Pages:] 437-454
Publisher: 
Vilnius Gediminas Technical University, Vilnius
Abstract: 
Extensive analysis of intertwinement with other industries caused the energy sector to gain momentum in the recent economic literature. This paper aims to create an indicator that captures the impact of financial stability for energy companies on all other industrial groups. To this end, we use daily data from 2007 until the end of 2021 to compute financial stability metrics for all European companies from the STOXX 600 index. The main contribution of our study is to harness the neural network forecasting power to predict extreme levels of this impact. We motivate this choice with evidence from the literature that documents the improved performance of these methods in predicting crises. Our methodological approach also employs an outlier detection algorithm based on copula (COPOD) to identify situations when the energy sector substantially impacts other industries and develop a framework to predict out-of-sample situations. We found evidence that the Deep Renewal model has superior forecasting accuracy to the standard Croston model. The main conclusion is that the design of this methodological framework allows authorities to monitor the impact of shocks produced by the energy sector on financial stability at the European level and undertake strategic management actions.
Subjects: 
COPOD
Deep Renewal process
energy
European companies
extreme levels
financial stability
JEL: 
D53
Q40
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.