Welcome to the InfoSci Platform
Reference Hub2
Energy Financial Risk Management in China Using Complex Network Analysis

Energy Financial Risk Management in China Using Complex Network Analysis

Guobin Fang, Yaoxun Deng, Huimin Ma, Jun Zhang, Li Pan
Copyright: © 2023 |Volume: 35 |Issue: 1 |Pages: 29
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781668478912|DOI: 10.4018/JOEUC.330249
Cite Article Cite Article

MLA

Fang, Guobin, et al. "Energy Financial Risk Management in China Using Complex Network Analysis." JOEUC vol.35, no.1 2023: pp.1-29. http://doi.org/10.4018/JOEUC.330249

APA

Fang, G., Deng, Y., Ma, H., Zhang, J., & Pan, L. (2023). Energy Financial Risk Management in China Using Complex Network Analysis. Journal of Organizational and End User Computing (JOEUC), 35(1), 1-29. http://doi.org/10.4018/JOEUC.330249

Chicago

Fang, Guobin, et al. "Energy Financial Risk Management in China Using Complex Network Analysis," Journal of Organizational and End User Computing (JOEUC) 35, no.1: 1-29. http://doi.org/10.4018/JOEUC.330249

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Effective energy financial risk management is crucial to ensure that China's economic system can remain stable. This article utilizes the quantile vector autoregressive spillover index model, complex networks, and deep learning methods to simultaneously assess both the internal and external energy financial market risks in China. Spillover effects under different market conditions are also examined. The research findings indicate that: (1) Under extreme market conditions, static total spillover values between internal and external markets exceed 70%, while under normal market conditions, they are only around 53% and 13%, respectively; (2) Crude oil and fuel oil as well as energy and stocks are important nodes in both internal and external markets; and (3) The attention-convolutional neural network-long short-term memory model outperforms the second-best performing model, and achieves an improvement of 12.9% and 21.4% in terms of mean absolute error and root mean square error, respectively; inclusion of early warning indicators leads to further improvements of 19.8% and 31.9%, respectively.