2018
DOI: 10.3390/su10124705
|View full text |Cite
|
Sign up to set email alerts
|

Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model

Abstract: In this paper, the time-varying volatility feedback of nine series of energy prices is researched by employing the time-varying parameter stochastic volatility in mean (TVP-SVM) model. The major findings and conclusions can be grouped as follows: Significant differences exist in the time-varying volatility feedback among the nine major energy productions. Specifically, crude oil and diesel's price volatility has a remarkable positive time-varying effect on their returns. Yet the returns, for natural gas and mo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
5
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 36 publications
0
5
0
Order By: Relevance
“…We focused on oil price, exchange rate and gross trade volume. The negative and positive impact of crude oil price fluctuation on its profitability also proved for Azerbaijan (Jiang et al, 2018). The dramatic reduction in oil prices paved the way to prepare for the new period -post-oil period and led to reforms (Hesami et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…We focused on oil price, exchange rate and gross trade volume. The negative and positive impact of crude oil price fluctuation on its profitability also proved for Azerbaijan (Jiang et al, 2018). The dramatic reduction in oil prices paved the way to prepare for the new period -post-oil period and led to reforms (Hesami et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…The resulting conclusion about the direct and negative impact of oil prices on the profitability of the banking sector is consistent with the conclusions [43] for Turkey. When conducting stress testing of the banking sector, the state regulator should take into account the impact of global economic and geopolitical events on world oil prices [44], and provide options for an adequate macroprudential response to possible risks in order to prevent threats to banking stability [68], which, in turn, is a necessary condition for the profitability of responsible banking [10], which ensures the achievement of the Sustainable Development Goals through a balance between economic development and the interests of society.…”
Section: Discussionmentioning
confidence: 99%
“…The implementation of the concept of sustainable development in the state policies of the countries of the world community provides for the decarbonization of the economy, including the transition to renewable energy sources [43]. Such processes form the risks of economic changes for countries exporting energy resources, including oil [44], which affects the interests of all economic agents [45], including the banking sector. At the level of an individual, economic changes can act as factors of negative emotions, interfere with a person's well-being [46], thus jeopardizing the achievement of the sustainable development goals.…”
Section: Methodsmentioning
confidence: 99%
“…Machine learning, proposed by Samuel (1959), has been widely used in the fields of energy and economics with diverse applications, for example, the optimization of energy inputs Abdelaziz et al, 2016;Nabavi-Pelesaraei et al, 2017;Ali & Abd Elazim, 2018;Khanali et al, 2021), the investigation of energy efficiency , forecasting energy commodity prices (Ding, 2018;Yu et al, 2017;Zhang et al, 2015), forecasting energy demand (Yang et al, 2014;Panapakidis and Dagoumas, 2017;Ou et al, 2020;Haque et al, 2021). Popular ML techniques in the relevant literature include applied artificial neural networks (ANN) (Olanrewaju et al, 2013;Kunwar et al, 2013); deep learning (Lago et al, 2018;Peng et al, 2018), support vector machine (SVM) (Papadimitriou et al, 2014;Zhu et al, 2016;Jiang et al, 2018), decision trees (Bastardie et al, 2013;Zhao and Nie, 2020) and ensemble methods (Ghasemi et al, 2016;Mirakyan et al, 2017).…”
Section: Forecasting Transport Energy Demand Using ML Techniquesmentioning
confidence: 99%