Empirical Analysis of Natural Gas Markets

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make l...

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Bibliographic Details
Other Authors: Hamori, Shigeyuki (Editor), Anderson, David A. (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
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DOAB: description of the publication
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245 1 0 |a Empirical Analysis of Natural Gas Markets 
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520 |a Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide. 
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546 |a English 
650 7 |a Economics, finance, business & management  |2 bicssc 
653 |a spillover effect 
653 |a market integration 
653 |a natural gas market 
653 |a time frequency dynamics 
653 |a BRICS 
653 |a exchange rates 
653 |a connectedness 
653 |a time domain 
653 |a frequency domain 
653 |a natural gas 
653 |a crude oil 
653 |a electricity utilities sector index 
653 |a time-frequency dynamics 
653 |a ESG 
653 |a renewable energy 
653 |a copula 
653 |a value-at-risk 
653 |a electricity 
653 |a spot 
653 |a futures 
653 |a transmission 
653 |a pipelines 
653 |a external cost 
653 |a health 
653 |a property damage 
653 |a bodily injury 
653 |a uncertainty 
653 |a insurance 
653 |a coal 
653 |a spillover effects 
653 |a dynamic approaches 
653 |a forecasting 
653 |a logistic regression 
653 |a random forests 
653 |a support vector machines 
653 |a US natural gas crises 
653 |a XGboost 
653 |a neural networks 
653 |a oil futures prices crashes 
653 |a foresting 
653 |a logistical regression 
653 |a extreme gradient boosting 
653 |a moving window 
653 |a SVAR 
653 |a oil price 
653 |a gas price 
653 |a US macroeconomic aggregates 
653 |a GDP 
653 |a CPI 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/69165  |7 0  |z DOAB: description of the publication