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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| HerausgeberIn: | |
|---|---|
| Sonstige: | |
| Year of Publication: | 2020 |
| Language: | English |
| Physical Description: | 1 electronic resource (200 p.) |
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| LEADER | 03120nam-a2200901z--4500 | ||
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| 005 | 20231214133135.0 | ||
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| 035 | |a (CKB)5400000000042811 | ||
| 035 | |a (oapen)https://directory.doabooks.org/handle/20.500.12854/69165 | ||
| 035 | |a (EXLCZ)995400000000042811 | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Hamori, Shigeyuki |4 edt | |
| 245 | 1 | 0 | |a Empirical Analysis of Natural Gas Markets |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (200 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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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. | ||
| 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 | ||
| 776 | |z 3-03943-136-6 | ||
| 776 | |z 3-03943-137-4 | ||
| 700 | 1 | |a Anderson, David A. |4 edt | |
| 700 | 1 | |a Hamori, Shigeyuki |4 oth | |
| 700 | 1 | |a Anderson, David A. |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:43:49 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338075430004498&Force_direct=true |Z 5338075430004498 |b Available |8 5338075430004498 | ||