Research

17 pages, 3345 KiB

Open AccessArticle

The Response of US Macroeconomic Aggregates to Price Shocks in Crude Oil vs. Natural Gas

by Jin Shang and Shigeyuki Hamori

Energies 2020, 13(10), 2603; https://0-doi-org.brum.beds.ac.uk/10.3390/en13102603 - 20 May 2020

Cited by 5 | Viewed by 2153

Price fluctuations in crude oil and natural gas, as important sources of energy, have a remarkable influence on our economies and daily lives. Therefore, it is extremely important to react appropriately and to formulate appropriate policies or strategies to reduce the expected negative [...] Read more.

Price fluctuations in crude oil and natural gas, as important sources of energy, have a remarkable influence on our economies and daily lives. Therefore, it is extremely important to react appropriately and to formulate appropriate policies or strategies to reduce the expected negative effects of fluctuations. However, as Kilian suggested, not all oil price shocks are similar; price increases can have diverse impacts on the real price of oil, depending on the underlying determinants of the price fluctuation. Therefore, economists, policymakers, and investors need to decompose real price shocks and evaluate the responses of macroeconomic aggregates to different types of shocks. In this study, we investigate and compare the different effects crude oil and natural gas price shocks have on US real GDP and CPI levels, utilizing a two-stage method based on a structural vector autoregression (SVAR) model proposed by Kilian. We found that a crude oil specific demand shock made larger contributions to the real price of oil than a natural gas specific demand shock did to the real price of gas, and that specific demand shocks in crude oil and natural gas markets had different effects on US CPI inflation and had similar effects on the real US GDP level. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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14 pages, 412 KiB

Open AccessArticle

Forecasting Crude Oil Market Crashes Using Machine Learning Technologies

by Yulian Zhang and Shigeyuki Hamori

Energies 2020, 13(10), 2440; https://0-doi-org.brum.beds.ac.uk/10.3390/en13102440 - 13 May 2020

Cited by 18 | Viewed by 2567

Abstract

To the best of our knowledge, this study provides new insight into the forecasting of crude oil futures price crashes in America, employing a moving window. One is the fixed-length window and the other is the expanding-length window, which has never been reported [...] Read more.

To the best of our knowledge, this study provides new insight into the forecasting of crude oil futures price crashes in America, employing a moving window. One is the fixed-length window and the other is the expanding-length window, which has never been reported in the past. We aimed to investigate if there is any difference when historical data are discarded. As the explanatory variables, we adapted 13 variables to obtain two datasets, 16 explanatory variables for Dataset1 and 121 explanatory variables for Dataset2. We try to observe results from the different-sized sets of explanatory variables. Specifically, we leverage the merits of a series of machine learning techniques, which include random forests, logistic regression, support vector machines, and extreme gradient boosting (XGBoost). Finally, we employ the evaluation metrics that are broadly used to assess the discriminatory power of imbalanced datasets. Our results indicate that we should occasionally discard distant historical data, and that XGBoost outperforms the other employed approaches, achieving a detection rate as high as 86% using the fixed-length moving window for Dataset2. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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22 pages, 2275 KiB

Open AccessArticle

Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?

by Wenting Zhang and Shigeyuki Hamori

Energies 2020, 13(9), 2371; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092371 - 09 May 2020

Cited by 8 | Viewed by 2118

Abstract

Our study combines machine learning techniques and dynamic moving window and expanding window methods to predict crises in the US natural gas market. Specifically, as machine learning models, we employ extreme gradient boosting (XGboost), support vector machines (SVMs), a logistic regression (LogR), random [...] Read more.

Our study combines machine learning techniques and dynamic moving window and expanding window methods to predict crises in the US natural gas market. Specifically, as machine learning models, we employ extreme gradient boosting (XGboost), support vector machines (SVMs), a logistic regression (LogR), random forests (RFs), and neural networks (NNs). The data set used to develop the model covers the period 1994 to 2019 and contains 121 explanatory variables, including those related to crude oil, stock markets, US bond and gold futures, the CBOE Volatility Index (VIX) index, and agriculture futures. To the best of our knowledge, this study is the first to combine machine learning techniques with dynamic approaches to predict US natural gas crises. To improve the model’s prediction accuracy, we applied a suite of parameter-tuning methods (e.g., grid-search) to select the best-performing hyperparameters for each model. Our empirical results demonstrated very good prediction accuracy for US natural gas crises when combining the XGboost model with the dynamic moving window method. We believe our findings will be useful to investors wanting to diversify their portfolios, as well as to policymakers wanting to take preemptive action to reduce losses. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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20 pages, 2733 KiB

Open AccessArticle

Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

by Tiantian Liu, Xie He, Tadahiro Nakajima and Shigeyuki Hamori

Energies 2020, 13(8), 1900; https://0-doi-org.brum.beds.ac.uk/10.3390/en13081900 - 13 Apr 2020

Cited by 15 | Viewed by 2552

Abstract

Using a fresh empirical approach to time-frequency domain frameworks, this study analyzes the return and volatility spillovers from fossil fuel markets (coal, natural gas, and crude oil) to electricity spot and futures markets in Europe. In the time domain, by an approach developed [...] Read more.

Using a fresh empirical approach to time-frequency domain frameworks, this study analyzes the return and volatility spillovers from fossil fuel markets (coal, natural gas, and crude oil) to electricity spot and futures markets in Europe. In the time domain, by an approach developed by Diebold and Yilmaz (2012) which can analyze the directional spillover effect across different markets, we find natural gas has the highest return spillover effect on electricity markets followed by coal and oil. We also find that return spillovers increase with the length of the delivery period of electricity futures. In the frequency domain, using the methodology proposed by Barunik and Krehlik (2018) that can decompose the spillover effect into different frequency bands, we find most of the return spillovers from fossil fuels to electricity are produced in the short term while most of the volatility spillovers are generated in the long term. Additionally, dynamic return spillovers have patterns corresponding to the use of natural gas for electricity generation, while volatility spillovers are sensitive to extreme financial events. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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10 pages, 819 KiB

Open AccessFeature PaperArticle

Natural Gas Transmission Pipelines: Risks and Remedies for Host Communities

by David A. Anderson

Energies 2020, 13(8), 1873; https://0-doi-org.brum.beds.ac.uk/10.3390/en13081873 - 12 Apr 2020

Cited by 5 | Viewed by 2832

Abstract

Transmission pipelines deliver natural gas to consumers around the world for the production of heat, electricity, and organic chemicals. In the United States, 2.56 million miles (4.12 million km) of pipelines carry natural gas to more than 75 million customers. With the benefits [...] Read more.

Transmission pipelines deliver natural gas to consumers around the world for the production of heat, electricity, and organic chemicals. In the United States, 2.56 million miles (4.12 million km) of pipelines carry natural gas to more than 75 million customers. With the benefits of pipelines come the risks to health and property posed by leaks and explosions. Proposals for new and recommissioned pipelines challenge host communities with uncertainty and difficult decisions about risk management. The appropriate community response depends on the risk level, the potential cost, and the prospect for compensation in the event of an incident. This article provides information on the risks and expected costs of pipeline leaks and explosions in the United States, including the incident rates, risk factors, and magnitude of harm. Although aggregated data on pipeline incidents are available, broadly inclusive data do not serve the needs of communities that must make critical decisions about hosting a pipeline for natural gas transmission. This article breaks down the data relevant to such communities and omits incidents that occurred offshore or as part of gas gathering or local distribution. The article then explains possible approaches to risk management relevant to communities, pipeline companies, and policymakers. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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14 pages, 1414 KiB

Open AccessArticle

Examination of the Spillover Effects among Natural Gas and Wholesale Electricity Markets Using Their Futures with Different Maturities and Spot Prices

by Tadahiro Nakajima and Yuki Toyoshima

Energies 2020, 13(7), 1533; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071533 - 25 Mar 2020

Cited by 8 | Viewed by 2735

Abstract

This study measures the connectedness of natural gas and electricity spot returns to their futures returns with different maturities. We employ the Henry Hub and the Pennsylvania, New Jersey, and Maryland (PJM) Western Hub Peak as the natural gas price indicator and the [...] Read more.

This study measures the connectedness of natural gas and electricity spot returns to their futures returns with different maturities. We employ the Henry Hub and the Pennsylvania, New Jersey, and Maryland (PJM) Western Hub Peak as the natural gas price indicator and the wholesale electricity price indicator, respectively. We also use each commodity’s spot prices and 12 types of futures prices with one to twelve months maturities and realize results in fourfold. First, we observe mutual spillover effects between natural gas futures returns and learn that the natural gas futures market is integrated. Second, we observe the spillover effects from natural gas futures returns to natural gas spot returns (however, the same is not evident for natural gas spot returns to natural gas futures returns). We find that futures markets have better natural gas price discovery capabilities than spot markets. Third, we observe the spillover effects from natural gas spot returns to electricity spot returns, and the spillover effects from natural gas futures returns to electricity futures returns. We learn that the marginal cost of power generation (natural gas prices) is passed through to electricity prices. Finally, we do not observe any spillover effects amongst electricity futures returns, except for some combinations, and learn that the electricity futures market is not integrated. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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19 pages, 5259 KiB

Open AccessArticle

Can One Reinforce Investments in Renewable Energy Stock Indices with the ESG Index?

by Guizhou Liu and Shigeyuki Hamori

Energies 2020, 13(5), 1179; https://0-doi-org.brum.beds.ac.uk/10.3390/en13051179 - 04 Mar 2020

Cited by 21 | Viewed by 4591

Abstract

Studies on the environmental, social, and governance (ESG) index have become increasingly important since the ESG index offers attractive characteristics, such as environmental friendliness. Scholars and institutional investors are evaluating if investment in the ESG index can positively change current portfolios. It is [...] Read more.

Studies on the environmental, social, and governance (ESG) index have become increasingly important since the ESG index offers attractive characteristics, such as environmental friendliness. Scholars and institutional investors are evaluating if investment in the ESG index can positively change current portfolios. It is crucial that institutional investors seek related assets to diversify their investments when such investors create funds in the renewable energy sector, which is highly related to environmental issues. The ESG index has proven to be a good investment choice, but we are not aware of its performance when combined with renewable energy securities. To uncover this nature, we investigate the dependence structure of the ESG index and four renewable energy indices with constant and time-varying copula models and evaluate the potential performance of using different ratios of the ESG index in the portfolio. Criteria such as risk-adjusted return, standard deviation, and conditional value-at-risk (CVaR) show that the ESG index can provide satisfactory results in lowering the potential CVaR and maintaining a high return. A goodness-of-fit test is then used to ensure the results obtained from the copula models. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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26 pages, 3756 KiB

Open AccessArticle

How Does the Spillover among Natural Gas, Crude Oil, and Electricity Utility Stocks Change over Time? Evidence from North America and Europe

by Wenting Zhang, Xie He, Tadahiro Nakajima and Shigeyuki Hamori

Energies 2020, 13(3), 727; https://0-doi-org.brum.beds.ac.uk/10.3390/en13030727 - 07 Feb 2020

Cited by 23 | Viewed by 3153

Abstract

Our study analyzes the return and volatility spillover among the natural gas, crude oil, and electricity utility stock indices in North America and Europe from 4 August 2009 to 16 August 2019. First, in time domain, both total return and volatility spillover are [...] Read more.

Our study analyzes the return and volatility spillover among the natural gas, crude oil, and electricity utility stock indices in North America and Europe from 4 August 2009 to 16 August 2019. First, in time domain, both total return and volatility spillover are stronger in Europe than in North America. Furthermore, compared to natural gas, crude oil has a greater volatility spillover on the electricity utility stock indices in North America and Europe. Second, in frequency domain, most of the return spillover occurs in the short-term, while most of the volatility spillover occurs over a longer period. Third, the rolling analyses indicate that the return and volatility from 2009 to late 2013 remained stable in North America and Europe, which may be a result of the 2008 global financial crisis, and started to fluctuate after late 2013 due to some extreme events, indicating that extreme events can significantly influence spillover effects. Moreover, investors should monitor current events to diversify their portfolios properly and hedge their risks. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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28 pages, 4109 KiB

Open AccessArticle

Connectedness Between Natural Gas Price and BRICS Exchange Rates: Evidence from Time and Frequency Domains

by Yijin He, Tadahiro Nakajima and Shigeyuki Hamori

Energies 2019, 12(20), 3970; https://0-doi-org.brum.beds.ac.uk/10.3390/en12203970 - 18 Oct 2019

Cited by 10 | Viewed by 2530

Abstract

In this paper, we investigate the connectedness between natural gas and BRICS (Brazil, Russia, India, China, and South Africa)’s exchange rate in terms of time and frequency. This empirical work is based on the approach of connectedness proposed by Diebold and Yilmaz, who [...] Read more.

In this paper, we investigate the connectedness between natural gas and BRICS (Brazil, Russia, India, China, and South Africa)’s exchange rate in terms of time and frequency. This empirical work is based on the approach of connectedness proposed by Diebold and Yilmaz, who provided an effective way of valuing how much variation in one variable is responsible for the value of other variables, and the method proposed by Baruník and Křehlík, who decomposed the results from Diebold and Yilmaz into different frequencies. We also use the rolling-window method to conduct time-varying analysis. The data used in this paper are from 23 August 2010 to 20 June 2019. We find that the natural gas price hardly influences BRICS’s exchange rates, which provides an important practical implication for policymakers, especially in oil-dependent countries. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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15 pages, 3869 KiB

Open AccessArticle

Measurement of Connectedness and Frequency Dynamics in Global Natural Gas Markets

by Tadahiro Nakajima and Yuki Toyoshima

Energies 2019, 12(20), 3927; https://0-doi-org.brum.beds.ac.uk/10.3390/en12203927 - 16 Oct 2019

Cited by 6 | Viewed by 2810

Abstract

We examine spillovers among the North American, European, and Asia–Pacific natural gas markets based on daily data. We use daily natural gas price indexes from 2 February 2009 to 28 February 2019 for the Henry Hub, National Balancing Point, Title Transfer Facility, and [...] Read more.

We examine spillovers among the North American, European, and Asia–Pacific natural gas markets based on daily data. We use daily natural gas price indexes from 2 February 2009 to 28 February 2019 for the Henry Hub, National Balancing Point, Title Transfer Facility, and Japan Korea Marker. The results of spillover analyses indicate the total connectedness of the return and volatility series to be 22.9% and 32.8%, respectively. In other words, volatility is more highly integrated than returns. The results of the spectral analyses indicate the spillover effect of the return series can largely be explained by short-term factors, while that of the volatility series can be largely explained by long-term factors. The results of the dynamic analyses with moving window samples do not indicate that global gas market liquidity increases with the increasing spillover index. However, the results identify the spillover effect fluctuation caused by demand and supply. Full article

(This article belongs to the Special Issue Empirical Analysis of Natural Gas Markets)

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