The European Union Emissions Trading System (EU ETS) has strengthened the information flow and connection between carbon market and energy markets, which makes the carbon-energy system more ...complicated. This paper investigates information linkages and dynamic spillover effects between the carbon and energy markets. We adopt a systemic time-series approach to study connectedness in both returns and volatility in the carbon-energy system. Moreover, a rolling-windows method is used to show the dynamic features. Empirical results show that Brent oil prices play an important role in affecting carbon price changes and risks. Feedback exists from the carbon market to other energy markets, and electricity prices are shown to be the biggest information receiver in the system. It is also shown that the level of connectedness in the volatility system is substantially higher than that in the returns system. Our results can provide useful implications for policymakers to design market mechanisms and market investors to manage their portfolios.
•Information linkages and spillover between carbon and energy markets are studied.•Both return and volatility connectedness in the carbon-energy system are estimated.•Crude oil, clean energy, and coal play a pivotal role in both returns and volatility systems.•The electricity market is the main net information receiver affected by carbon market.•Spillover effects are much stronger in the volatility system than in the returns system.
This paper investigates the oil–gas relationship from a multi-scale perspective by combining the connectedness network framework and the ensemble empirical mode decomposition (EEMD) method. The ...empirical results show that the direction and magnitude of the information flow between oil and gas returns behave differently across time scales. In general, WTI and its refinery products tend to act as net information transmitters, while the United States and United Kingdom natural gas markets act as net receivers. The total spillover connectedness for the oil and gas markets, as measured by a rolling-window approach, has dynamic, volatile characteristics. The robustness of the results is shown by substituting Brent for WTI.
•Oil–gas relationship from the multi-scale perspective is examined.•We combined connectedness network framework with the EEMD method.•We find WTI and its refinery products tend to serve as net information transmitters.•The US and UK natural gas markets serve as net receivers.•The robustness of the results is shown by substituting Brent for WTI.
•Investment sentiment of renewable energy is constructed using Google search index.•Static and dynamic spillover of three is analysed in terms of return and volatility.•Spillover effect of volatility ...system was stronger than that of return system.•Impact of fossil energy market on renewable stock market was greater than sentiment.•Sentiment can explain return and volatility of renewable stock to a certain degree.
This study investigates the dynamic directional information spillover of return and volatility between the fossil energy market, investor sentiment towards renewable energy and the renewable energy stock market using the connectedness network approach. Empirical results show that the spillover effects of the volatility system are generally stronger than that of the return system, which suggest that risk transmission among the markets is more obvious. In both systems, the impact of the fossil energy market, especially crude oil, on the renewable energy stock market is greater than the impact of investor sentiment on the renewable energy stock market. This finding shows that the renewable energy stock market is closely related to the fossil energy market. Furthermore, the rolling window approach is adopted to examine the time-varying information spillover among them. The dynamic findings suggest that investor sentiment towards renewable energy can explain the return and volatility of renewable energy stock to a certain degree.
This paper investigates the impact of the North American shale gas revolution on price movement regimes in the North American and European gas markets, using the Markov regime-switching model. It ...then measures price spreads between oil and gas from 1998 to 2015 to identify the impact of the revolution on the relationship between oil and regional gas prices. The results show that the typical movement regime of Henry Hub prices changes from 'slightly upward' to 'sharply downward'. In addition, the clear seasonal effect of Henry Hub prices has disappeared after the shale gas revolution. The typical movement of national balancing point (NBP) prices has changed gradually from a 'sharply upward' regime to the alternative regimes between 'sharply downward' and 'slightly upward', tending to follow oil prices. This indicates that the shale gas revolution has had little impact on NBP price movement. Meanwhile, Henry Hub prices have decoupled from WTI prices, while NBP and Brent prices have continued to exhibit a long-term equilibrium level around which they have swung in the short time-frame since the shale gas revolution. Pertinent energy policy makers and energy market participants should pay attention to these changes and adjust their trade, production and investment strategies accordingly.
•Impact of shale gas revolution on Henry Hub and NBP price movement regime is analysed.•Impact of revolution on relationship between oil and regional gas price is identified.•Revolution changes Henry Hub movement regime, having minor impact on NBP regime.•Clear seasonal fluctuation of Henry Hub prices has disappeared since the revolution.•Henry Hub has decoupled from WTI, while NBP and Brent exhibit long-term equilibrium.
This paper constructs the returns and volatility system networks of the global new energy companies using the connectedness network approach. Then, it measures the information spillover direction and ...degree among the global new energy companies for the two systems, furthermore, exploring the rolling-windows estimating method to investigate the time-varying characteristics of the information spillover mechanism. It selects 20 large energy companies with a weighting greater than 1.24% from the NEX index and obtains the weekly stock prices for these companies from November 24, 2006 to January 4, 2019. Some new findings can be obtained: (1) The total information spillover degree among the global new energy companies is very high, especially for the volatility system. (2) The static and dynamic asymmetry indexes confirm the existence of asymmetric effect for the information transmission mechanism, which shows that the bad news does contribute more to the systemic risk of global new energy company stock market than the good news. (3) Some new energy companies act as net information transmitters, which are relatively important in the networks. These new findings could provide a reference for investors in the new energy sector to develop specific portfolios and risk management, as well as advise relevant policy makers.
•Returns and volatility system networks of global new energy companies are constructed.•Dynamic information spillover among global new energy companies is explored.•Total information spillover degree among global new energy companies is very high.•Asymmetric effect exists for the information transmission mechanism.•Some new energy companies acting as net transmitters are important in networks.
This study identifies nonlinear causality between electricity and fuel source returns based on a new multi-scale framework. The empirical results show that the causality direction between electricity ...and fuel returns behaves differently across time scales. Generally speaking, linear or nonlinear causality relationship between the electricity and gas markets exists for most time scales. The study also explores the magnitude of the feedback effects between electricity and fuel returns using the connectedness network. The results show that the degree of feedback effects between the electricity and fuel markets is relatively weak at the original data level. Across various time scales, there is positive but weak information spillover from the oil and uranium to electricity markets and reciprocal information spillover between the electricity and gas/coal markets at most time horizons. The information spillover from the gas to electricity returns is relatively strong.
•Multi-scale nonlinear causality between PJM electricity and fuel returns is explored.•Information spillover between them is examined using connectedness network.•Causality direction between them behave differently across time horizons.•Positive weak spillover from oil and uranium to electricity returns exists.•Information spillover from the gas to electricity returns is relatively strong.
•The international natural gas trade is analyzed using complex network theory.•Natural gas market integration is analyzed using the minimum spanning tree model.•Both the LNG and pipeline gas trade ...networks display scale-free distributions.•The markets in North America, Europe and Asia are not integrated.•The improvement of market integration will promote the trade globalization.
This paper analyzes the evolution characteristics of the international natural gas trade structure and the integration of the international natural gas market by using complex network theory. It is found that both the LNG and pipeline gas import and export trade networks display scale-free distributions, while the countries in the LNG trade network are linked more closely than those in the pipeline gas trade network. The markets in North America, Europe and Asia are not integrated, which indicates that a unified global natural gas market has not yet been formed. However, the degree of integration between the European and Asian markets is relative strong during 2000–2011. Finally, the integration among international natural gas markets and the inter-regional LNG trades are highly interrelated and mutually influencing.
This study investigates the impacts of driving factors for natural gas prices during the period from 1999 to 2017. A data-driven approach, namely the directed acyclic graph (DAG), is first employed ...to disclose the contemporaneous relations among natural gas, crude oil and various factors. The main results indicate that there is a stable contemporaneous causal flow from crude oil to natural gas. Unlike most of the previous research, we found a long-term equilibrium relationship between crude oil and natural gas returns when additional factors were taken into account. However, the impact of oil price returns on natural gas price volatility has decreased after the 2008 global financial crisis. Finally, storage and seasonality factors can never be ignored when analysing natural gas prices, while the impact of speculative activity on natural gas volatility is relatively weak.
•The impacts of driving factors for natural gas prices are investigated.•A DAG approach is used to disclose the contemporaneous relations among them.•There is a stable contemporaneous causal flow from crude oil to natural gas.•The impact of oil returns on natural gas volatility has decreased after 2008.•Storage and seasonality factors should be highlighted while speculation is weak.
This paper explores dynamic information connectedness effects between natural gas markets, uncertainties and stock markets in the North American and European regions for high- and low-frequency bands ...using the time-frequency connectedness network model. The empirical results suggest that the total return and volatility spillover effects in North America and Europe are mainly generated by the high-frequency band (1–12 weeks), whereas the total spillover effect for the low-frequency band (12 weeks to longer) is relatively weak. Generally, in terms of return connectedness, the North American and European natural gas markets act as information receivers to the system. With regard to volatility connectedness, the North American gas market has an impact on energy market uncertainty and economic policy uncertainty, whereas the European gas market acts as an information receiver from economic policy uncertainty. Finally, our evidence shows that both these regional gas markets are affected to a considerable extent by financial market uncertainty in both the short and long term. These new findings suggest some useful implications for investors and policy makers with various time horizons.
•Time-frequency spillover between gas, uncertainty and stock markets is explored.•Financial market, energy market and economic policy uncertainty is considered.•North American and European gas markets act as information receivers in return.•North American gas market affects OVX and EPU, while European gas market is impacted by EPU in volatility.•Both of regional gas markets are affected by financial market uncertainty in short and long run.
This study examines the dynamic characteristics of information spillover effect among economic policy uncertainty (EPU), stock and housing markets in China's first-, second- and third-tier cities. To ...measure return and volatility spillovers over time and across frequencies simultaneously, the researchers utilize the time-frequency connectedness network approach developed by Baruník and Křehlík (2018). The empirical findings suggest that return and volatility spillovers are stronger in the longer period (more than 3 months) than in the shorter period (1 to 3 months). In the short term, second and third-tier cities are net transmitters of information spillovers, while in the long term, first-tier cities, EPU, and stock markets are the net information transmitters. Furthermore, the long-term information from the EPU and stock market affect most of the real estate markets for different tier cities. Additionally, market segmentation reveals the city-specific characteristics of China's real estate market, especially the close connections between first-tier cities and the stock market. These results have important empirical implications for real estate policymakers and investors when they make related short or long-term decisions.
•Spillovers among EPU, stock and housing markets in China are investigated.•Return and volatility spillovers over time and across frequencies are measured.•Spillovers are stronger in the longer period than in the shorter period.•Long-term information from EPU and stock market affects most of real estate markets.•Close connection between first-tier cities and the stock market is obvious.
