Energy Stock Research Articles

On the Effects of Physical Climate Risks on the Chinese Energy Sector

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market.

Exploring the risk dynamics of US green energy stocks: A green time-varying beta approach

Green investments play a crucial role in fighting climate change and facilitating the shift towards a low-carbon economy in line with goals of the Paris Agreement. This paper focuses on the U.S. green energy sector, analyzing its underlying risk dynamics, especially during crisis periods. In this paper, we employ a novel green energy time-varying beta (GETVB) to assess the risk profiles of U.S. green energy stocks across different market conditions. We have chosen NASDAQ Clean Edge Green Energy Index (CELS) as the U.S. green energy market index. We use Markov-switching and discrete-threshold-regression models to examine whether the GETVB varies across regimes and is affected by market stress. In particular, we examine if the market risk of these stocks itself exhibits higher volatility during regimes of stress. Our results show that the green stocks are apparently risky, with a GETVB most likely to lie between 1.2 and 1.6. However, these stocks turn out to be resilient against market volatility with the market stress having negligible impact on GETVB. This suggests an inherent robustness of green stocks against extreme market conditions. This resilience makes the U.S. green energy stocks a potential safe destination for investors during times of significant market volatility. Based on the results, we recommend that policymakers bolster support for green investments through enhanced tax incentives and subsidies, aiming to standardize the ESG metrics for increased transparency, and aligning the international financial flows with strategies that align with meeting the Paris Agreement targets.

Spillover and dependence between Chinese carbon and new energy stock markets: A cross-quantile perspective

The interplay between China's carbon emissions trading market and the new energy stock market has attracted increasing scholarly attention, given their alignment with green and sustainable development goals. This study utilizes the Cross-Quantilogram methodology to assess predictability and spillover dynamics in the Chinese carbon markets and the new energy stock markets across various quantiles. The analysis yields some significant findings: (1) Despite the small magnitude, bidirectional spillover effects are discernible between China's carbon market and the Chinese new energy market, with heterogeneous correlation among the three pilot carbon markets. (2) The cross-quantile correlation among markets exhibits varying dynamics with increasing lag orders. (3) During market downturns, the new energy market poses short-term abnormal extreme risks as a spillover source for the Guangdong carbon market, while exhibiting a stronger spillover effect on the Hubei carbon market during longer lag periods. (4) Certain sectors, such as the wind power, nuclear power, and photovoltaic sectors, stand out with a more pronounced cross-quantile in relation to the carbon market. (5) Both risk events and policy changes play a significant role in shaping market interactions. (6) Including Guangdong and Hubei carbon asset in a portfolio can effectively reduce the volatility risk of new energy stock portfolios. A valuable comprehension of the intricate interdependencies between China's carbon market and new energy assets is provided by this research, benefiting policymakers and investors in the energy and environmental sectors.

Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis

This research examines how degrees of freedom and covariance matrix configurations affect Wishart distributed matrix eigenvalue distributions. We use the energy distance metric to compare eigenvalue distributions in Identity, Diagonal, and Structured covariance matrices. Through extensive simulations, we show that degrees of freedom and covariance matrix architectures greatly affect eigenvalue dispersion and energy distance distributions. Statistical models are more reliable with smaller, more stable distributions from higher degrees of freedom. We analyze the eigenvalue distributions of NextEra Energy (NEE), Enphase Energy (ENPH), First Solar (FSLR), SunPower (SPWR), and Brookfield Renewable Partners (BEP) stocks using this analytical methodology. Daily returns and covariance matrices were calculated using daily closing prices from January 1, 2018, to January 1, 2023. Our findings support simulation studies indicating larger degrees of freedom lead to more stable energy distance distributions.The findings of this study are useful in finance, genetics, and environmental studies, where stability and variability of the covariance structure are important. The requirement for higher-dimensional settings and real-world datasets to validate the theoretical framework are our research’s constraints. This research expands Wishart distribution knowledge and provides a solid analytical foundation for data analysis and model fitting in numerous scientific fields.

Nonlinear impact of climate risks on renewable energy stocks in China: A moderating effects study

This paper assesses the physical climate risks renewable energy stocks face in China. Employing a functional coefficient model, the study investigates the nonlinear impacts and moderating effects of climate risk on the returns and volatility of these stocks. The key findings are as follows: (1) The physical climate risk levels for renewable energy stocks exhibit a normal distribution pattern. (2) Physical climate risk has a U-shaped nonlinear effect on both stock returns and volatility, where higher levels of risk enhance returns but also increase volatility. (3) Climate transition risk has a U-shaped influence on stock returns and an inverted U-shaped influence on stock volatility. Specifically, high climate transition risk not only elevates returns but also stabilizes the renewable energy stock market. Moreover, heightened climate transition risk amplifies the negative effects of oil price volatility on stock returns and increases overall stock volatility.

Attractiveness of clean energy stocks in Europe

The Ukrainian conflict has put in evidence the need to understand if oil and gas shocks increase the attractiveness of clean energy investments in Europe. If that is the case, clean energy investments are both the solution to the end of the month problem in Europe and to the end of the world problem due to climate change. Using monthly data for the period 2008–2021, we pinpoint supply and demand shocks in the oil and gas markets affecting Europe and investigate their effects on clean energy stock returns using a structural VAR model. We complete the analysis by studying the connection of these shocks with geopolitical risk. Our findings show that, clean energy stocks are positively impacted by a negative shock in the gas supply to Europe but are not significantly affected by a negative shock in global oil supply. Moreover, clean energy stocks are positively influenced both by oil-specific and gas-specific demand shocks. Finally, the favorable impact of economic demand shocks on clean energy returns lasts more in the oil price model than in the gas price model. Regarding geopolitical risk, we find a strong connectedness, particularly with shocks in the European gas market. These findings imply that, in Europe, clean energy is indeed a substitute to oil and gas in times of crisis. We are then likely to observe an increase in clean energy returns as prices of fossil sources spike.

Quantile and time-frequency spillover between rare earth and its interlinked markets: A geopolitical perspective

Underlying vulnerability of geopolitical risk for rare earth markets and its interlinked markets motivated this research is to explore their nexus by using different connectedness models. In doing so, the current study employed three diversified set of methodologies namely, quantile vector autoregression, BK framework, and cross-quantilogram to investigate connectedness structure across different market conditions. The dataset of this study spanning from March 29, 2018 to April 28, 2023, covering several turmoil periods. Surprisingly, the empirical findings reveal significant evidence of connectedness between all markets. Notably, in lower quantile, geopolitical uncertainty, rare earth, and green energy stocks are the major transmitters, while oil is the major receiver. At the same time, rare earth, clean energy, technology, natural resource markets, and all green energy tokens except carbon are the significant transmitters in the upper quantile. In contrast, BK measures demonstrate that total frequency connectedness is more significant in the short and long run. While, cross-quantilogram results reveal that connectedness is more pronounced in the long run for rare earth, clean energy, technology, and mining stocks. Specifically, it is important to note that spillover effect are more substantial and differ across different crisis episodes. In addition, large swings in the connectedness index between geopolitical risk and chosen markets is observed after the escalation of the Russia-Ukraine War. The empirical results offer several important implications for investors and policymakers to apply effective risk management techniques and safeguard investments especially during economic turbulence.

A Simple Physics-Based Model of Growth-Based Economies Dependent on a Finite Resource Base

Mainstream economics describes virtual wealth with theory that is at odds with the physical laws that govern a nation’s physical resources. This confusion fundamentally prevents the realization of “sustainable” economies. The relation between debt and the metabolism of a country (measured by GDP or power consumption) appears to follow a diffusion relationship, in which debt encodes the temporal evolution of an economic potential. Debt enables the production of resources and the realization of a country’s economic wealth potential (the sum of its environmental, geological, and societal endowments, among others). Any economic scheme dependent on finite stocks of free energy for growth must eventually collapse, and as such cannot be considered sustainable. Our simple debt–diffusion model is shown to closely match the trajectories of 44 different economies.

Spillover relationships between international crude oil markets and global energy stock markets under the influence of geopolitical risks: New evidence

The VAR-LASSO connectedness method and VAR-X-LASSO connectedness method are employed in this study to explore the intricate spillover relationships between international crude oil markets and global energy stock markets while also examining the impact of geopolitical risks on these spillover relationships. By comparing the connectedness indices derived from the VAR-X-LASSO connectedness method and the VAR-LASSO connectedness method, this paper yields some intriguing empirical findings. First, the net transmitter of systemic shocks mainly appears in energy stock markets within developed countries. Second, we observed the net spillover direction from energy stock markets in most developed countries, especially those in the United States, Canada, France, Italy, Norway and Spain, to international crude oil markets, and the net spillover direction from international crude oil markets to energy stock markets in most developing countries. Third, geopolitical risks have been observed to strengthen the unidirectional spillover intensity from international crude oil markets to energy stock markets, and their influence intensified after 2015. However, there is minimal influence of geopolitical risks on the unidirectional spillover effects from energy stock markets to international crude oil markets.

Extreme risk spillovers in international energy markets: New insights from multilayer networks in the frequency domain

This paper constructs multilayer risk spillover networks in the frequency domain, containing short-, medium-, and long-term layers, to explore extreme risk spillovers in international energy markets at different frequency bands. We analyze the topology properties of multilayer networks at the system, region, and country levels. Based on daily data from 20 national energy stock markets spanning 2006–2023, the empirical results show that: (1) The overall topology of multilayer networks, including the edge structure and spillover strength structure, exhibit frequency heterogeneity. With the onset of a major crisis, the frequency heterogeneity of the network topology increases significantly, and international energy risk spillovers tend to concentrate in fewer layers (i.e., over one or two frequency bands). (2) Long-term risk is primarily responsible for the extreme risk spillover behavior between energy markets. (3) Compared to short- and medium-term spillovers, long-term spillovers are more crisis-sensitive. Furthermore, long-term spillovers within different regions vary in their sensitivity to local or global crises. (4) As market conditions fluctuate, the key players driving the short-term layer change, whereas those driving the medium- and long-term layers remain relatively stable. Overall, our findings provide new insights for policymakers and investors to prevent international energy systemic risks and optimize investment strategies.

Carbon credit sentiments and green energy stocks

ABSTRACT This paper provides a new perspective on the role of climate attention in the media for returns of green energy stocks. Making use of a data set that includes various measures of sentiments and media attention in newspapers and social media related to 1) carbon credit and 2) stock markets, we disentangle the drivers of green stock returns. Our results for various green stock indices suggest that green stock returns are not only driven by stock-related news but also driven by sentiment related to carbon credit news. We also show that the underlying news source (social media vs. newspapers) matters. News related to uncertainty around carbon credit in conventional newspapers is accompanied by decreasing returns. In social media, future-oriented pessimistic emotions relate negatively and recent positive sentiment relates positively to green stock returns.

Middle East conflict and energy companies: The effect of air and drone strikes on global energy stocks

The recent April 2024 Israel-Iran conflict had a notable impact on global energy markets. Returns on the top ten global energy stocks indicate investor apprehension up to 10 days before the event started on April 13, 2024. Energy stocks had significant negative returns on the event day itself, with positive CAARs pre-event and negative CAARs post-event. The dynamic market response highlights the heightened uncertainty for energy firms due to regional instability and potential supply chain interruptions, emphasizing the critical role of geopolitical events in shaping investor sentiment and the financial performance of energy firms.

Quartile risk dependence between clean energy markets and the U.S. travel and leisure index

ABSTRACT We employed the Cross-quantilogram method for the first time to assess the cross-quantile risk relationship among the clean energy market and the Dow Jones U.S. Travel & Leisure Index during the period from 2014 to 2023. This investigation aimed to explore the asymmetric nature of the risk-dependence structure. Our findings reveal that, under market stability conditions, the U.S. Travel & Leisure index exhibited the highest correlation with all clean energy stocks. However, the risk correlation with the NASDAQ OMX Geothermal, NASDAQ OMX Solar, and NASDAQ OMX Wind Indices significantly decreased during longer investment horizons and extreme quantiles. Notably, in the extreme tail, the correlation between specific clean energy markets and the U.S. Travel & Leisure index displayed heterogeneity. Our results have new practical implications for policymakers and investors who need to capture the risk connection among clean energy indices and the U.S. Travel and Leisure sector.

Do climate change risks affect the systemic risk between the stocks of clean energy, electric vehicles, and critical minerals? Analysis under changing market conditions

This paper analyzes the impact of climate change risks—specifically from natural disasters, global warming, international summits, and U.S. climate policy—on the return connectedness (systemic risk) of a network consisting of the stocks of clean energy, electric vehicles, and critical minerals in bear, bull, and normal market conditions. Employing a quantile vector autoregression (QVAR) approach, we find significant temporal variations in the total connectedness index, with notable spikes during the COVID-19 pandemic and the Russia-Ukraine war. Total connectedness is higher but less variable under bear and bull market conditions. Concerns about global warming has a positive and significant impact on systemic risk during bear and normal market conditions while international summits have a negative impact during normal market conditions. However, the effects of these climate change risks are small in magnitude. Economic policy uncertainty and stock market volatility have the largest positive impacts on systemic risk under most market conditions. Our results reveal a nonlinear (inverted U-shaped) relationship between variable importance and systemic risk quantile, showing that the impact on connectedness is largest in magnitude under normal market conditions.

Drought and energy stock markets in the United States

Abstract Amid increasing concerns about the impact of climate change on financial asset pricing, this paper investigates how drought risk affects stock returns in the U.S. energy sector over a thirty-year period (1990–2019). To this end, we ranked energy companies based on the long-term trend of the Palmer drought severity index (PDSI) to compare stock performance between groups experiencing the most significant increases and decreases in drought risk. Our analysis indicates that firms located in drought-prone regions have lower stock returns than those in regions with decreasing drought risk, particularly since 2010. This finding is noteworthy, as the average PDSI trend gap between regions at high risk of drought and those at low risk has widened during this period. We further divide our sample of energy companies into two subsectors-fuel production and electric utility-and find that the stock prices of both subsectors have been negatively affected by drought risk since 2010. The study underscores the energy sector’s dependency on water availability, suggesting novel implications for policymakers and investors in assessing the financial impacts of drought risks in their climate-related decision-making.

Impact of firm characteristics and country-level governance on global energy stocks during crises

Two successive crises have raised the need to understand the dynamics of the global energy sector during these crises. This study examines the effects of the Russia-Ukraine War (RUW) and the COVID-19 pandemic on the energy sector across developed and emerging markets. Additionally, it examines the role of firm- and country-specific variables in stimulating energy stocks. We employ a rigorous event study methodology to analyse data from 387 firms spanning 44 countries and reveal that the RUW exerted a more significant global impact on the energy sector compared to the pandemic, both before and after the event day. Notably, emerging markets displayed early reactions before the global announcement of the COVID-19 pandemic. Remarkably, the American markets were hardest hit by the pandemic, while Asia and the Pacific, Europe, and the Middle East and Africa fared relatively better. Intriguingly, our cross-sectional analysis demonstrates that firm-specific characteristics (leverage and firm size) and the country-level governance variable, corruption control, influenced market performance during these events. Conversely, country-level development variables demonstrated minimal impact. This study enhances the understanding of how global crises uniquely impact the global energy sector. It also demonstrates how country-level governance variables could influence stock market returns.

How do the Green Energy Stocks React to Green Bond Issuances?

Achieving sustainable development is one of the main issues at the global level and both public and private sector enterprises need to make large – scale investments to fight against climate change. In this respect, green bonds gain importance to raise money for environmentally – friendly projects, especially clean energy. Proceeds from green bonds are earmarked towards financing of investments that have positive environmental impacts. This paper explores the relationship among green bond issuances and stock market reaction with special focus on renewable energy firms. Herein, through a dataset of green bond issuance announcements worldwide by 46 unique firms over the period from 2014 to 2023, we investigate how the share prices respond to such announcements using event – study methodology. From the empirical evidence of the downward stock price movements, we suggest that investors react negatively to the announcement of green bond issuances. In other words, we find significant and negative cumulative average abnormal returns (CAAR) across all the event windows except in the window of [0, 10], meaning that our findings are robust to several alternative event windows. Further, we determine that the share price response, in general, does not differ depending on the use of green bond proceeds and the years.

An improved deep temporal convolutional network for new energy stock index prediction

Accurate prediction of the stock indexes in the new energy market is of significant importance to both investors and policymakers. However, in response to the volatility and uncertainty characteristic of the new energy market, most scholars currently focus on training prediction methods using features from a single time scale, which cannot capture the fluctuations of new energy stock indexes under different time scales. Therefore, in this paper, a hybrid deep learning model Multi-kernel Parallel TemporalNet (MKP-TemporalNet) is proposed for predicting new energy stock indexes. This model initially incorporates an attention mechanism to calibrate the feature importance of multivariate time series dynamically, and then combines the characteristics of improved Temporal Convolutional Networks (iTCN) and Bidirectional Gated Recurrent Units (BiGRU) to enhance prediction accuracy effectively. In particular, the novelty of the iTCN lies in the development of a multi-kernel parallel convolution structure within a residual layout at the core of the temporal convolution module, to address the low efficiency of traditional TCN's single kernel convolution in extracting temporal features from input sequences at different time scales. Results from evaluating MKP-TemporalNet against several popular machine learning models on six new energy stock indexes confirm its predictive effectiveness in the new energy sector.

How do climate policy uncertainty and renewable energy and clean technology stock prices co-move? evidence from Canada

AbstractThis work probes the dynamic co-movement between the Climate Policy Uncertainty Index (CPU) and the Renewable Energy and Clean Technology Index (RECT) employing the novel wavelet power spectrum (WPS) and wavelet coherence (WC) approaches for monthly data between 2013 and 2022. Using the wavelet approach enables us to observe the causality direction from both time and frequency dimensions and also to help detect the causal linkage in the short-medium and long-term horizons. This is the first study aiming to perform this relationship from both time and frequency dimensions. Remarkably, findings reveal that: i) CPU seems only volatile in 2019 and 2021 in the short run; (ii) there was significant volatility in the RECT in the short and long terms (SLT) between 2018 and 2022; (iii) RECT significantly caused the CPU between 2014 and 2018; iv) after 2019, CPU started to cause RECT in the short and medium terms (SMT).

Power Interconnectivity for the Caribbean Area

Caribbean area is considered a renewable energy hub when sustainable energy sources are considered. Renewable energy sources include solar, wind, hydropower, bioenergy, and geothermal energy. The PWRLINKCAR (Power Link Caribbean) project proposes the union of the electrical markets of Greater and Lesser Antilles for promoting the use of renewable energies. In this way, the integration of the electricity markets reduces energy stocks and electricity prices. Energy link geographical analysis is performed regarding the present infrastructures and the depth of the trajectory where submarine cable will be placed. Therefore, the cost of the power link is directly related to the depth of the ocean considering submarine cable performance and the installing process. The benefits of the interconnectivity are social, political, economic, and technical.