This study investigates the time-varying impact of energy prices on the US stock market, using monthly data from January 1986 to December 2023. The research explores the interplay between crude oil, natural gas and electricity prices and stock returns through a maximal overlap discrete wavelet transform-based quantile regression analysis. This study extensively examines alternative energy prices along with those of crude oil, demonstrating that energy shocks have asymmetric and time-varying impacts on equity returns. The findings reveal distinct supply-and-demand-driven effects. While crude oil prices exhibit an asymmetric relationship with stock returns, with rising prices negatively affecting equity markets in the long run, natural gas and electricity prices demonstrate significant supply-driven effects, mitigating the adverse impacts of crude oil price fluctuations. These results emphasize theimportance of alternative energy sources such as shale gas and renewables in moderating oil price shocks, and they highlight the role of energy price volatility in shaping market behavior.
Greening the financial system is of the utmost importance in facilitating the shift toward a net-zero carbon economy. In order to achieve this, regulators, policy makers and financial institutions are actively working toward the development of green products and implementing incentives that promote green finance. Within the Middle East and North Africa (MENA) region, various challenges are being addressed by a range of initiatives and recommendations. This study highlights that drastic reductions in emissions are imperative to effectively combat climate change. Mobilizing capital, both from the public and private sectors, is an urgent requirement in this regard. Delaying this transition can result in escalated costs and significant risks that may destabilize both our environment and our economies. Therefore, it is of the utmost importance to effectively "green" the financial system and provide the necessary support to facilitate the transition efforts of real economies.
In this paper we investigate oil market volatility prediction using a comprehensive data set of 205 variables spanning macroeconomic, financial, energy-related and sentiment indicators. We employ machine learning techniques for variable selection and dimension reduction, combining hard thresholding, soft thresholding and principal component analysis, evaluated through an out-of-sample time-series backtesting framework. The empirical findings reveal that financial variables dominate short-horizon forecasting, while macroeconomic and sentiment factors become progressively more important at longer horizons. A hybrid approach combining principal component analysis with preliminary variable filtering improves forecast accuracy, particularly for medium-term predictions. Support vector regression and random forest methods demonstrate strong performance when paired with appropriate feature selection techniques, suggesting the importance of capturing nonlinear relationships while maintaining robustness to outliers. These results indicate that effective oil volatility forecasting requires careful consideration of both the forecast horizon and the interaction between feature selection and machine learning methods
Forward contracts mitigate price risk in electricity markets, but varying pricing methodologies can create confusion and lead to prices that do not accurately reflect spot prices, potentially causing losses at maturity. Our study proposes a forward contract pricing model based on an Ornstein-Uhlenbeck stochastic process, incorporating a climate risk factor as its main contribution. In addition, we analyze the forward risk premium derived from the model and suggest an adjustment using a generalized autoregressive conditional heteroscedasticity (GARCH) model to improve future price estimates in the Colombian wholesale electricity market. This approach makes the market more complete and fairer for all participants, particularly in the case of hydroelectric generation. The proposed model has significant implications for market participants, as it provides a more realistic, and therefore fairer, price assessment, which stands as the primary contribution of this research.
In connection to public policy design for achieving net zero, we assess the industrial affordability of deep decarbonization in the International Energy Agency (IEA) member countries. Our international assessment employs a newly developed formula to calculate an industry-specific index A, equivalent to the incremental energy cost due to deep decarbonization divided by the value added. If A 6., where. is a userdefined fractional threshold, industrial affordability is said to exist. Using carbon intensity data purchased from the IEA and effective carbon rates published by the Organisation for Economic Co-operation and Development, we find that industrial affordability declines with an industry's carbon intensity, a country's effective carbon rate and its deep decarbonization target. Further, mandating net zero sans mitigation is unaffordable at a relatively low., such as 0.1, for the carbon-intensive industries of chemical products, coke and petroleum products, and basic metals. Hence,our recommendation in pursuance of the call for net zero by the United Nations is a politically feasible deep decarbonization strategy with public support enabled by gradual implementation, exemptions and subsidies.
Most of the literature on herding behavior focuses on purely financial markets. However, there remains a gap in our understanding of herding behavior in commodity futures markets across various maturities, as this strand of the literature is still limited and inconclusive. This paper extends the academic literature by focusing on the herding behavior within and across five energy futures markets, including light crude oil (West Texas Intermediate), Brent crude oil, heating oil, natural gas and Reformulated Blendstock for Oxygenate Blending (RBOB) gasoline. Our analysis shows an anti- herding effect for the full sample period (from October 2005 to April 2022) and in both bullish and bearish markets for all the futures markets investigated. However, herding behavior was detected during the 2007-9 global financial crisis and the start of the ongoing war in Ukraine. Nevertheless, no evidence of herding during the early days of Covid-19 pandemic was found for those markets. The cross-herding inves- tigation shows significant cross-market herding effects. Specifically, the natural gas market evolved inversely to the other energy commodity markets during the finan- cial crisis and during the conflict between Russia and Ukraine, and all markets moved together in the same direction during the Covid-19 crisis.
The deregulation of the European energy markets from the late 1990s onward, and the privatization of state-owned operators that had previously had monopolistic dominance in the market, had profound effects on stakeholders in the member states of the European Union. One of the most important direct effects was the entry of a number of new operators into the market, which thus increased competition in the generation and supply of gas and electricity, giving consumers a much improved choice of supplier. This study explores the energy markets of Italy and the United Kingdom and provides a comparative analysis of the operating performance of the competing energy companies therein. The innovative contribution of this paper is that we provide cluster trend analysis of the financial position and performance of the Italian and UK energy companies in the period 2008-17. Our main findings are that the overall performance of energy companies in Italy was weak, and that the incumbent successor to the state monopoly company retained a dominant position, while this was not the case in the United Kingdom. This calls into question the liberalization and transformation processes for energy companies and poses questions to the state and regulators.
Texas is the largest electricity-consuming state in the United States and leads thenation in variable renewable energy (VRE) development. It projects a huge increasein solar plant construction, despite VRE development's "cannibalization effect" onthe investment incentive for solar generation and the rising popularity of short-term VRE power purchase agreements in the United States. Our empirical investigationof short-term spot and forward solar energy sales first uses Texas's monthly whole-sale electricity market data for February 2016 to December 2021 to forecast theaverage daytime (07:00-19:00) spot energy prices and their standard deviations forforward-looking periods of one year, three years, five years and ten years. It thenapplies the price forecast results to analyze the revenue forecasts for a solar gener-ation developer's spot and forward energy sales, revealing that a new solar plant'srevenue forecast level (respectively, volatility) increases (respectively, decreases)with a short-term solar power purchase agreement's forward energy price. When theforward energy price is below (respectively, above) the spot energy price forecast,the developer's short-term power purchase agreement offer in response to a load-serving entity's VRE procurement auction announcement is for a megawatt-fraction(respectively, 100%) of the plant's energy output.
The credit risk of energy-using units (clients) is one of the main challenges in energyperformance contracting projects. We develop a credit risk evaluation model forenergy performance contracting projects that is optimized using rough set theoryand random forest interpolation. The model is used to analyze 69 420 data entriesfrom the Wind database and the Shanghai Stock Exchange for 178 listed companies(clients) with high energy consumption between 2007 and 2019. Our results showthat the long-term capital debt ratio, current ratio, net profit growth rate, payableturnover ratio, asset-liability ratio, receivable turnover ratio, degree of operating leverage, cash ratio, operating profit margin, net sales margin and degree of finan-cial leverage are key indicators closely related to the credit risk evaluation of clients.Debt-paying ability is the optimal primary indicator for the credit risk evaluation ofclients, while the long-term capital debt ratio is the optimal key indicator. These keyindicators have a low data dispersion and a relatively stable variation. Our modelfindings suggest ways to reduce the opportunistic behavior of clients and thus reducethe transaction costs of energy performance contracting projects, which could helpto increase the motivation and confidence of project participants and improve thesuccess rate of these projects
This paper analyses the technical and business cases for a hybrid floating solar and pumped hydro facility to provide secure, sustainable baseload power to local industrial and commercial users through bilateral PPA contracting and private wire connections. Based upon a realistic hydrological setting with a range of assumptions for both the floating solar and the pumped hydro installations, daily optimisation of operations to provide different levels of secure baseload PPA contracts are optimised. The research shows how the premium for baseload contracts depends upon the size of the contracts and the size of the solar installation. The premia are the opportunity costs to the hydro operators from potential sales to the wholesale market. We find that the premia are affordable to local users and that the combination of solar and pumped storage thereby enables a hydro operator to offer higher levels of secure baseload power throughout the year to local industries where the national power resources are otherwise unreliable.
In this paper, the notion of greenhouse gas aversion (GHGA) is introduced into themean-variance portfolio framework. GHGA is assumed to be a weighted sum ofthe portfolio holdings' greenhouse gas emission intensities. A new portfolio perfor-mance measure, the GHGA-tilted Sharpe ratio, is offered for greenhouse-gas-averseinvestors. While the classical Sharpe ratio may monotonically decrease with grow-ing GHGA, the GHGA-tilted Sharpe ratio has a maximum at intermediate values ofGHGA, defining an optimal GHGA-based mean-variance portfolio. The main hold-ings of such a portfolio represent promising investment leads for socially responsibleinvestors who do not want to abandon the "brown" industries altogether. An exam-ple of a GHGA-based mean-variance portfolio formed with the major constituentsof the energy sector is discussed.
The Covid-19 pandemic affected financial markets in several ways, influencing the dynamics of the relationships between asset classes. We investigate the connectedness between cryptocurrencies and international energy markets from 2018 to 2021 using the time-varying parameter vector autoregression approach. Net total directional connectedness suggests that the cryptocurrency and energy indexes had heterogeneous roles. Bitcoin and Ripple coin were the net receivers of shocks, while Ethereum switched from receiver to transmitter. The US energy market was a persistent net transmitter of shocks, while Asian energy markets were consistent net shock receivers. Pairwise connectedness reveals that cryptocurrencies can explain the volatility of the energy markets during the difficult period of the pandemic at the beginning of 2020. We provide insights for portfolio optimization and policy implications.
Within a power system, the instantaneous loss of generation or import/export via an interconnector causes a disturbance in the grid frequency. In a low inertia system, it becomes increasingly feasible to identify, size, classify and locate such grid events using a suitably sensitive and accurate network of measurement devices. Loss of generation or interconnector capacity often leads to a significant change in the price stack, leading to movement in market prices as traders adjust their positions. We demonstrate that a systematic trading strategy using an event -detection signal based on public frequency data and highly accurate measurement devices can be profitable. We also assess the sensitivity of profits to the overall event -detection and trade -execution lead times.
Within a power system, the instantaneous loss of generation or import/export via an interconnector causes a disturbance in the grid frequency. In a low inertia system, it becomes increasingly feasible to identify, size, classify and locate such grid events using a suitably sensitive and accurate network of measurement devices. Loss of generation or interconnector capacity often leads to a significant change in the price stack, leading to movement in market prices as traders adjust their positions. We demonstrate that a systematic trading strategy using an event-detection signal based on public frequency data and highly accurate measurement devices can be profitable. We also assess the sensitivity of profits to the overall event-detection and trade-execution lead times.
Following the Russian invasion of Ukraine, European countries had to limit natural gas imports from Russia, rendering electricity generation from renewable sources even more important. In this paper, first we document the increase in investment in renewable energy generation capacity over the period from 2012 to 2022 in four southern European Union countries - Portugal, Italy, Greece and Spain - and then we investigate the association between generation capacity from renewable energy sources and the stock returns of energy firms in these countries and examine whether investments in renewable energy capacity generation have had any effect on the performance of these energy firms following Russia's invasion of Ukraine. We report strong evidence that energy firms from countries with higher investments in renewable energy capacity exhibit statistically significant higher stock returns. Moreover, we find that the positive effect that a high percentage of energy generation capacity from renewable sources has on stock returns has become more pronounced after the invasion. The empirical findings suggest that country -wide investments and policies that accelerate the transition to renewable energy significantly contribute to the energy sector's resilience against security -threatening geopolitical risks.
This paper considers the valuation of swing contracts for energy markets using semianalytical proxies. Using accurate Monte Carlo or finite -difference methods is computationally expensive. Therefore, different approximations have been introduced in the literature: for example, Keppo's method replicates the swing contract by forwards and call options. We develop Keppo's method further by introducing two new schemes that build on this approach. Our first methodology adds the probability of exercising the option to the constraints. Our second approach, where the quantities depend on the actual spot level, goes one step further such that the constraints on the total amount are satisfied in a weak sense, which leads to an upper bound for the price. We perform several numerical experiments with a one -factor Lucia-Schwartz model to show the improved quality of the numerical results for different contract parameters. Both of our methods yield a more accurate calculated price than the commonly used approach while retaining its computational advantage.
Green investing is becoming popular in India as the government aims to reduce CO 2 emissions through eco-friendly projects. However, these investments in Indian green companies are new and may face price fluctuations and risks linked to other assets. To better understand these risks, we employ a dynamic conditional correlation- generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, enabling us to analyze market interdependence by estimating time -varying conditional correlation using monthly data. Our study focuses on the volatility spillover effects within Indian sustainability indexes (S&P BSE CARBONEX and S&P BSE GREENEX), which can be predicted based on information regarding the market volatility of traditional stocks, crude oil and economic policy uncertainty. We show that there is high persistence in the conditional variances for all pairs, indicating that past shocks to volatility have a long-lasting impact on future volatility. However, the effects of ARCH are found to be weak, suggesting that recent volatility innovations have a limited influence on future volatility. The DCC persistence is found to be significantly positive for all pairs, indicating a strong dynamic dependence between the volatilities of pairs of assets. Understanding these dynamics can guide both portfolio diversification for investors and the crafting of effective policies.
This study examines the net monthly returns of real estate exchange-traded funds (ETFs) through various performance evaluation models and market situations. The results reveal that these ETFs generated positive alphas and outperformed benchmark indices in absolute returns. However, their performance varied across market conditions, demonstrating both outperformance and underperformance compared to U.S. and global stocks. During the COVID-19 pandemic, real estate ETFs displayed a decline, trailing behind U.S. and global equities in both absolute returns and risk-adjusted performance. This emphasized their vulnerability during economic crises. Utilizing the Carhart four-factor model, significant exposure of real estate ETFs to the stock market was observed. Moreover, an assessment of ETF portfolio managers’ skills indicated proficiency in security selection but limited capabilities in market timing.
Pair trading on the German intraday power market is a commonly used risk-averse, heuristic trading strategy. However, due to myopic decision-making and a lack of foresight, the profit obtained is far from optimal. By comparing this strategy with the ex post optimal solution (ie, a strategy with perfect foresight), we show on a set of 15 selected days from 2020 to 2022 that the predictive information lets us generate on average more than five times as much profit by excessively buying and selling the same contracts for a trading interval of five minutes. Another problem with pair trading in practice is the possibility of unbalanced auction wins. We show that an unbiased loss of up to 10% has a negligible impact on the obtained profit. In contrast, we also show the value of frequent optimization updates by simulating strategies with only sporadic participation in the market. While this is hardly beneficial for the pairtrading strategy, the ex post optimal profit increases on average by 30% when the time between two trades is halved.
