On the relationship between oil market and European stock returns

This paper investigates the dynamic relationship between the oil market and European stock market returns using monthly data from May 2007 to April 2022 for 27 European Union member countries. A novel approach is adopted by using the time-varying Granger causality test and the structural vector auto-regression model to examine the causal links. Empirical results reveal strong evidence of time-varying causation between the variables, considering the oil market from both the supply-side and demand-side perspectives. In light of these findings, numerous policy considerations emerge, including refining risk management strategies for investors, reformulating economic and energy policies, the potential impact on monetary policy decisions, the need for ad hoc market regulations, facilitating investor education initiatives, promoting international cooperation, and advancing the transition to sustainable energy sources.

Green stocks, crypto asset, crude oil and COVID19 pandemic: Application of rolling window multiple correlation

The COVID-19 pandemic disrupted almost all spares of global social, psychological, and economic life. The emergence of various variants and corresponding variations in daily infection asymmetrically influenced economic indicators. This study extends the existing literature by exploring the hedging potential of crude oil, carbon efficiency index of green firms, and bitcoin during this pandemic. This objective is realized by employing the recently advanced rolling window multiple correlation of Polanco-Martínez (2020). This approach is based on the new p-value corrected method, which has advantages over other correlation methods. The sample observations are based on daily data from 1/22/2020 to 12/20/2021. In the bivariate case, we find a significant positive correlation between COVID-19 and CEI, while a negative impact is observed between COVID-19 and WTI. Similarly, we observe a significant and nonlinear association between COVID-19 and BTC. However, our findings show positive and significant correlations among variables in the multivariate case. The overall findings show that CEI and BTC can be safe havens for investors during this worse pandemic. The study's robust findings can be used to derive important policy implications worldwide during the COVID-19 pandemic.

Energy markets responds to Covid-19 pandemic

This paper aims to detect the sensitivity of the Oil market to different Covid-19 outbreak periods. To test its haven propriety, and its sensitivity to the study phase, our research investigates the Covid-19 indicators explanatory power. Using the OLS regression, our results reveal that new pandemic wave announcement declines the Oil market demand. It doubts its safe-haven property. In parallel, we detect that this market responds to the determining factors of the Covid-19 Pandemic. At this level, we found that the number of the reported cases has lost its explanatory power since the emergence of the second pandemic wave. On the contrary, mortality following this virus has become a significant explanatory factor.

An energy system optimization model accounting for the interrelations of multiple stochastic energy prices

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein-Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10479-021-04229-3.

The nexus between black and digital gold: evidence from US markets

In the context of the debate on cryptocurrencies as the 'digital gold', this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments.

The dynamics of volatility spillovers between oil prices and stock market returns at the sector level and hedging strategies: evidence from Pakistan

This study investigates the transmission of volatility between OPEC-oil and sector stock returns in Pakistan. The issue of volatility spillovers across the oil and sector stocks is a crucial part of risk management and portfolio designs, as all firms are not expecting to be equally affected by changes in oil price. Empirically, we estimate a bivariate VAR-GARCH model using daily data sampled from January 1, 2003 to December 29, 2017. We also analyze the optimal weights and hedge ratios for oil-stock portfolio holdings based on our model results. Our findings reveal that negative and significant spillover effects from the oil market to agriculture, energy, and machinery sector stocks are present. However, our findings show that volatility spillover effects are insignificant from stock returns to oil. The findings of the study illustrate that development of stock market will motivate highly polluting firms to invest more in renewable and clean energy, which will help reduce carbon emissions.