The reaction of the metal and gold resource planning in the post-COVID-19 era and Russia-Ukrainian conflict: Role of fossil fuel markets for portfolio hedging strategies

The prime objective of this article is to examine the policy-making role of metal markets, gold resources, and clean energy markets in the post-COVID-19 era and the Russia-Ukrainian military conflict. In doing so, we analyze the role of fossil fuels, clean energy, and metals markets, considering the military conflict in Ukraine in 2022. The study employs event study methodology (ESM), Total connectedness index (TCI), and network analyses. The results indicate that natural gas and clean energy prices are less affected by conflict in the aftermath of an invasion than traditional energy and metals markets. In addition, we observe an increase in the TCI in the energy markets during announcement days. The TCI of the metals market is greater than that of the energy market. According to network connectivity, the key asset class transmitters of the shock in Europe are the Geopolitical index (GPR), gold, and the clean energy stock index (ERIX). The U.S. markets are less affected by the situation in Ukraine. The average hedge suggests that the optimal hedge differs from one market to the next, with fossil fuels and renewable energy, respectively, being more hedge effective and reducing risk by an average of around 0.80 and 0.59, given their ability to function as a hedging instrument.

Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis.

We provide the first empirical study on the role of panic and stress related to the COVID-19 pandemic, including six uncertainties and the four most traded cryptocurrencies, on three green bond market volatilities. Based on daily data covering the period from January 1, 2020 to January 31, 2022, we combine Diebold and Yilmaz's (2012, 2014) time domain spillover approach and Ando et al.'s (2022) quantile regression framework to investigate the time-frequency spillover connectedness among markets and measure the direction and intensity of the net transmission effect under extreme negative and positive event conditions, and normal states. We further provide novel insights into the green finance literature by examining sensitivity to quantile analysis of the net transfer mechanism between green bonds, cryptocurrencies, and pandemic uncertainty. Regarding the network connectedness analysis, the results reveal strong net information spillover transmission among markets under the bearish market. In extremely negative event circumstances, the MSCI Euro green bond acts as the leading net shock receiver in the system, whereas COVID-19 fake news appears as the largest net shock contributor, followed by BTC. According to sensitivity to quantile analysis, the net dynamic shock transfer mechanism is time-varying and quantile-dependent. Overall, our work uncovers crucial implications for investors and policymakers.

The role of political risk, uncertainty, and crude oil in predicting stock markets: evidence from the UAE economy

This study examines how the determinants of the political risk factor affect the forecasting performance of the United Arab Emirates’ stock market during the COVID-19 pandemic. The empirical investigations of this goal are conducted through using new machine learning models including a linear regression, an artificial neural network, a random forest, an extreme gradient boosting (XGBoost), and a light gradient boosting (LightGBM). We also use a game theory-based model the SHapley Additive explanation (SHAP) interpretation framework to evaluate the most important features for predicting the UAE’s stock market prices. The experimental results show that the LightGBM and XGBoost outperform the traditional machine learning models such as the linear regression and produce a holistic probability distribution over the entire outcome space, which helps quantify the uncertainties related to the effect of the COVID-19 pandemic on predicting the UAE’s stock market. The novel SHAP algorithm also provides insights in interpreting the complex “black box” architecture of the machine learning models to help predict this country’s stock prices. The results provide important implications for the political risk management in periods akin to the COVID-19 pandemic. [ FROM AUTHOR] Copyright of Annals of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Dynamic spillover effects and connectedness among climate change, technological innovation, and uncertainty: Evidence from a quantile VAR network and wavelet coherence

This study investigated time-frequency transmission and connectedness among green indexes dealing with clean energy, environmental preservation, and technological innovation and information uncertainty related to economics news, the COVID-19 pandemic, and Twitter usage. First, by employing a quantile vector autoregression framework, we assessed how the static and dynamic connectedness between markets switched across a broad spectrum of market conditions, particularly bear, normal, and bull markets. Second, we examined the dynamics of the co-movement between green financial markets and the level of uncertainty in the time-frequency domain using novel vector wavelet coherence analysis. Our analysis yielded the following major findings: Statically, high spillover and volatility effects existed among the indexes;dynamically, evidence of very strong connectedness between climate change indexes was reported at extreme lower and extreme upper quantiles. The findings further exhibit the switching of climate change between net contributing/net receiving shock behavior during the pandemic. Technological innovation, the COVID-19 pandemic, and uncertainty have strong effects on climate change markets as revealed by multiple, quadruple, and vector wavelet analysis. Implications for both environmental investors and policymakers were revealed.

Dynamic causality interplay from COVID-19 pandemic to oil price, stock market, and economic policy uncertainty: evidence from oil-importing and oil-exporting countries.

In this study we examine the time-varying causal effect of the novel COVID-19 pandemic in the major oil-importing and oil-exporting countries on the oil price changes, stock market volatilities and the economic uncertainty using the wavelet coherence and network analysis. During the period of the pandemic, we explore such relationship by resorting to the wavelet coherence and gaussian graphical model (GGM) frameworks. Wavelet analysis enables us to measure the dynamics of the causal effect of the novel covid-19 pandemic in the time-frequency space. Regarding the findings displayed herein, we first found that the COVID-19 pandemic has a severe influence on oil prices, stock market indices, and the economic uncertainty. Second the intensity of the causality effect is stronger in the longer horizon than in the short ones, suggesting that the causality exercise continues. Our findings also provide evidence that the COVID-19 pandemic and oil price changes in oil-importing countries mirror those in oil-exporting countries and vice versa. Further, the COVID-19 pandemic has a profound immediate time-frequency effect on the US, Japanese, South Korean, Indian, and Canadian economic uncertainties. A better understanding of oil and stock market prices in the oil-importing and oil-exporting countries is vital for investors and policymakers, specially since the novel unprecedented COVID-19 crisis has been recognized among the most serious ever happened. Thus, the findings suggest that the authorities should strongly take efficient actions to minimize risk.

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning.

This study aims to predict oil prices during the 2019 novel coronavirus (COVID-19) pandemic by looking into green energy resources, global environmental indexes (ESG), and stock markets. The study employs advanced machine learning, such as the LightGBM, CatBoost, XGBoost, Random Forest (RF), and neural network models. An accurate forecasting framework can effectively capture the trend of the changes in oil prices and reduce the impact of the COVID-19 pandemic on such prices. Additionally, a large dataset with different asset classes was used to investigate the crash period. The research also introduced SHapely Additive exPlanations (SHAP) values for model analysis and interpretability. The empirical results indicate the superiority of the RF and LightGBM over traditional models. Moreover, this new framework provides favorable explanations of the model performance using the efficient SHAP algorithm. It also highlights the core features of predicting oil prices. The study found that high values of GER and ESG lead to lower crude oil prices. Our results are crucial for investors and policymakers in promoting climate change mitigation and sustained economic prosperity through green energy resources.