A wavelet analysis of dynamic connectedness between geopolitical risk and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

ESG practices mitigating geopolitical risks: Implications for sustainable environmental management

As climate change and geopolitical conflicts intensify, understanding how geopolitical risks affect companies prioritizing Environmental, Social, and Governance (ESG) practices is crucial. This study investigates the dynamic relationship between global geopolitical risks and the performance of Environmental, Social, and Governance (ESG) and non-ESG companies, particularly their influence on green markets. Utilizing a robust methodological framework, including the dynamic time-varying parameters vector autoregression (TVP-VAR) model, and causal impact modeling, we analyze daily financial data from 2021 to 2024. The results reveal a substantial negative impact of geopolitical risks on non-ESG companies, contrasting with the resilience of ESG-committed counterparts. This suggests that ESG-committed companies demonstrate better resilience against geopolitical risks, emphasizing the protective role of ESG practices amid uncertainties. Additionally, the inclusion of ESG companies in green markets diminishes the severity of the negative impact of geopolitical risks, underlining the transformative role of ESG commitment in shaping investor behavior towards sustainable investments. Our findings offer insights for policymakers and investors navigating geopolitical risks and ESG performance, with a focus on environmental management, and provide guidance for effective risk mitigation and investment policies to enhance environmental sustainability.

Welfare influences of green energy volatility in Vietnam: new evidence from an extended TVP-VAR approach

We present a study utilizing TVP-VAR - a time-varying parameter vector autoregression combined with an extended joint connectedness approach to examine the interrelationships between welfare and green energy volatility in the system of variables, including economic growth measured by gross domestic product (GDP), rural income, urban income, and renewable energy consumption. We characterize the connectedness of these variables from 19954 to 2019 in Vietnam. Net total directional connectedness of renewable energy consumption and GDP, rural-urban income suggests that both GDP and rural income have constantly been recipients of net contagion shocks, and urban income is a net critical receiver in the period 2001-2010. Renewable energy consumption consistently behaves as an important transmitter of shocks. Pairwise connectedness reveals that renewable energy consumption demonstrates a consistent shock-transmitting behavior for other variables. Renewable energy consumption could be either a transmitter or a receiver of shock from GDP, depending on the period. Our findings are critical since they will help policymakers formulate appropriate policies for reducing the vulnerabilities of these variables and minimizing the spread of risk and uncertainty among them, then improving the individuals' welfare and obtaining ecological sustainability in the case of developing countries.

Geopolitical risk, economic policy uncertainty, and dynamic connectedness between clean energy, conventional energy, and food markets

The global financial markets suffered unprecedented shocks, leading to significantly increased uncertainty in the markets due to various economic and financial recessions and geopolitical tensions, resulting in substantial fluctuations in market prices. Therefore, this paper aims to identify the response of the clean energy, conventional energy, and food markets to economic uncertainty and political tension while considering the influence of numerous crises and political conflicts. To achieve this, we employ the DCC-GARCH-based connectedness approach and the quantile-on-quantile model on monthly data spanning from May 2008 to June 2023. The results provide evidence of the sensitivity of dynamic volatility spillovers between financial assets to GEPU and GPR during major economic and financial crises and geopolitical events. Notably, this sensitivity increases significantly during the global financial crisis (GFC), the European debt crisis, Brexit, the US presidential election, the COVID-19 pandemic, and the Russian-Ukrainian war. However, the investigation of the tail dependence structure reveals that the relationship between uncertainties and total volatility connectedness across various market conditions appears to be asymmetric and heterogeneous. Our findings assist policymakers and green investors in designing the most effective policies to mitigate the impact of uncertainties on both conventional and green investments. This is achieved through insightful knowledge about the primary drivers of contagion among these indices, all while not compromising sustainability goals.

Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models

This article investigates the performance of three models - Autoregressive Integrated Moving Average (ARIMA), Threshold Autoregressive Moving Average (TARMA) and Evidential Neural Network for Regression (ENNReg) - in forecasting the Brent crude oil price, a crucial economic variable with a significant impact on the global economy. With the increasing complexity of the price dynamics due to geopolitical factors such as the Russo-Ukrainian war, we examine the impact of incorporating information on the war on the forecasting accuracy of these models. Our analysis shows that incorporating the impact of the war can significantly improve the forecasting accuracy of the models, and the ENNReg model with the inclusion of the dummy variable outperforms the other models during the war period. Including the war variable has enhanced the forecasting accuracy of the ENNReg model by 0.11%. These results carry significant implications regarding policymakers, investors, and researchers interested in developing accurate forecasting models in the presence of geopolitical events such as the Russo-Ukrainian war. The results can be used by the governments of oil-exporting countries for budget policies.