The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

This study investigates risk transmission in the US energy instrument market to determine if certain factors, such as crude oil and natural gas, influence this market and whether stock or energy investment portfolios track their behavior. To investigate volatility spillover, the VAR-based connectedness approach is applied. This approach facilitates the measurement of interdependence across a network of variables, providing insights into aggregate, directional, and net interdependence. The use of the time-varying parameter vector autoregression (TVP-VAR) approach, as developed by Antonakakis and Gabauer, avoids the problems associated with selecting rolling window sizes and the resultant loss of observations during estimations. The analysis revealed a distinction between alternative and traditional ETFs, with lower interdependence observed among the volatility of alternative energy ETFs. While most energy ETFs transmit risk within the systems analyzed, some act as risk receivers, though their net receiving/transmitting character fluctuates. The results of this study are significant for investment portfolio managers.

Socially responsible investing (SRI) or sustainable, responsible, and impact investing is growing fast. The net total of SRI assets at the beginning of 2018 was USD 12.0 trillion. There is extensive literature on SRI, but very little of it relates to portfolio construction and risk management combining SRI and commodities. In this paper, the authors pay attention to model volatility and dynamic conditional correlations between SRI investment and selected representative of commodities. We state the following hypothesis: the potential to create portfolio and risk management opportunities exists between SRI and commodities such as grain, precious metals, and industrial metals. To verify this, modeling of volatility and dynamic conditional correlation (DCC) between pair of elements is necessary. Empirical research conducted for the global market based on selected indices for SRI and commodities confirms this hypothesis. These results can improve asset selection in portfolio construction and allow investors to make more reasonable decisions.

This study investigates the effects of crude oil and natural gas future returns on energy stock portfolios. We consider returns of portfolios of energy companies approximated by energy ETFs and returns of Brent crude oil and natural gas contracts listed on the US market from January 2015 to September 2022. To study the relationship between Brent crude oil, natural gas, and ETFs, we apply Granger causality in mean and variance, Dynamic Conditional Correlation and the tail dependence-focused copula approach. The research hypothesis regarding the dependence between energy ETFs and the underlying energy risk factors—crude oil and natural gas, and therefore, the existence of hedging or diversification opportunities, was verified. Our empirical findings indicate that crude oil has a medium effect on energy ETFs, and for natural gas it is even lower in the analyzed period, so hedging opportunities are weak, but opportunities for diversification arise.