The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic.

Previous studies argue that the relationships between clean/green and dirty energy assets are time-varying, but there is a lack of evidence on the hedging ability of clean energy stocks and green bonds for dirty assets, such as crude oil and an energy stock index exchange traded fund (ETF), and the portfolio implications. Furthermore, potential drivers of the dynamics of the hedge portfolio returns are still unknown. To address these research gaps, the authors provide an extensive analysis of the hedging ability of clean/green assets against two dirty energy assets (crude oil prices and energy ETF) using daily data from 3 January 2012 to 29 November 2019. Using corrected dynamic conditional correlation models, the authors model correlation and then compute hedge ratios and hedging effectiveness, which all seem to vary with time. The results from hedging effectiveness indicate that investors should follow a dynamic hedging strategy and that clean energy stocks are more effective hedge than green bonds, especially for crude oil. The application of regression analyses shows that the implied volatilities of US equities and crude oil as well as US dollar index have a negative impact on the hedge portfolio returns, whereas gold prices and inflation have a positive impact.

The purpose of this study is to provide a method to investigate the effects of thermal relaxation times in a poroelastic material by using the finite element method. The formulations are applied under the Green and Lindsay model, with four thermal relaxation times. Due to the complex governing equation, the finite element method has been used to solve these problems. All physical quantities are presented as symmetric and asymmetric tensors. The effects of thermal relaxation times and porosity in a poro-thermoelastic medium are studied. Numerical computations for temperatures, displacements and stresses for the liquid and the solid are presented graphically.

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.

This study examines the dynamics of return and volatility connectedness between the rare earth stock index and the indexes of clean energy, consumer electronics, telecommunications, healthcare equipment, and aerospace & defence. Using daily data from 25 March 2010 to 25 August 2020, a quantile-based connectedness approach is applied to uncover both average and tail-based connectedness while considering the full sample period and the COVID-19 pandemic days. The results suggest that the interdependence among these indexes changes dramatically at the lower and upper quantiles, suggesting a strong influence of extreme market scenarios on both returns and volatility connectedness dynamics. Higher integration of sectoral indexes is observed during 2010–2012 and the COVID-19 pandemic period. Health care and telecommunication indexes have been consistent transmitters of return and volatility spillovers to other indexes during the full sample period. Consumer electronics and clean technology indexes switch their roles from a net receiver to a net transmitter during pandemic days. The rare earth remains on the recipient’s side consistently. The findings indicate that the ongoing U.S.–China trade embargo has not impacted the return and volatility dynamics of the five sectoral indexes superseding the demand-driven dynamics for rare earth.