Impact of the COVID‑19 outbreak on the US equity sectors:Evidence from quantile return spillovers

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.

Discovering interlinkages between major cryptocurrencies using high‑frequency data: new evidence from COVID‑19 pandemic

Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies(Bitcoin,Ethereum,and Litecoin),this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period.We also estimate the optimal weights,hedge ratios,and hedging effectiveness during both sample periods.We find that the return spillovers vary across the two periods for the Bitcoin–Ethereum,Bitcoin–Litecoin,and Ethereum–Litecoin pairs.However,the volatility transmissions are found to be different during the two sample periods for the Bitcoin–Ethereum and Bitcoin–Litecoin pairs.The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period.Based on optimal weights,investors are advised to decrease their investments(a)in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and(b)in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period.All hedge ratios are found to be higher during the COVID-19 period,implying a higher hedging cost compared to the pre-COVID-19 period.Last,the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period.Overall,these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification,hedging,forecasting,and risk management.

Coherence,Connectedness,Dynamic Linkages Among Oil and China's Sectoral Commodities with Portfolio Implications

This paper investigates the time-frequency dependence,return and volatility connectedness,dynamic linkages,and portfolio diversification gains among oil and China’s sectoral commodities,namely,Petrochemicals(CIFI),Grains(CRFI),Energy(ENFI),Non-ferrous metals(NFFI),Oil&Fats(OOFI),and Softs(SOFI),utilizing a proposed research framework that contains the wavelet coherence,novel TVP-VAR based connectedness,and the cDCC-,DECO-FIAPARCH(1,d,1)model.The empirical results demonstrate that global oil market exhibits a relatively higher(lower)coherence with ENFI,NFFI,and OOFI(CRFI)on the long-term time horizon and the oil market leads China’s sectoral commodities during most sample periods.The crude oil market transmits significant connectedness to China’s sectoral commodities,especially the energy commodity sector(ENFI).The dynamic return and volatility total spillovers tend to intensify and exhibit significant fluctuations during the GFC and the oil price collapse.Further,the time-varying linkages among oil and China’s sectoral commodities are positive and fluctuant,mainly at a relatively low level.The dynamic return and volatility connectedness,multi-view linkages,optimal portfolio weights,and hedging ratios display significant time-varying features.The oil-commodity nexus offers diversification benefits and the optimal-weighted portfolio presents the best variance and downside risk reduction performance.Furthermore,risk management effectiveness is market-condition-dependent and heterogeneous across different commodity sectors and sub-samples.This paper can not only help investors and market regulators to capture the complex interconnectedness and risk transmission trajectory among oil and China’s sectoral commodities but also benefits for investors and portfolio managers to construct optimal portfolios and hedging strategies.