The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Editorial to special issue“Hidden market linkages between Bitcoin,cryptocurrencies and financial markets:Evidence from high‑frequency data and higher‑order moments”in financial innovation

The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.

Regime specific spillover across cryptocurrencies and the role of COVID‑19

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.

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.