This study aims to explore dynamic correlations and volatility spillovers as well as hedging opportunities of clean and dirty cryptocurrencies with both developed and emerging regional stock markets during the COVID-19 pandemic and the Russia–Ukraine conflict.
This study applies, first, the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity model proposed by Engle (2002) to analyze the dynamic correlations between clean–dirty cryptocurrencies and regional stock markets. Second, it uses the VAR–MGARCH with the BEKK representation developed by Engle and Kroner (1995), to explore the volatility spillover effects between all variables. Third, it determines the optimal portfolio weights and the hedge ratios following Kroner and Ng (1998) and Kroner and Sultan (1993), respectively.
The findings reveal significant correlation and volatility spillovers between cryptocurrencies and regional stock markets, which are more prevalent during the COVID-19 pandemic than during the Russia–Ukraine conflict. In addition, in times of crisis, pairing clean cryptocurrencies with emerging market indices appears more attractive due to their relatively lower contagion effects and volatility spillovers as well as cheaper hedging cost.
To the best of the authors’ knowledge, this study is among the first to analyze the dynamic linkages of clean and dirty digital currencies with regional stock market indices during both COVID-19 pandemic and Russia–Ukraine conflict, contributing to not only enhanced understanding of these cross-market spillovers in time of crisis but also their hedging benefits. This understanding provides actionable insights into portfolio construction and risk management involving clean and dirty cryptocurrencies and regional stock market indices.
