The purpose of this paper is to examine the connected dynamics of the affected Asian financial markets and global financial market in relation to the outbreak of the coronavirus (COVID-19) pandemic. We particularly examine the temporal dependence and connectedness of the affected markets with the global financial market by using the time-varying dependence approach in a time-frequency space under COVID-19. Our findings indicate a strong, positive dependence among the investigated markets’ due to the outbreak of COVID-19. In addition, we report an increased tendency of co-movements over the higher horizon which is documented by COVID-19. These findings are of significant interest for market participants, policymakers, and international investors.

Utilising Chinese-developed data based on long-standing influenza indices, and the more recently-developed coronavirus and face mask indices, we set out to test for the presence of volatility spillovers from Chinese financial markets upon a broad number of traditional financial assets during the outbreak of the COVID-19 pandemic. Such indices are used to specifically measure the performance of Chinese companies who are inherently involved in the R&D and production of materials and products used to mitigate and counteract the effects of influenza and coronavirus, therefore, such indices present a unique barometer of broad population-based sentiment relating to COVID-19 in comparison to traditional Chinese influenza. Within days of the formal announcement of the COVID-19 outbreak, results indicate exceptionally pronounced and persistent impacts of the coronavirus pandemic upon Chinese financial markets, compared to that of the traditional and long-standing influenza index. Further, in a novel finding to date, COVID-19 is found to have had a substantial effect on directional spillovers upon the Bitcoin market. Cryptocurrency-based confidence appears to have been instigated through government-developed education schemes, which are identified as one possible explanation for our results, which are found to remain robust across both data-frequency and methodological variation.