This study investigates the reaction of stock markets to the Covid-19 pandemic and the Global Financial Crisis of 2008 (GFC) and compares their influence in terms of risk exposures. The empirical investigation is conducted using the modified ICSS test, DCC-GARCH, and Diebold-Yilmaz connectedness analysis to examine financial contagion and volatility spillovers. To further reveal the impact of these two crises, the statistical features of tranquil and crisis periods under different time intervals are also compared. The test results show that although the outbreak’s origin was in China, the US stock market is the source of financial contagion and volatility spillovers during the pandemic, just as it was during the GFC. The propagation of shocks is considerably higher between developed economies compared to emerging markets. Additionally, the results show that the COVID-19 pandemic induced a more severe contagious effect and risk transmission than the GFC. The study provides an extensive examination of the COVID-19 pandemic and the GFC in terms of financial contagion and volatility spillovers. The results suggest the presence of strong co-movements of world stock markets with the US equity market, especially in periods of financial turmoil.

In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock market and most of the Australian sector indices, with the financial, health care, information technology, and utility sectors displaying a decrease in co-movements during the pandemic. The Modified Iterative Cumulative Sum of Squares (MICSS) analysis results indicated the presence of structural breaks in the volatilities of most of the sector indices around the end of February 2020, but consumer staples, industry, information technology and real estate indices did not display any break. Markov regime-switching regression analysis depicted that the pandemic has mainly affected three sectors: consumer staples, industry, and real estate. When we considered the firm size, we found that smaller companies in the energy sector exhibited gradual deterioration, whereas small firms in the consumer staples sector experienced the largest positive impact from the pandemic.

This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018.

This study explores the connectedness between selected emerging equity markets (BRIC-T) and the Emerging Markets Financial Stress Index (EMFSI). We aim to reveal the extent of spillovers from stock market indices to aggregated financial tension in these countries. Empirical investigations are executed through Quantile Vector Autoregression analysis. Results show that spillovers occur mainly during extreme negative and positive return periods. When we focus on four important phases, namely Global Financial Crisis (GFC), the Euro Area Sovereign Debt Crisis, the COVID-19 pandemic, and the Russia-Ukraine war, three countries come to the fore. While Brazil has had a substantial and persistent impact across the years, during the GFC, two other countries, Russia and Türkiye, seem to induce positive return spillovers toward emerging markets’ stress. This impact becomes bilateral in Russia (both in positive and negative returns) during the pandemic and the Russia-Ukraine war. Thus, we conclude that among the examined market economies, negative or positive return transmissions to emerging market stress are led mainly by Brazil and Russia. The importance of energy sources and political factors can account for this result.

This study examines received and transmitted volatility spillovers of Credit Default Swap (CDS) and Asset-Swap Spread (ASW) for Brazil and Turkey. The empirical analysis is implemented using two country-based (stock markets and exchange rates) and two global (volatility index and global economic activity index) variables to account for the impact of integration into global markets. Empirical results suggest that both countries display distinctive features in their spillover networks. While exchange rates and the stock market figure prominently in Brazil as a source of spillovers, for Turkey, the primary element in spillovers appears to be credit risk indicators. Time-varying analysis results show that the European Debt Crisis of 2010-2011 and the global liquidity crunch of 2018-2019 are two critical periods in volatility spillovers that occurred toward credit risk indicators. Brazil displays more sensitivity to the developments of the pandemic than Turkey, likely due to its dependence on global economic activity and energy prices. Finally, for both countries, the leading variable in spillovers to credit risk indicators during financial turbulence episodes appears to be foreign exchange markets. This result highlights both economies? fragility and vulnerability to foreign exchange market-based shocks. Thus, we suggest effective and solid measures in this regard. Otherwise, those shocks could potentially induce a higher cost of financing in both economies due to the negative impacts on CDS and ASW spreads.