Markov Regime Switching Analysis for COVID-19 Outbreak Situations and their Dynamic Linkages of German Market

Abstract

German stock index(DAX) Markov Regime Switching Analysis (MRSA) GARCH(Generalised Autoregressive heteroscedasticity) Stock returns Growth rates of the disease Volatility This paper deals with the analysis of the dynamic linkage, co-movement between COVID-19 outbreak situations and German stock market. Firstly, Markov Regime Switching Analysis(MRSA) is proposed and employed to investigate the situations in the pandemic, as to catch the dynamics of how the daily number of the newly-infected changes, and also to assess the impact of the pandemic situations on German Market. Secondly, we compute the log growth rates of the weekly new cases and the log-returns of weekly DAX index, then fit the GARCH models to both of them to acquire their volatilities. We then employ the MRSA model once more to expose the dynamic linkages and co-movement between these two volatilities series. Through our empirical analyses, we find that GARCH models can capture the dynamics of stock returns and the growth rates. On the other hand, the MRSA models work well to identify the dynamics between different regimes with different states in dealing with the volatilities obtained from the estimated GARCH models. Our proposed econometric methods are highly practical, it indicates the possibility of replicating the results obtained in this study to assess the impact of other epidemics and negative factors on economic activities. Knowing what may happen during a pandemic, more effective measures and actions can be taken to protect people while dealing with another pandemic in the future.