EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients

Abstract

For this paper, we dynamically analysed the comovements between three major stock markets—Germany, the UK, and the US—and the countries of the European Union, divided into two groups: Eurozone and non-Eurozone. Correlation coefficients based on a detrended cross-correlation analysis (DCCA) were used, and the respective temporal variation was evaluated. Given the objective of performing a dynamic analysis, sliding windows were used in an attempt to represent short and long-term analyses. Critical moments in financial markets worldwide were also taken into account, namely the subprime debt crisis, the sovereign debt crisis, and Brexit. The results suggest that Germany and other Eurozone countries generally share high levels of comovements, although the Brexit decision reduced those connections. The subprime crisis also increases comovements among markets.

Integration in Bric Stock Markets: An Empirical Analysis

“…The interdependence and integration of financial markets are quite distinct concepts, and the interdependence between markets is associated with the phenomenon of price movements between different markets, even though there is no economic basis for or enough knowledge of the facts that led to this joint movement. On the other hand, we are dealing with integrated markets when assets with similar risk but belonging to different markets are associated with similar returns (Tilfani, Ferreira, Dionisio, and Youssef El Boukfaoui, 2020).…”

The Dynamic Volatilities and Correlations between German Stock Market Indices and Commodities: Evidence from Wavelet and MGARCH-DCC Approaches

“…Shariah-compliant screening removes those companies with high outstanding debt from Islamic stocks and those involved with unlawful activities, gambling, and interest-based businesses. Previous research on German stock indices, such as Tilfani, Ferreira, Dionisio, and Youssef El Boukfaoui (2020) and Celebi and Hönig (2019), focused mostly on conventional stock indices and ignored Islamic stock indices. Therefore, we aim to add to the academic literature by examining the German Islamic stock index and comparing it with the conventional index and other commodities, such as gold, crude oil, and Bitcoin.…”

“…Therefore, we aim to investigate the comovement of commodity volatility by investigating the dynamic volatility and correlation between the commodity and stock markets. Tilfani et al (2020) investigated the relationships among three major stock markets, namely Germany, the United Kingdom, and the United States, as well as the European Union nations, which were separated into two factions: Eurozone and non-Eurozone. They found that, in general, Germany and other Eurozone countries have substantial levels of comovement, but Brexit reduced these connections.…”

The use of transfer entropy to analyse the comovements of European Union stock markets: a dynamical analysis in times of crises

“…However, it may suffer from the limitation of linear parametric modelling (Diks & Fang, 2017), leading us to apply the TE approach to overcome the idéntifiéd restrictions and look again at spillovers in the European equity market as a whole by using the non-parametric method. Furthermore, as can be seen from the above literature review, there is a scarcity of studies that dynamically assess integration in all the EU stock markets (Tilfani et al [2020] being an exception) which have the ability to identify who the main stock market influéncérs are and which countries are most affected by them.…”

“…However, there is no consensus on this issue, as is stated, for example, by Gabriel and Pires (2015) and shown in the related literature review. It is also possible to identify several approaches, from Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (with a vast range of spécifications) to event-study approaches or the use of the Diebold and Yilmaz approach, among others (see, for example, Tilfani et al, 2020, or Chakrabarti et al, 2021. Among the most popular are lagged crosscorrelation (Ramchand & Susmel, 1998) and Granger causality (Granger et al, 2000), albeit with their limitations; while the former can neither distinguish the possible bidirectional impact between markets nor separate the effects caused solely by the source time series from the environmental effects, the latter can only detect information coming from the source series; besides this, it is also a linear approach, meaning it may not be sensitive to non-linear interactions.…”