Dynamic Connectivity in a Financial Network Using Time-Varying DCCA Correlation Coefficients

Abstract

This paper aims to analyse the connectivity of 13 stock markets, between 1998 and 2019, with a time-varying proposal, to evaluate evolution of the linkage between these markets over time. To do so, we propose to use a network built based on the correlation coefficients from the Detrended Cross-Correlation Analysis, using a sliding windows approach. Besides allowing for analysis over time, our approach also enables us to verify how the network behaves for different time scales, which enriches the analysis. We use two different properties of networks: global efficiency and average grade, to measure the network’s connectivity over time. We find that the markets under analysis became more connected before the subprime crisis, with this behavior extending even after the Eurozone crisis, showing that during extreme events there is an increase in financial risk, as found in the international literature.

Evaluating the connectedness of commodity future markets via the cross-correlation network

“…The detrended cross-correlation analysis (DCCA) [19,20] has been the most acknowledged and applied technique in the analysis of cross-correlations between financial time series, such as commodity future prices [21,22] and stock trading volumes or prices [23,24]. Since there could potentially be cross-correlations between any two financial time series, financial markets can thus be linked into networks [25][26][27]. The analysis of DCCA networks also has the potential to measure the importance of each individual entity in the whole system.…”

“…When the VAR-based methods characterize the directional relationship that a shock in one time series leads to the volatility change in another time series within a given lag time, the DCCA approach describes the bilateral relationship of co-fluctuation of two time series. In spite of the widespread applications of the DCCA approach in investigating the dynamics of financial networks [8,[27][28][29][30][31][32], whether, or to what extent, can such an approach represent the volatility spillover effect as indicated by the VAR-based measures is still unclear. The exploration of such a research question is crucial to deepen the understanding of the dynamics of complex financial systems, as well as enrich the application of the DCCA approach.…”