Deep diving into the S&P Europe 350 index network and its re-action to COVID-19
Date
2021
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Abstract
In this paper, we analyse the dynamic partial correlation network of the constituent
stocks of S&P Europe 350. We focus on global parameters such as radius, which is
rarely used in financial networks literature, and also the diameter and distance parameters.
The first two parameters are useful for deducing the force that economic instability should
exert to trigger a cascade effect on the network. With these global parameters, we hone the
boundaries of the strength that a shock should exert to trigger a cascade effect. In addition,
we analysed the homophilic profiles, which is quite new in financial networks literature. We
found highly homophilic relationships among companies, considering firms by country and
industry. We also calculate the local parameters such as degree, closeness, betweenness,
eigenvector, and harmonic centralities to gauge the importance of the companies regarding
different aspects, such as the strength of the relationships with their neighbourhood and
their location in the network. Finally, we analysed a network substructure by introducing
the skeleton concept of a dynamic network. This subnetwork allowed us to study the stability
of relations among constituents and detect a significant increase in these stable connections
during the Covid-19 pandemic.
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Keywords
financial networks, centralities, homophily, multivariate GARCH, networks connectivity, gaussian graphical model, Covid-19