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Deedp Diving Into The S&P 350 Europe Index Network Ans Its Reaction To Covid-19

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  • Ariana Paola Cortés à ngel
  • Mustafa Hakan Eratalay

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.

Suggested Citation

  • Ariana Paola Cortés à ngel & Mustafa Hakan Eratalay, 2021. "Deedp Diving Into The S&P 350 Europe Index Network Ans Its Reaction To Covid-19," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 134, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    • Handle: RePEc:mtk:febawb:134
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    References listed on IDEAS

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    1. Mustafa Hakan Eratalay & Ariana Paola Cortés Ángel, 2022. "The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms," JRFM, MDPI, vol. 15(4), pages 1-41, March.

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    Keywords

    Financial Networks; Centralities; Homophily; Multivariate GARCH; Networks Connectivity; Gaussian graphical model; Covid-19;
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