Shock Diffusion Analysis for a Directed Market Network Constructed with Use of the Risk Measure ΔCoVaR

Abstract

This paper studies a complex network formed as a directed graph in which nodes represent the companies traded on the NYSE or NASDAQ while directed edges represent a connectedness measure between the financial assets. The directed edge weight between any two nodes is calculated with use of the value of ΔCoVaR, one of the most popular systemic risk measures proposed by M. Brunnermeier and T. Adrian in 2011. The value of ΔCoVaR measures the relationship between any two assets and is based not only on the yields of the assets, but take into account the mutual effect of its performance. In contrast with correlation coefficient, ΔCoVaR is asymmetric. The analysis is focused on the static model of the ΔCoVaR estimation. Moreover, this paper uses statistical testing procedures to assess the significance of the findings and interpretations based on this co-risk measure. We examine the intrinsic properties and regularities of stock market analyzing the directed complex network with more than 3700 stocks as nodes which have been traded on the NYSE and NASDAQ in recent years. We connect any two stock with a directed edge if the value of the corresponding ΔCoVaR is statistically significant and its normalized value is greater than a given threshold. We discuss both out-degree and in-degree distributions and find essential vertices in the network, which represent the leading stocks. We demonstrate that the network follows the power-law distribution and behaves scale-free. Moreover, we address the problem of finding influential spreaders, i.e. companies which are more likely to spread negative shocks in a large part of the network. In this paper we use three different measures (closeness centrality, betweenness centrality, PageRank) to determine the most influential stocks in the directed market graph.

Comparative Analysis of Financial Network Topology for the Russian, Chinese and US Stock Markets

“…It turned out that the network analysis is capable to examine the importance of companies using different centrality measures [45,45] and to analyze the systemic risks and financial market stability based on the topological properties of networks [46,47,48]. Systemic risk contagion has been analyzed in [49,50] to find major influencers based on banking behaviors in the global environment. The paper [51] studies European markets and shows that EU markets are exposed to systemic risks.…”