Open AccessShock Diffusion Analysis for a Directed Market Network Constructed with Use of the Risk Measure ΔCoVaR
Author(s) -
Ivan Androsov,
Alexey Faizliev,
E. V. Korotkovskaya,
A. E. Lun'kov,
С. В. Миронов,
Vladimir Petrov,
Sergei Sidorov,
Fedor M. Smolov
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1334/1/012003
Subject(s) - econometrics , measure (data warehouse) , social connectedness , stock (firearms) , systemic risk , risk measure , computer science , stock market , economics , financial economics , financial crisis , data mining , mechanical engineering , psychology , portfolio , paleontology , macroeconomics , horse , biology , engineering , psychotherapist
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.