A robustness comparison of two market network models

IMA Journal Management Mathematics. 2022. Vol. 33. No. 1. P. 123–137.

Semenov D., Koldanov A. P., Koldanov P., Pardalos P. M.

Two market network models are investigated. One of them is based on the classical Pearson correlation
as the measure of association between stocks returns, whereas the second one is based on the
sign similarity measure of association between stocks returns. We study the uncertainty of
identification procedures for the following market network characteristics: distribution of weights
of edges, vertex degree distribution in the market graph, cliques and independent sets in the market
graph, and the vertex degree distribution of the maximum spanning tree. We define the true
network characteristics, the losses from the error of its identification by observations, and the uncertainty
of identification procedures as the expected value of losses. We use elliptically contoured distribution
as a model of multivariate stocks returns distribution. It is shown that identification statistical
procedures based on the sign similarity are statistically robust in contrast to the procedures based on the classical Pearson correlation

Keywords: неопределенность, Market network analysis, Measures of association, функция риска, Risk function, сеть случайных величин, Uncertainty, Random variables network, distribution-free statistical procedures, анализ рыночных сетей, мера ассоциации, статистические процедуры не зависящие от распределения

Publication based on the results of: Research of robustness of network analysis algorithms (2020)