Phenomena similar to the collapse on financial and economic networks can be described as cascading failures across nodes in large scale networks delineating collective dynamics, leading to a system-wide collapse. The reflexivity property makes it difficult to disentangle the source and destination of a shock that percolates through the network. The methodology proposed by Sudarshan Kumar†, Tiziana Di Matteo‡ and Anindya S. Chakrabarti, combining vector autoregression model with an unique identification restrictions obtained from the topological structure of the network, show that planarity of the network allows to statistically estimate a dynamical process consistent with the observed network and thereby uniquely identify a path for shock propagation from any chosen epicenter to all other nodes in the network.
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Myself
As an Investment Consultant and Specialist, Pompeo Pontone is a Professional Investor with 25 years’ experience in the fields of Investment Management and Quantitative Finance, with advanced expertise in Computer Science and Data Science.