Categorical network models for systemic risk measurement

A very important area of financial risk management is systemic risk modelling,
which concerns the estimation of the interrelationships between financial institutions, with
the aim of establishing which of them are more central and, therefore, more contagious/
subject to contagion. The aim of this paper is to develop a systemic risk model
which, differently from existing ones, employs not only the information contained in
financial market prices, but also big data coming from financial tweets. From a methodological
viewpoint, we propose a new framework, based on categorical graphical models,
that can estimate systemic risks with models based on two different sources: financial
markets and financial tweets, and suggest a way to combine them, using a Bayesian
approach. From an applied viewpoint, we present the first systemic risk model based on big
data, and show that such a model can shed further light on the interrelationships between
financial institutions. This can help predicting the level of returns of a bank, conditionally
on the others, for example when a shock occurs in another bank.