Modelling Systemic Risk Using Neural Network Quantile Regression

Abstract

We propose a novel approach to estimate the conditional value at risk (CoVaR) of nancial institutions. Our approach is based on neural network quantile regression. Building on the estimation results we model systemic risk spillover eects across banks by considering the marginal eects of the quantile regression procedure. We obtain a time-varying risk network represented by an adjacency matrix. We then propose three measures for systemic risk. The Systemic Fragility Index and the Systemic Hazard Index are measures to identify the most vulnerable and most critical rms in the nancial system, respectively. As a third risk measure we propose the Systemic Network Risk Index which represents the overall level of systemic risk. We apply our methodology to the global systemically relevant banks from the United States in a time period from 2007 until 2018. Our results are similar to previous studies about systemic risk. We nd that systemic risk increased sharply during the height of the nancial crisis in 2008 and again after a short period of easing in 2011 and 2015.

Intelligent Process Automation of Industries Using Artificial Intelligence and Machine Learning

“…G. Keilbar and W. Wang in [13] evaluated another risk that neural network system bears compared to traditional systems. It is considered that neural networks bear statistical truths instead of literal truths.…”