Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 019

Modelling Systemic Risk Using Neural Network Quantile Regression

Georg Keilbar
Weining Wang

We propose an approach to calibrate the conditional value-at-risk (CoVaR) of
financial institutions based on neural network quantile regression. Building on
the estimation results we model systemic risk spillover effects across banks by
considering the marginal effects of the quantile regression procedure. We adopt a
dropout regularization procedure to remedy the well-known issue of overfitting
for neural networks, and we provide empirical evidence for the favorable out-of-
sample performance of a regularized neural network. We then propose three
measures for systemic risk from our fitted results. We find that systemic risk
increases sharply during the height of the financial crisis in 2008 and again
after a short period of easing in 2011 and 2015. Our approach also allows
identifying systemically relevant firms during the financial crisis.

Systemic risk, CoVaR, Quantile regression, Neural networks

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