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Modelling Systemic Risk Using Neural Network Quantile Regression

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  • Keilbar, Georg
  • Wang, Weining

Abstract

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.

Suggested Citation

  • Keilbar, Georg & Wang, Weining, 2019. "Modelling Systemic Risk Using Neural Network Quantile Regression," IRTG 1792 Discussion Papers 2019-019, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2019019
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    References listed on IDEAS

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    1. repec:hum:wpaper:sfb649dp2012-053 is not listed on IDEAS
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    7. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
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      • Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
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    Cited by:

    1. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    • C00 - Mathematical and Quantitative Methods - - General - - - General

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