Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/230795 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2019-019
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Systemic risk
CoVaR
Quantile regression
Neural networks
JEL: 
C00
Dokumentart: 
Working Paper

Datei(en):
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