Modelling systemic risk using neural network quantile regression

Georg Keilbar () and Weining Wang ()
Additional contact information
Georg Keilbar: Humboldt-Universität zu Berlin
Weining Wang: University of York

Empirical Economics, 2022, vol. 62, issue 1, No 6, 93-118

Abstract: Abstract We propose a novel 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 in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. We then propose three network-based measures from our fitted results. First, we use the Systemic Network Risk Index (SNRI) as a measure for total systemic risk. A comparison to the existing network-based risk measures reveals that our approach offers a new perspective on systemic risk due to the focus on the lower tail and to the allowance for nonlinear effects. We also introduce the Systemic Fragility Index (SFI) and the Systemic Hazard Index (SHI) as firm-specific measures, which allow us to identify systemically relevant firms during the financial crisis.

Keywords: Systemic risk; CoVaR; Quantile regression; Neural networks (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s00181-021-02035-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:empeco:v:62:y:2022:i:1:d:10.1007_s00181-021-02035-1

Ordering information: This journal article can be ordered from
http://www.springer. ... rics/journal/181/PS2

DOI: 10.1007/s00181-021-02035-1

Access Statistics for this article

Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund

More articles in Empirical Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().