Economics at your fingertips  

Modelling extremal dependence for operational risk by a bipartite graph

Oliver Kley, Claudia Klüppelberg and Sandra Paterlini

Journal of Banking & Finance, 2020, vol. 117, issue C

Abstract: We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between them. We then derive estimators and provide estimation methods for individual as well as aggregated tail risk, measured in terms of Value-at-Risk and Conditional-Tail-Expectation for very high confidence levels, and introduce also an asymptotically full capital allocation method for portfolio risk. Having access to real-world operational risk losses from the Italian banking system, we apply our model to these data, and carry out risk estimation in terms of the previously derived quantities. Simulation studies further reveal first that even with a small number of observations, the proposed estimation methods produce estimates that converge to the true asymptotic values, and second, that quantifying dependence by means of the empirical network has a big impact on estimates at both individual and aggregate level, as well as for capital allocations.

Keywords: Bipartite graph; Extremal dependence; Operational risk; Quantile risk measure; Value-at-Risk; Expected shortfall (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

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:

DOI: 10.1016/j.jbankfin.2020.105855

Access Statistics for this article

Journal of Banking & Finance is currently edited by Ike Mathur

More articles in Journal of Banking & Finance from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-10-03
Handle: RePEc:eee:jbfina:v:117:y:2020:i:c:s0378426620301217