Modelling Extremal Dependence for Operational Risk by a Bipartite Graph
Oliver Kley (),
Claudia KlÃ¼ppelberg () and
Sandra Paterlini ()
No 2019/2, DEM Working Papers from Department of Economics and Management
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 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 provide also an asymptotically full capital allocation method. Estimation methods for such tail risk measures and capital allocations are also proposed and tested on simulated data. Finally, by having access to real-world operational risk losses from the Italian banking system, we show that even with a small number of observations, the proposed estimation methods produce reliable estimates, and 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; Estimation; Expected Shortfall; Extremal dependence; Operational Risk; Quantile risk measure; Value-at-Risk (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://www.economia.unitn.it/alfresco/download/wo ... edb4c/DEM2019_02.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:trn:utwprg:2019/02
Access Statistics for this paper
More papers in DEM Working Papers from Department of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by email@example.com ().