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Title:

Modelling extremal dependence for operational risk by a bipartite graph

Document type:
Zeitschriftenaufsatz
Author(s):
Kley, Oliver; Klüppelberg, Claudia; Paterlini, Sandra
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...     »
Keywords:
Bipartite graph Extremal dependence Operational risk Quantile risk measure Value-at-Risk Expected shortfall
Dewey Decimal Classification:
510 Mathematik
Journal title:
Journal of Banking & Finance
Year:
2020
Journal volume:
117
Year / month:
2020-08
Quarter:
3. Quartal
Month:
Aug
Pages contribution:
105855
Fulltext / DOI:
doi:10.1016/j.jbankfin.2020.105855
WWW:
ScienceDirect
Publisher:
Elsevier BV
E-ISSN:
0378-4266
Notes:
Available online 16 May 2020
Status:
Verlagsversion / published
Submitted:
28.03.2020
Accepted:
10.05.2020
Date of publication:
01.08.2020
Semester:
SS 20
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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