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Titel:

Modelling extremal dependence for operational risk by a bipartite graph

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Bipartite graph Extremal dependence Operational risk Quantile risk measure Value-at-Risk Expected shortfall
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Journal of Banking & Finance
Jahr:
2020
Band / Volume:
117
Jahr / Monat:
2020-08
Quartal:
3. Quartal
Monat:
Aug
Seitenangaben Beitrag:
105855
Volltext / DOI:
doi:10.1016/j.jbankfin.2020.105855
WWW:
ScienceDirect
Verlag / Institution:
Elsevier BV
E-ISSN:
0378-4266
Hinweise:
Available online 16 May 2020
Status:
Verlagsversion / published
Eingereicht (bei Zeitschrift):
28.03.2020
Angenommen (von Zeitschrift):
10.05.2020
Publikationsdatum:
01.08.2020
Semester:
SS 20
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
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