Operational risk: analytical results when high-severity losses follow a generalized Pareto distribution (GPD) – a note

Böcker, Klaus

doi:10.21314/jor.2006.138

Cited by 9 publications

(7 citation statements)

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“…As a consequence of (4.4), analaytic stand-alone OpVAR is then for fixed t > 0 given explicitly by (see again Böcker [5])…”

Section: Results For the Heavy-tailed Gpd Modelmentioning

confidence: 99%

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Modeling and measuring multivariate operational risk with Lévy copulas

Böcker¹,

Klüppelberg²

2008

JOP

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Simultaneous modelling of operational risks occurring in different event type/business line cells poses a serious challenge for operational risk quantification. Here we invoke the new concept of Lévy copulas to model the dependence structure of operational loss events. We explain the consequences of this dependence concept for frequencies and severities of operational risk in detail. For important examples of the Lévy copula and heavy-tailed GPD tail severities we derive first order approximations for multivariate operational VAR.

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“…As a consequence of (4.4), analaytic stand-alone OpVAR is then for fixed t > 0 given explicitly by (see again Böcker [5])…”

Section: Results For the Heavy-tailed Gpd Modelmentioning

confidence: 99%

“…in Moscadelli [17]; an example for its practical implementation can be found in Nguyen & Ottmann [18]. Finally, closed-form results for stand-alone OpVAR as well as expected shortfall using GPD tail severities are provided in Böcker [5].…”

Section: Results For the Heavy-tailed Gpd Modelmentioning

confidence: 99%

Modeling and measuring multivariate operational risk with Lévy copulas

Böcker¹,

Klüppelberg²

2008

JOP

Self Cite

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“…where n 6 ¼ 0 and b ¼ An. The latter model covers more general tail behavior, since it can be adapted to the case in which the tail of F(x) cannot be represented in the form shown in Equation (27) [23][24][25]. Although this model does not generally represent a location-scale family, we advise researchers to explore the model once the amount of available data can support the added complexity.…”

Section: Tail-based Inferencementioning

confidence: 99%

Modeling of risk losses using size-biased data

Yashchin

2007

IBM J. Res. & Dev.

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In this paper we present a method for drawing inferences about the process of financial losses that are associated with the operations of a business. For example, for a bank such losses may be related to erroneous transactions, human error, fraud, lawsuits, or power outages. Information about the frequency and magnitude of losses is obtained through the search of a number of sources, such as printed, computerized, or Internet-based publications related to insurance and finance. The data consists of losses that were discovered in the search. We assume that the probability of a loss appearing in the body of sources and also being discovered increases with the magnitude of the loss. Our approach simultaneously models the process of losses and the process of populating the database. The approach is illustrated using data related to operational risk losses that are of special interest to the banking industry.

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“…The existing of analytical solution of operational VaR is a prerequisite for addressing the above problems. Bocker and KlÄuppelberg (2005), Bocker and Sprittulla (2006) and Bocker (2006) have found that, with LDA, when it comes to the general distribution, the analytical solution to operational VaR is not existing. But as for the heavy-tailed risk measurement, the value of the tail does have analytical solution.…”

Section: Introductionmentioning

confidence: 99%

Connection Parameters of Heavy-tailed Operational Risk Measurement Model and Management Model

He¹,

Qing²,

Mo³

et al. 2016

JRACR

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In order to connect the heavy-tailed operational risk measurement model with management model, a model identifying the crucial supervising parameters of operational risk is built after the heavy-tailed operational VaR's sensitivity is theoretically researched by the elasticity analysis method. Further, the analysis of model application is illustrated with a numerical example. The crucial supervising parameters connect the operational risk measurement model and management model, which make the operational risk management frameworks to be a complete system. And a dynamical supervising system of operational risk is established. This research in theory improves the application of loss distribution approach to the operational risk measurement and management.

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Operational risk: analytical results when high-severity losses follow a generalized Pareto distribution (GPD) – a note

Cited by 9 publications

References 0 publications

Modeling and measuring multivariate operational risk with Lévy copulas

Modeling and measuring multivariate operational risk with Lévy copulas

Modeling of risk losses using size-biased data

Connection Parameters of Heavy-tailed Operational Risk Measurement Model and Management Model

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