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Evidence, Estimates and Extreme Values from Austria

Published on Sep 1, 2014in Journal of Operational Risk 0.73
· DOI :10.21314/JOP.2014.138
Stefan Kerbl1
Estimated H-index: 1
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Abstract
We explore a new data source of operational loss events, the Austrian Loss Data Collection, featuring more than 42 000 observations. We provide statistical characteristics per event type and business line and analyze the cross-time and crosssection of the data. Subsequently, we make use of the data to address a central question of operational risk research: which approach is best suited to model the severity distribution of a single loss? Scientific attention to this question resulted in three major candidates: the generalized Pareto distribution (GPD), the central distribution of extreme value theory; the g-and-h distribution, an exotic relative to the lognormal; and the recently proposed semiparametric approach based on the modified Champernowne transformation. To evaluate the performance of each approach, we apply cross-validation. First, we find that capital requirements for operational risk are the best proxy for total losses of banks (among the considered indicators) and that, while frequency is highly correlated with size, mean losses are not. Second, we confirm the results of prior research that the GPD provides a very good fit in the cross-validation exercise. Additionally, the g-and-h distribution was found to rank second.
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  • Citations (4)
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Published on Apr 1, 2011in Applied Economics Letters 0.59
Helder Ferreira de Mendonça11
Estimated H-index: 11
(CNPq: National Council for Scientific and Technological Development),
Délio José Cordeiro Galvão3
Estimated H-index: 3
(Central Bank of Brazil),
Renato Falci Villela Loures2
Estimated H-index: 2
(Federal Fluminense University)
This article presents an analysis for the estimation of economic capital concerning operational risk in a Brazilian banking industry case making use of Markov chains, Extreme Value Theory (EVT) and Peaks Over Threshold (POT) modelling. The findings denote that some existent methods present consistent results among institutions with similar characteristics of loss data.
Published on Nov 1, 2007in Astin Bulletin 1.02
Matthias Degen5
Estimated H-index: 5
(ETH Zurich),
Paul Embrechts49
Estimated H-index: 49
(ETH Zurich),
Dominik D. Lambrigger5
Estimated H-index: 5
(ETH Zurich)
Operational risk has become an important risk component in the banking and insurance world. The availability of (few) reasonable data sets has given some authors the opportunity to analyze operational risk data and to propose different models for quantification. As proposed in Dutta and Perry [12], the parametric g-and-h distribution has recently emerged as an interesting candidate. In our paper, we discuss some fundamental properties of the g-and-h distribution and their link to extreme value t...
Published on Nov 1, 1996in The American Statistician 5.38
Rob J. Hyndman41
Estimated H-index: 41
,
Yanan Fan16
Estimated H-index: 16
Abstract There are a large number of different definitions used for sample quantiles in statistical computer packages. Often within the same package one definition will be used to compute a quantile explicitly, while other definitions may be used when producing a boxplot, a probability plot, or a QQ plot. We compare the most commonly implemented sample quantile definitions by writing them in a common notation and investigating their motivation and some of their properties. We argue that there is...
Published on Jan 1, 1977
W John61
Estimated H-index: 61
Cited By4
Newest
Xiaoqian Zhu7
Estimated H-index: 7
(CAS: Chinese Academy of Sciences),
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences),
Dengsheng Wu10
Estimated H-index: 10
(CAS: Chinese Academy of Sciences)
The Basel Committee on Banking Supervision (BCBS) states that in addition to the fact that it lacks simplicity, the Advanced Measurement Approach (AMA) must be discarded because the flexibility of ...
Published on Sep 1, 2018in Annals of Data Science
Lu Wei2
Estimated H-index: 2
(CAS: Chinese Academy of Sciences),
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences),
Xiaoqian Zhu7
Estimated H-index: 7
(CAS: Chinese Academy of Sciences)
This paper is the first to provide a comprehensive overview of the worldwide operational loss data collection exercises (LDCEs) of internal loss, external loss, scenario analysis and business environment and internal control factors (BEICFs). Based on analyzing operational risk-related articles from 2002 to March 2017 and surveying a large amount of other information, various sources of operational risk data are classified into five types, i.e. individual banks, regulatory authorities, consortia...
Published on Oct 19, 2016in International Journal of Financial Studies
Suren Pakhchanyan1
Estimated H-index: 1
(University of Oldenburg)
Following the three-pillar structure of the Basel II/III framework, the article categorises and surveys 279 academic papers on operational risk in financial institutions, covering the period from 1998 to 2014. In doing so, different lines of both theoretical and empirical directions for research are identified. In addition, this study provides an overview of existing consortia databases and other publicly available sources on operational loss that may be incorporated into empirical research, as ...
Published on Apr 16, 2016in Journal of Operational Risk 0.73
Daniel H. Stahl1
Estimated H-index: 1
Operational risk modeling presents a number of difficulties. The severity distribution is often very heavy tailed (moments of second order and higher are infinite) making Monte Carlo simulations ineffective. Analytical solutions like the loss distribution approach (LDA) model are not flexible enough to model the empirical correlations often found between frequency and severity distributions. This paper proposes a significant generalization of the LDA model. This generalization involves treating ...