Modelling Operational Risk Using Bayesian Inference

Shevchenko, Pavel V.

doi:10.1007/978-3-642-15923-7

Cited by 81 publications

(64 citation statements)

References 42 publications

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“…To reach this objective, the most interesting point comes from the way institutions use the data sets. In the traditional Loss Distribution Approach (Frachot et al (2001), Cruz (2004), Chernobai et al (2007), Shevchenko (2011)), the losses found in a category form a distribution called severity distribution, and the dates enable creating a frequency distribution. The losses are assumed independent and identically distributed (i.i.d.).…”

Section: Introductionmentioning

confidence: 99%

Using a time series approach to correct serial correlation in operational risk capital calculation

Guégan¹,

Hassani²

2013

JOP

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To cite this version:Dominique Guegan, Bertrand Hassani. Using a time series approach to correct serial correlation in operational risk capital calculation. Authors: Acknowledgment:The authors would like to thank the Sloan Foundation for funding this research.1 Disclaimer: The opinions, ideas and approaches expressed or presented are those of the authors and do not necessarily reflect Santander's position. As a result, Santander cannot be held responsible for them. If by implementing the traditional LDA, no particular distribution proves its adequacy to the data -as soon as the goodness-of-fit tests reject them -keeping the LDA corresponds to an arbitrary choice. This paper suggests an alternative and robust approach. For instance, for the two data sets explored in this paper, with the introduced time series strategies, the independence assumption is relaxed and the autocorrelations embedded within the losses are captured. The construction of the related LDF enables the computation of the capital charges and therefore permits to comply with the regulation taking into account at the same time the large losses with adequate distributions on the residuals, and the correlations between the losses with the time series processes.

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Section: Introductionmentioning

confidence: 99%

Using a time series approach to correct serial correlation in operational risk capital calculation

Guégan¹,

Hassani²

2013

JOP

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“…They initiated many detailed methodologies specific for different fields. A classic example is the risk management methodology in the banking sector, based on the recommendations of Basel II standards for mathematical models of risk management [90]. The current dominant statistical approach is not satisfactory because it does not give effective tools for inferences about the vague concepts and relations between them (see the afore-mentioned sentences by Valiant cited in Sect.…”

Section: Risk Management By Agents In Bdtmentioning

confidence: 99%

Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement

Skowron

Jankowski²,

Dutta

2015

Studies in Big Data

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Big Data, requires to develop relevant computation models for the agents as well as methods for incorporating changes in the reasoning of the computation models themselves; these requirements would enable agents to control computations for achieving the target goals. It is to be noted that users are also agents. Agents are performing computations on complex objects of very different natures (e.g., (behavioral) patterns, classifiers, clusters, structural objects, sets of rules, aggregation operations, reasoning schemes etc.). One of the challenges for systems based on Big Data is to provide the systems with high-level primitives of users for composing and building complex analytical pipelines over Big Data. Such primitives are very often expressed in natural language, and they should be approximated using low-level primitives, accessible from raw data. In Granular Computing (GrC), all such constructed and/or induced objects are called granules. To model interactive computations, performed by the agent in complex systems based on Big Data, we extend the existing approach to GrC by introducing complex granules (c-granules or granules, for short). Many advanced tasks, concerning complex systems based on Big Data may be classified as control tasks performed by agents aiming at achieving the high quality trajectories (defined by computations) relative to the considered target tasks and quality measures. Here, new challenges are to develop strategies to control, predict, and bound the behavior of the system based on Big Data at scale. We propose to investigate these challenges using the GrC framework. The reasoning, which aims at controlling the computational schemes from time-to-time, in order to achieve the required target, is called an adaptive judgement. This reasoning deals with granules and computations over them. Adaptive judgement is more than a mixture of reasoning based on deduction, induction and abduction. Due to the uncertainty the agents generally cannot predict exactly the results of actions (or plans). Moreover, the approximations of the complex vague concepts initiating actions (or plans) are drifting with time. Hence, adaptive strategies for evolving approximation of concepts with respect to time are needed. In particular, the adaptive judgement is very much needed in the efficiency management of granular computations, carried out by agents, for risk assessment, risk treatment, cost/benefit analysis. The approach, discussed in this paper, is a step towards realization of the Wisdom Technology (WisTech) program [2,3], and is developed over years of experiences, based on the work on different real-life projects.

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“…A good literature overview can be found in [9,10], therefore we refer only to selected papers which we regard vital for our research. We also provide overview of necessary regulations and some basic facts for copula approach in dependences modeling.…”

Section: Operational Risk Modeling Involves Various Problemsmentioning

confidence: 99%

Modeling Correlations in Operational Risk

Karwański

Grzybowska

2018

Acta Phys. Pol. A

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The key demand for banks' economic capital methodology is to ensure that the model covers all relevant sources of risk in the right way. Operational risk models treat the arising losses as stochastic variables. One of the problems encountered in modeling is the need of taking into account correlations between events. It is possible to build models for correlated events based on copula functions. But the problem is that the losses are related to isolated events and simple applications of copulas are not allowed. The authors present a new algorithm that shows a modied application of copulas to calculating operational risk. The calculations were done on real data that allows for examining the correlation impact on risk measurement. As an additional evaluation of the algorithm a reference model based on the ParetoLévy copulas was used.

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Modelling Operational Risk Using Bayesian Inference

Cited by 81 publications

References 42 publications

Using a time series approach to correct serial correlation in operational risk capital calculation

Using a time series approach to correct serial correlation in operational risk capital calculation

Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement

Modeling Correlations in Operational Risk

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