A dynamical approach to operational risk measurement

Bardoscia, Marco; Bellotti, R.

doi:10.21314/jop.2011.088

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…The loss generating process topic has also been addressed in Chernobai and Yildirim (2008) and Baroscia and Bellotti (2012). Focusing on the latter, the presence of autocorrelation between incidents is sometimes intuitive e.g.…”

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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“…The key step in the procedure to determine the distribution of idiosyncratic parameters across banks is to assign values to the 13 location coefficients 𝜔 in Equation (7). As outlined in Appendix A, in order to roughly match aggregate frequency and severity numbers to those published by ORX for given years, we only need to focus on parameters 𝛼 i,1 and 𝛼 i,0 which nearly control the frequency and severity respectively.…”

Section: Parameter Calibrationmentioning

confidence: 99%

An internal fraud model for operational losses in retail banking

Paredes,

Vega

2023

Appl Stoch Models Bus & Ind

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This article presents a novel dynamic model for internal fraud losses in the retail banking sector, incorporating internal factors such as ethical quality of workers and bank risk controls. The model's parameters are calibrated for each bank in the Operational Riskdata eXchange (ORX) consortium, based only on publicly available exposure indicators. The model generates simulated internal operational losses, exhibiting standard stochastic properties and tail behavior that closely align with actual operational losses. At an aggregate level, the model endeavors to replicate the average frequency and severity of losses observed within the internal fraud—retail banking category. Moreover, we identify macro‐environmental factors that exert influence over the severity and frequency of model‐simulated losses, consistent with findings in the existing literature.

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“…Based on this strand of the literature, Bardoscia and Bellotti (2011) models the amount of operational losses recorded at a certain time in a certain process.…”

Section: Related Literaturementioning

confidence: 99%

An internal fraud model for operational losses in retail banking

Paredes¹,

Vega²

2020

Preprint

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This paper develops a dynamic internal fraud model for operational losses in retail banking. It considers public operational losses arising from internal fraud in retail banking within a group of international banks. Additionally, the model takes into account internal factors such as the ethical quality of workers and the risk controls set by bank managers. The model is validated by measuring the impact of macroeconomic indicators such as GDP growth and the corruption perception upon the severity and frequency of losses implied by the model. In general,results show that internal fraud losses are pro-cyclical, and that country specific corruption perceptions positively affects internal fraud losses. Namely, when a country is perceived to be more corrupt, retail banking in that country will feature more severe internal fraud losses.

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A dynamical approach to operational risk measurement

Cited by 9 publications

References 11 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

An internal fraud model for operational losses in retail banking

An internal fraud model for operational losses in retail banking

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