Tackling the over-dispersion of operational risk: Implications on capital adequacy requirements

Feria-Domínguez, José Manuel; Jiménez-Rodríguez, Enrique; Sholarin, Ola

doi:10.1016/j.najef.2014.11.004

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…Finally, the equidispersion property of the Poisson process may be inadequate in practice: for example, in the Cruz study fraud loss data [23] the empirical variance doubles the mean. This overdispersion characteristic of the OpRisk has been also documented by [3,30] .…”

Section: Introductionsupporting

confidence: 60%

Analysis of an aggregate loss model in a Markov renewal regime

Ramírez‐Cobo

Carrizosa

Lillo

2021

Applied Mathematics and Computation

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In this article we consider an aggregate loss model with dependent losses. The loss occurrence process is governed by a two-state Markovian arrival process ( MAP 2 ), a Markov renewal process that allows for (1) correlated inter-loss times, (2) non-exponentially distributed inter-loss times and, (3) overdisperse loss counts. Some quantities of interest to measure persistence in the loss occurrence process are obtained. Given a real OpRisk database, the aggregate loss model is estimated by fitting separately the inter-loss times and severities. The MAP 2 is estimated via direct maximization of the likelihood function, and severities are modeled by the heavy-tailed, double-Pareto Lognormal distribution. In comparison with the fit provided by the Poisson process, the results point out that taking into account the dependence and overdispersion in the inter-loss times distribution leads to higher capital charges.

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Section: Introductionsupporting

confidence: 60%

Analysis of an aggregate loss model in a Markov renewal regime

Ramírez‐Cobo

Carrizosa

Lillo

2021

Applied Mathematics and Computation

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“…The important motivation of Poisson distribution from estimation point of view depends on mean–variance relation [20]. In over-dispersed Poisson model, an extra parameter is included which estimates how much larger the variance is than the mean [21]. This parameter estimate is then, used to correct the effects of the larger variance on the P values [22].…”

Section: Methodsmentioning

confidence: 99%

“…Applying iteratively re-weighted least squares in the more general case involves working with weights say . This implies that when variance is proportional to mean (not necessarily equal to mean), Poisson estimator is maximum Quasi-Poisson likelihood estimator and the model is said to Quasi-Poisson regression model [21]. The quasi-likelihood function for each independent observation, is defined aswhere V is some known function and suppose the expectation, is some function of parameters β i .…”

Section: Methodsmentioning

confidence: 99%

Quasi-Poisson versus negative binomial regression models in identifying factors affecting initial CD4 cell count change due to antiretroviral therapy administered to HIV-positive adults in North–West Ethiopia (Amhara region)

Tegegne¹,

Ndlovu²,

Zewotir³

2016

AIDS Res Ther

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BackgroundCD4 cells are a type of white blood cells that plays a significant role in protecting humans from infectious diseases. Lack of information on associated factors on CD4 cell count reduction is an obstacle for improvement of cells in HIV positive adults. Therefore, the main objective of this study was to investigate baseline factors that could affect initial CD4 cell count change after highly active antiretroviral therapy had been given to adult patients in North West Ethiopia.MethodsA retrospective cross-sectional study was conducted among 792 HIV positive adult patients who already started antiretroviral therapy for 1 month of therapy. A Chi square test of association was used to assess of predictor covariates on the variable of interest. Data was secondary source and modeled using generalized linear models, especially Quasi-Poisson regression.ResultsThe patients’ CD4 cell count changed within a month ranged from 0 to 109 cells/mm3 with a mean of 15.9 cells/mm3 and standard deviation 18.44 cells/mm3. The first month CD4 cell count change was significantly affected by poor adherence to highly active antiretroviral therapy (aRR = 0.506, P value = 2e−16), fair adherence (aRR = 0.592, P value = 0.0120), initial CD4 cell count (aRR = 1.0212, P value = 1.54e−15), low household income (aRR = 0.63, P value = 0.671e−14), middle income (aRR = 0.74, P value = 0.629e−12), patients without cell phone (aRR = 0.67, P value = 0.615e−16), WHO stage 2 (aRR = 0.91, P value = 0.0078), WHO stage 3 (aRR = 0.91, P value = 0.0058), WHO stage 4 (0876, P value = 0.0214), age (aRR = 0.987, P value = 0.000) and weight (aRR = 1.0216, P value = 3.98e−14).ConclusionsAdherence to antiretroviral therapy, initial CD4 cell count, household income, WHO stages, age, weight and owner of cell phone played a major role for the variation of CD4 cell count in our data. Hence, we recommend a close follow-up of patients to adhere the prescribed medication for achievements of CD4 cell count change progression.

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“…There are at least two reasons to justify our hypothesis that parent banks' financial characteristics affect their entities differently. First, the problem of the foreign bank's solvency is part of the parent bank's overall risk management (Feria-Dominguez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Determinants of the capital adequacy ratio of foreign banks’ subsidiaries: The role of interbank market and regulation

Mili

Sahut²,

Trimeche

et al. 2017

Research in International Business and Finance

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This paper examines the factors influencing the capital adequacy ratio (CAR) of foreign banks. We test whether the CAR of subsidiaries and branches in developed and developing countries depends on the same factors. We use data from 310 subsidiaries and 265 branches to test the impact of the parent banks' fundamentals on subsidiaries' and branches' capital ratios. We also study how the economic condition and regulatory environment in a bank's home country determine foreign banks' CAR. Our results provide strong evidence that the CAR of subsidiaries and branches operating in developing and developed countries do not depend on the same set of explanatory factors. We also find that the regulatory framework of a parent bank's home country affects the capitalization of its foreign subsidiaries in the host countries. Finally, we show that specific variables of the parent bank have a stronger effect for foreign banks highly related to the interbank market.

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Tackling the over-dispersion of operational risk: Implications on capital adequacy requirements

Cited by 11 publications

References 24 publications

Analysis of an aggregate loss model in a Markov renewal regime

Analysis of an aggregate loss model in a Markov renewal regime

Quasi-Poisson versus negative binomial regression models in identifying factors affecting initial CD4 cell count change due to antiretroviral therapy administered to HIV-positive adults in North–West Ethiopia (Amhara region)

Determinants of the capital adequacy ratio of foreign banks’ subsidiaries: The role of interbank market and regulation

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