Conduct Risk - Distribution Models with Very Thin Tails

Mitic, Peter

doi:10.2139/ssrn.2967412

Cited by 2 publications

(3 citation statements)

References 7 publications

(7 reference statements)

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“…Of these, CPBP is often treated in a different way from the others because it tends to contain exceptionally large losses, which distort calculated capital values unacceptable. See, for example, [8] or [9]. Consequently, that category is excluded from this analysis.…”

Section: Operational Risk: Categorisationmentioning

confidence: 99%

“…The upper quartile scenario models an extreme condition by inflating the largest losses by 25%. The largest losses are known to have a significant effect on regulatory capital (see [8]). Technically, the stress factors in the upper quartile were supplied by reading a spreadsheet containing instructions to manipulate selected losses in the way required.…”

Section: Projections: Economic and Scenario Stressmentioning

confidence: 99%

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A Framework for Analysis and Prediction of Operational Risk Stress

Mitic

2021

MCA

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A model for financial stress testing and stability analysis is presented. Given operational risk loss data within a time window, short-term projections are made using Loess fits to sequences of lognormal parameters. The projections can be scaled by a sequence of risk factors, derived from economic data in response to international regulatory requirements. Historic and projected loss data are combined using a lengthy nonlinear algorithm to calculate a capital reserve for the upcoming year. The model is embedded in a general framework, in which arrays of risk factors can be swapped in and out to assess their effect on the projected losses. Risk factor scaling is varied to assess the resilience and stability of financial institutions to economic shock. Symbolic analysis of projected losses shows that they are well-conditioned with respect to risk factors. Specific reference is made to the effect of the 2020 COVID-19 pandemic. For a 1-year projection, the framework indicates a requirement for an increase in regulatory capital of approximately 3% for mild stress, 8% for moderate stress, and 32% for extreme stress. The proposed framework is significant because it is the first formal methodology to link financial risk with economic factors in an objective way without recourse to correlations.

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Section: Operational Risk: Categorisationmentioning

confidence: 99%

Section: Projections: Economic and Scenario Stressmentioning

confidence: 99%

A Framework for Analysis and Prediction of Operational Risk Stress

Mitic

2021

MCA

Self Cite

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“…VaR that is "too high" often arises from GP-or GEV-like fitted distributions as well as when attempting to fit any distribution to a small number of very large losses. The latter problem is described in Mitic (2017). Whereas the EB and its CLT equivalent are both aimed at estimating minimum VaR, no technique currently exists for estimating maximum VaR.…”

Section: Indicators For the Validation Of Fitted Distributionsmentioning

confidence: 99%

A central limit theorem formulation for empirical bootstrap value-at-risk

Mitic¹,

Bloxham²

2018

JRMV

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In this paper, the importance of the empirical bootstrap (EB) in assessing minimal operational risk capital is discussed, and an alternative way of estimating minimal operational risk capital using a central limit theorem (CLT) formulation is presented. The results compare favorably with risk capital obtained by fitting appropriate distributions to the same data. The CLT formulation is significant in validation because it provides an alternative approach to the calculation that is independent of both the empirical severity distribution and any dependent fitted distribution.

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Conduct Risk - Distribution Models with Very Thin Tails

Abstract: Regulatory requirements dictate that financial institutions must calculate risk capital (funds that must be retained to cover future losses) at least annually. Procedures for doing this have been well-established for many years, but recent developments in the treatment

Cited by 2 publications

References 7 publications

A Framework for Analysis and Prediction of Operational Risk Stress

A Framework for Analysis and Prediction of Operational Risk Stress

A central limit theorem formulation for empirical bootstrap value-at-risk

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