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

Guégan, Dominique; Hassani, Bertrand K.

doi:10.21314/jop.2013.126

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…This may be due, as least in part, to flexibility on this specific issue in the US Final Rule (2007). 66 While such testing is beyond the scope of this study, operational risk losses are very likely to be serially correlated (see Guégan and Hassani, 2013), and other empirical examinations of this issue generally show that its deleterious effects on statistical inference can be very material (see van Belle, 2008). So this is an issue that should continue to be further addressed in future research.…”

Section: Robustnessmentioning

confidence: 99%

Estimating operational risk capital with greater accuracy, precision and robustness

Opdyke¹

2014

JOP

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The largest US banks and Systemically Important Financial Institutions are required by regulatory mandate to estimate the operational risk capital they must hold using an Advanced Measurement Approach (AMA) as defined by the Basel II/III Accords. Most of these institutions use the Loss Distribution Approach (LDA) which defines the aggregate loss distribution as the convolution of a frequency distribution and a severity distribution representing the number and magnitude of losses, respectively. Capital is a Value-at-Risk estimate of this annual loss distribution (i.e. the quantile corresponding to the 99.9%tile, representing a one-in-a-thousand-year loss, on average). In practice, the severity distribution drives the capital estimate, which is essentially a very large quantile of the estimated severity distribution. Unfortunately, when using LDA with any of the widely used severity distributions (i.e. heavy-tailed, skewed distributions), all unbiased estimators of severity distribution parameters appear to generate biased capital estimates due to Jensen's Inequality: VaR always appears to be a convex function of these severities' parameter estimates because the (severity) quantile being estimated is so large and the severities are heavy-tailed. The resulting bias means that capital requirements always will be overstated, and this inflation is sometimes enormous (sometimes even billions of dollars at the unit-of-measure level). Herein I present an estimator of capital that essentially eliminates this upward bias when used with any commonly used severity parameter estimator. The Reduced-bias Capital Estimator (RCE), consequently, is more consistent with regulatory intent regarding the responsible implementation of the LDA framework than other implementations that fail to mitigate, if not eliminate this bias. RCE also notably increases the precision of the capital estimate and consistently increases its robustness to violations of the i.i.d. data presumption (which are endemic to operational risk loss event data). So with greater capital accuracy, precision, and robustness, RCE lowers capital requirements at both the unit-of-measure and enterprise levels, increases capital stability from quarter to quarter, ceteris paribus, and does both while more accurately and precisely reflecting regulatory intent. RCE is straightforward to explain, understand, and implement using any major statistical software package.

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Section: Robustnessmentioning

confidence: 99%

Estimating operational risk capital with greater accuracy, precision and robustness

Opdyke¹

2014

JOP

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“…Bardoscia and Bellotti (2011) introduce a dynamic operational risk model, which incorporates the evolution of the losses in time and takes into account different time-correlations among the processes. Guegan and Hassani (2013) study the possibility of multiple losses triggered by one event by capturing autocorrelation and large losses simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

Incorporating single and multiple losses in operational risk: a multi-period perspective

Mizgier¹,

Wimmer

2017

Journal of the Operational Research Society

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Operational disruptions can have serious repercussions for firms over extended periods of time. In this work, we develop a multi-period model of operational risk. We define the loss process of operational disruptions as a sum of events triggering single and multiple losses.We empirically validate our approach using an extensive data set of operational disruptions experienced by firms from the financial services and manufacturing industry sectors. The results of our simulations point out that operational risk is significantly underestimated if the events leading to multiple losses are not accounted for in the firms' long-term capital planning.

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“…are independent too (note that modelling severities with autocorrelation is possible, see e.g. Guégan and Hassani (2013). F X (x; θ) is modelled by one of the following two-parameter distributions: Pareto, Lognormal, Log-logistic, Log-gamma.…”

Section: Opcar Estimation Frameworkmentioning

confidence: 99%

Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?

Peters¹,

Shevchenko²,

Hassani³

et al. 2016

JOP

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Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London.

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

Cited by 18 publications

References 14 publications

Estimating operational risk capital with greater accuracy, precision and robustness

Estimating operational risk capital with greater accuracy, precision and robustness

Incorporating single and multiple losses in operational risk: a multi-period perspective

Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?

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