4

55

 

Risk Books (Visit publisher website)

2006

English

 

 

 

Basic

 

 

Operational risk, operational risk measurement, loss distribution approach, scenario analysis, severity distribution, tail-adjusted normal density function, mean, variance, kurtosis, mean confidence interval, standard deviation confidence interval, kurtosis confidence interval, maximum-likelihood estimation, frequency distribution, binomial distribution, negative binomial distribution, Poisson distribution, economic capital calculation, steps to modeling operational risk economic capital exposure, expected tail loss, qualitative adjustments to economic capital, validation, diversification, correlation, event classification, loss data classification scheme, mixing internal and external data, disclosure bias, reporting bias, search bias, scaling losses

 

This chapter presents a logical approach to the development of a mathematical model for operational risk economic capital. The starting point is understanding loss data in terms of a severity distribution (paying particular attention to the tails) and a frequency distribution for operational risk losses at the business unit level. With these distributions explained, the chapter then turns to the steps in the modeling operational risk economic capital exposure. Three models are considered and explained. The remainder of the chapter explains the issues with diversification, scenario analysis, and operational risk data.

 

4.1 Severity distribution for historical loss data
4.2 Frequency distribution: how to quantify the occurences of loss events
4.3 Economic capital calculation
4.4 Operational risk economic capital diversification
4.5 Scenario analysis
4.6 Event classification
4.7 Mixing internal data with external data
4.8 Conclusion
4.9 References

 

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The book firmly focuses on practical solutions to modelling issues.

Helps you set up a strong programme with quantification in mind, providing good quality information for modelling, in order to deliver the numbers you need for regulatory approval, and deliver value to your business lines.

Presents many complex topics in a highly accessible manner.

Five comprehensive sections succinctly guide you through the following key topics:

* The AMA framework: Outlines a general approach to constructing an advanced measurement approach framework within your firm.
* Modelling basics: Gets you fluent in the fundamentals of operational risk modelling with discussion of general concepts and an analysis of the main mathematical models regularly used to analyse loss data.
* Modelling challenges: Shows you how models can be adjusted to overcome limitations in the underlying data.
* Alternative modelling approaches: This section gives you a general framework for thinking about qualitative elements and their role in modelling - including scenario analysis, key risk indicators, and how to overcome the challenges in the modelling of less tangible risks, such as technology risk.
* Comparative views of implementation: To help you gain a broader understanding of how your colleagues around the world are implementing their op risk solutions – the final section contains an overview of an op risk framework at a US bank, plus implementation studies from Spain and Germany.