# LO 43.6: Explain the use of scenario analysis and the hybrid approach in modeling

LO 43.6: Explain the use of scenario analysis and the hybrid approach in modeling operational risk capital.
Scenario analysis data is designed to identify fat-tail events, which is useful when calculating the appropriate amount of operational risk capital. The advantage of using scenario analysis is that data reflects the future through a process designed to consider what if scenarios, in contrast to the LDA which only considers the past. The major disadvantage of scenario analysis is that the data is highly subjective, and it only produces a few data points. As a result, complex techniques must be applied to model the full loss distribution,
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Topic 43 Cross Reference to GARP Assigned Reading – Girling, Chapter 12
as the lack of data output in scenario analysis can make the fitting of distributions difficult. In addition, small changes in assumptions can lead to widely different results.
There are many different approaches to scenario analysis, but whichever method is used, a scarcity of data points is likely. This makes pure scenario analysis a difficult approach to defend in estimating risk capital. Also, the more reliance there is on scenario analysis, the more robust the program must be because sometimes there is little or no loss data available and a model may need to rely purely on scenario analysis for a particular risk category. Consequently, it is acceptable to have different modeling techniques for various risk categories as long as the differences are justified. While some scenario-based models have been approved in Europe, U.S. regulators generally do not accept them.
In the hybrid approach, loss data and scenario analysis output are both used to calculate operational risk capital. Some firms combine the LDA and scenario analysis by stitching together two distributions. For example, the LDA may be used to model expected losses, and scenario analysis may be used to model unexpected losses. Another approach combines scenario analysis data points with actual loss data when developing frequency and severity distributions.
I n s u r a n c e
Banks have the option to insure against the occurrence of operational risks. The important considerations are how much insurance to buy and which operational risks to insure. Insurance companies offer polices on everything from losses related to fire to losses related to a rogue trader. A bank using the AMA for calculating operational risk capital requirements can use insurance to reduce its capital charge. However, the recognition of insurance mitigation is limited to 20% of the total operational risk capital required.
The LDA allows for a risk profiling of an institution, which can include the risk reducing effect of insurance, which then alters the aggregate loss distribution. Typically this is done by reducing the severity of the losses that exceed a given deductible in the insurance policy. In other words, insurance typically lowers the severity but not the frequency.
Operational risk capital may need to be billions of dollars, so it can be worthwhile to pursue insurance as a means to reduce the amount of capital needed. Insurance companies are attempting to accommodate industry needs through new insurance products that meet Basel requirements.
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Topic 43 Cross Reference to GARP Assigned Reading – Girling, Chapter 12
K e y C o n c e pt s
LO 43.1 The three methods for calculating operational risk capital requirements are (1) the basic indicator approach (BIA), (2) the standardized approach (TSA), and (3) the advanced measurement approach (AMA). Large banks are encouraged to move from TSA to the AMA in an effort to reduce capital requirements.
LO 43.2 The first requirement to use the AMA is that the model must hold sufficient capital to cover all operational risk losses for one year with a certainty of 99.9%. The second requirement is that internal loss data, external loss data, scenario analysis, and business environment internal control factors must be included in the model. The third requirement is that there must be a method for allocating capital that incentivizes good behavior.
LO 43.3 The loss distribution approach (LDA) relies on internal losses as the basis of its design. It uses internal losses as direct inputs, with the remaining data elements being used for stressing or allocation purposes. However, regardless of its model design, a bank must have at least three years of loss data. The advantage of the LDA model is that it is based on historical data relevant to the firm. The disadvantage is that the data collection period is likely to be relatively short and may not capture all fat-tail events.
LO 43.4 When developing a model of expected operational risk losses, the first step is to determine the likely frequency of events on an annual basis. The most popular distribution for modeling frequency is the Poisson distribution. In a Poisson distribution, there is only one parameter, \, which represents the average number of events in a given year. The next step in modeling expected operational risk losses is to determine the severity of an event. The most common and least complex distribution is to use a lognormal distribution.
LO 43.5 Once the frequency and severity distributions have been established, the next step is to use them to generate data points to better estimate the capital required at a 99.9% confidence level. Monte Carlo simulation is a method for combining frequency and severity distributions to produce additional data points that have the same characteristics as observed data points.
LO 43.6 Scenario analysis data is designed to identify fat-tail events and is useful in calculating the appropriate amount of operational risk capital. In the hybrid approach, loss data and scenario analysis output are both used to calculate operational risk capital.
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Topic 43 Cross Reference to GARP Assigned Reading – Girling, Chapter 12
Co n c e pt Ch e c k e r s
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Under the basic indicator approach (BIA), what is Alpha Banks capital charge if it has revenues of $100 million,$150 million, and $200 million in the first three years? A.$22.0 million. B. $22.5 million. C.$23.0 million. D. $23.5 million. Which of the following statements is not a requirement to apply the advanced measurement approach (AMA)? A. The model must hold capital to cover all operational risk losses for one year with a certainty of 99.9%. B. Internal loss data, external loss data, scenario analysis, and business environment internal control factors must be included in the model. C. Capital must be allocated to minimize risk. D. There must be a method for allocating capital that incentivizes good behavior. Which of the following reasons is not a disadvantage of the loss distribution approach (LDA) to modeling operational risk capital requirements? A. The LDA is based on historical data. B. Most firms have limited historical data. C. Fat-tail events may not be captured by modeling. D. Historical data is not reflective of the future. When modeling risk frequency, it is common to: A. use a Poisson distribution. B. assume that risks are highly correlated. C. assume risk frequency and severity are the same. D. use a straight-line projection from the most recent loss data. Extreme losses in the tail of the operational risk loss distribution most likely follow which type of process/distribution? A. Generalized Pareto distribution. B. Historical simulation method. C. Poisson distribution. D. Extreme value theory. Page 84 2018 Kaplan, Inc. Topic 43 Cross Reference to GARP Assigned Reading – Girling, Chapter 12 C o n c e pt C h e c k e r A n sw e r s The BIA is based on 15% of the banks annual gross income over a three-year period and is computed as follows: [(100 + 150 + 200) x 0.15] K b ia =$22.5 million
2. C There is no specific requirement under the AMA to minimize risk.
3. A An advantage of the LDA model is that it is based on historical data relevant to the firm.
4. A
It is common to use a Poisson distribution to model loss frequency. A Poisson distribution has a single parameter, X, which can be varied to accurately describe loss data.
5. A The most common and least complex approach for modeling extreme losses is to use a
lognormal distribution. However, low frequency losses may be a better fit to distributions such as Generalized Gamma, Transformed Beta, Generalized Pareto, or Weibull.
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The following is a review of the Operational and Integrated Risk Management principles designed to address the learning objectives set forth by GARP. This topic is also covered in:
St a n d a r d i z e d M e a su r e me n t A ppr o a c h f o r O pe r a t i o n a l Ri s k
Topic 44
E x a m F o c u s
The focus of this topic is on the calculation of the standardized measurement approach (SMA). In particular, candidates should understand how the business indicator (BI) is derived and how buckets are used to group banks by size such that the BI will have a different impact on the SMA given a banks bucket. Candidates should also know how to calculate the internal loss multiplier and the loss component, along with understanding how this component impacts the SMA given a banks bucket classification. The SMA has evolved over time from earlier approaches that were more model-based and allowed too much flexibility. Candidates should also be familiar with the Basel Committee s outline of general and specific criteria applicable to operational loss data.
T h e S t a n d a r d i z e d M e a s u r e m e n t A p p r o a c h