All in all selection of an approach requires careful consideration in order to

All in all selection of an approach requires careful

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these approaches are less risk sensitive and will not reflect the underlying operational risks. All in all, selection of an approach requires careful consideration in order to balance cost with accuracy, transparency, and potential benefits in minimum regulatory capital. There is no doubt that the AMA approach is preferable for large banks. The LDA approach is much more risk sensitive, due to the fact that banks can select their own loss severity distribution to (over) estimate the unexpected loss. A fat tailed distribution is preferable. Therefore, banks are granted a great flexibility to model the capital charge for operational risks. On the other hand, the LDA approach does not give a better insight into the cause of the risks. By choosing the Scorecard Approach, banks can manage their operational risk by focussing on the drivers of the losses. Perhaps a combination of the LDA approach and the Scorecard Approach is the best solution. 32
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8 Appendices Appendix A-Monte Carlo Simulation The Monte Carlo Method has been successfully used in scientific applications for at least 75 years. Monte Carlo Simulation is named after the famous Casino in the Mediterranean Principality of Monaco. However, the use of the name "Monte Carlo" does not mean to imply that the method is either a "gamble" or "risky". It simply refers to the manner in which individual numbers are selected from valid "representative collections of input data" so they can be used in an iterative calculation process. These "representative collections of data" are some sort of a "Frequency Distribution" that is converted to a probability distribution. The Iterative Process The steps of the iterative calculation used by the Monte Carlo simulation process are as follow: 1. Use the existing data to create a Cumulative Distribution Function for each input variable that will be used by the metric. 2. Create an empty Frequency Distribution Histogram that will be incremented during each iteration. 3. Start the iteration process: A. Loop over each input variable used by the metric a. Use a random number (generated by a pseudo-random-number generator) between 0 and 1 with the Cumulative Distribution Function to obtain a weighted value for each input variable. B. Use the weighted value of all input variables in the metric to calculate a representative answer . C. Use the representative answer to determine which bin in the final Frequency Distribution Histogram should be incremented. D. Increment the appropriate bin in the Frequency Distribution Histogram by 1. 4. Repeat Step 3 (A, B, C and D) if the final Frequency Distribution Histogram in Step 3.D, is not "smoothly varying" (and therefore complete). A large number of iterations (like 50,000) will ensure that this Frequency Distribution is complete. 5. Normalize the Frequency Distribution Histogram (forming a Discrete Probability Distribution Function) and then create its Discrete Cumulative Distribution Function (or, Discrete Probability Distribution).
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