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Modeling Operational Risk
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics. (Matematisk statistik)
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The Basel II accord requires banks to put aside a capital buffer against unexpected operational losses, resulting from inadequate or failed internal processes, people and systems or from external events. Under the sophisticated Advanced Measurement Approach banks are given the opportunity to develop their own model to estimate operational risk.This report focus on a loss distribution approach based on a set of real data.

First a comprehensive data analysis was made which suggested that the observations belonged to a heavy tailed distribution. An evaluation of commonly used distributions was performed. The evaluation resulted in the choice of a compound Poisson distribution to model frequency and a piecewise defined distribution with an empirical body and a generalized Pareto tail to model severity. The frequency distribution and the severity distribution define the loss distribution from which Monte Carlo simulations were made in order to estimate the 99.9% quantile, also known as the the regulatory capital.

Conclusions made on the journey were that including all operational risks in a model is hard, but possible, and that extreme observations have a huge impact on the outcome.

Place, publisher, year, edition, pages
2012. , 62 p.
TRITA-MAT-E, 2012:14
Keyword [en]
Operational risk, Advanced Measurement Approaches, Loss Distribution
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-107435OAI: diva2:578791
Subject / course
Mathematical Statistics
Educational program
Master of Science in Engineering -Engineering Physics
Physics, Chemistry, Mathematics
Available from: 2012-12-19 Created: 2012-12-11 Last updated: 2012-12-19Bibliographically approved

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