Challenges and Pitfalls in measuring operational risk
About the Lecture
The lecture Challenges and Pitfalls in measuring operational risk by Edu Pristine is from the course Archiv - Operational Risk. It contains the following chapters:
- Nature of Operational Loss Distribution
- Consequences of Working with Heavy Tailed Loss Data
- Amount of Data Required to Estimate Percentiles of Loss Distribution
- Methods of Extrapolating Beyond the Data
- Loss Distribution Approach to Modelling Operational Risk Losses
- Challenges in Validating Capital Models
Author of lecture Challenges and Pitfalls in measuring operational risk
Edu Pristine
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Excerpts from the accompanying material
... vary from quite small and predictable to large and unexpected. Operational losses are described as heavy-tailed data. Traditional mean and standard deviation concepts ...
... total loss is largely driven by relatively few observation in the data set Dominance of mixture: The heavy tailed distribution ...
... less precision relative to well behaved distributions, such as the normal distribution. Therefore larger sample sizes are needed to estimate high quantile losses with reasonable bounds. ...
... several underlying loss generating mechanism, but some are more likely to yield extreme events. The extreme events are not drawn from a known distribution and/or do not offer a pattern for estimation. Generalized Pareto Distribution (GPD) can also be used to model extreme events. GPD are flexible ...
... mandated by Basel Committee to estimate the annual loss for the bank. Assume each loss distribution to be independent and identically distributed. Annual losses for unit of ...
... Basel mandate. To fulfill this mandate, we need 1000 year bank data. Further, even if the data is available, there ...
