Challenges and Pitfalls in measuring operational risk
Über den Vortrag
Der Vortrag „Challenges and Pitfalls in measuring operational risk“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Operational Risk“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- 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
Dozent des Vortrages Challenges and Pitfalls in measuring operational risk
Edu Pristine
Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.
http://www.edupristine.com
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Auszüge aus dem Begleitmaterial
... 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 ...
