Supervisory Guidelines for the Advanced Management Approaches by Edu Pristine

video locked

About the Lecture

The lecture Supervisory Guidelines for the Advanced Management Approaches by Edu Pristine is from the course Archiv - Operational Risk. It contains the following chapters:

  • Advanced Management Approach
  • Gross vs. Net Loss Data
  • Internal Loss Data Thresholds
  • Operational Risk Capital Charge
  • Operational Risk Management Framework (ORMF)
  • Validation & Verification
  • Operational Risk Categories
  • Modeling Operational Risk Distributions

Author of lecture Supervisory Guidelines for the Advanced Management Approaches

 Edu Pristine

Edu Pristine


Customer reviews

(1)
5,0 of 5 stars
5 Stars
5
4 Stars
0
3 Stars
0
2 Stars
0
1  Star
0


Excerpts from the accompanying material

... Additionally basel committee formed standards implementation group and operational risk subgroup for identifying and managing operational risk. Flexible approach to develop models to quantify the operational risk capital required for banks against operational risks, frauds, human error, IT errors ...

... can be classified as: Internal Loss Data, External Data, Scenario ...

... of the loss type. Banks must be able to quantify the losses and also the recoveries for the losses which will also include insurance recoveries. Gross loss is the loss ...

... may or may not be directly linked to the event provisions - reserves for potential operational loss impact pending losses - losses ...

... Losses must exclude: Insurance Premiums Maintenance contract costs. Business Enhancement Costs. Timing losses which are ...

... for data collection & modeling. Include material operational loss event data & thresholds must be reasonable enough. Support the credibility & accuracy or measures. Grouping of losses allowed if they are caused by ...

... must be used in different combinations & bank must prove that combinations are sufficient from modeling perspective. Data Elements are: Internal loss data - Used in frequency & severity distributions & reflects internal ...

... Operational Risk Management System (ORMS) includes: Statistical methodologies systems, data & capital allocation outputs, bank policies & processes regarding risk identification, measurement & management. Internal governance ...

... the processes in a Bank. Validation & verifications must be based on independent assessments so as to have no conflicts of interest between different subunits Differences between validation & verification are: Validation ...

... development phase. Verification of ORMF activities must ensure that: ORM policies and procedures are transparent, sound & documented. Corporate operational risk function, business unit functions & operational risk management governance committees & processes are ...

... made if necessary. Outcome analysis is effective & involves data comparisons between loss ...

... AMA data elements - External, Internal, Scenario & BEICS required procedures for approval of new or modified estimation models or methodologies. Detailed ...

... Responsible personnel must have access to all documentation & reports which ...

... does not result in large losses being left. Reference dates typically used are date of occurrence, date of discovery & accounting date. Accounting date & discovery dates are typically used for building the dataset. Date of ...

... will influence the capital char computation. AMA requires that Bank's risk measurement system must be sufficiently granular to capture major drivers of operational risk affecting tails of loss distributions. Supervisory Guidelines regarding ...

... capital requirements. Supervisory Guidelines regarding the building of the calculation dataset include: Policy must be in place which identifies that a loss is being recorded in internal or external loss event database. Banks should not use losses net of ...

... terms of capital requirements, flexible, simple & having well specified characteristics. Banks should evaluate distributions selected based on appropriate statistical techniques and preference must be given to highly sensitive tail distributions. Use statistical tests to examine the statistical properties of each Operational ...