Duration:

2 days

2 days

Location:

Prague, NH Hotel Prague

Prague, NH Hotel Prague

- Operational Risks Events and their Loss Impacts
- Collecting, Validating and Using Loss Data
- Estimating the Loss Distribution Function
- Using Extreme Value Theory to Quantify OP Risk
- Scenario Analysis and Stress Testing
- Regulatory and Economic Capital Assessment and Allocation
- Verifying, Validating and Back Testing of OP Risk Models
- OP Risk Governance
- Risk Reporting, Management and Control

The purpose of this workshop is to give you a good, "hands-on" understanding of methods and tools for measuring and managing of operational risk.

We will start with a brief review of the different types of operational risk events and their loss impacts.

We then explain how operational risk can be modeled using internal and external loss data, self-assessments and other techniques. We discuss the problem in collecting and validating relevant (and sufficient) data for reliable estimates of a loss distribution. We give examples of how data for loss frequency by business line and event type can be obtained from external loss databases such as ORX and we demonstrate how this data can be combined with internal data and qualitative assessments to construct a loss distribution and to calculate expected and unexpected losses. We explain and demonstrate how the loss distribution and its associated parameters can be used to calculate regulatory and economic capital.

Further, we explain, discuss and demonstrate how to verify and validate the OP risk framework and risk models. Methods include back testing and statistical testing. Specifically, we explain why the difficulties in conducting back testing owing to data availability and we show how it may be possible to secure relatively robustness of operational risk measurement with statistical testing.

We also explain and demonstrate how to deal with high severity/low frequency events and we discuss how correlations between different OP risk events can be incorporated, and how scenario analysis may be used to examine sensitivity of the estimation to changes in the assumptions in order to check the robustness of models in stress situations.

Finally, we discuss practical issues related to management, control and reporting of OP risk.

We will start with a brief review of the different types of operational risk events and their loss impacts.

We then explain how operational risk can be modeled using internal and external loss data, self-assessments and other techniques. We discuss the problem in collecting and validating relevant (and sufficient) data for reliable estimates of a loss distribution. We give examples of how data for loss frequency by business line and event type can be obtained from external loss databases such as ORX and we demonstrate how this data can be combined with internal data and qualitative assessments to construct a loss distribution and to calculate expected and unexpected losses. We explain and demonstrate how the loss distribution and its associated parameters can be used to calculate regulatory and economic capital.

Further, we explain, discuss and demonstrate how to verify and validate the OP risk framework and risk models. Methods include back testing and statistical testing. Specifically, we explain why the difficulties in conducting back testing owing to data availability and we show how it may be possible to secure relatively robustness of operational risk measurement with statistical testing.

We also explain and demonstrate how to deal with high severity/low frequency events and we discuss how correlations between different OP risk events can be incorporated, and how scenario analysis may be used to examine sensitivity of the estimation to changes in the assumptions in order to check the robustness of models in stress situations.

Finally, we discuss practical issues related to management, control and reporting of OP risk.

- Overview of Operational Risk
- Operational Risks Events and Measurement Methods
- Regulatory Treatment of Operational Risk
- Establishing an Operational Risk Management Environment
- Overview of Methods for Measuring OR

- Internal and External Data Sources
- Loss Data Workflow
- Risk Indicator Workflow
- Problems in Collecting Internal Data
- The Operational Riskdata eXchange (ORX)
- Structure and Content of the ORX Database
- Uses of the ORX Database
- Loss Scaling Relationships
- Empirical Findings

- The Basel Loss Data Collection Exercise
- Dealing with Operational Loss Data related to Credit or Market Risk
- Loss Data Categorization and Loss Attributes

- An Op Risk Management Framework
- The Modelling Process
- Measures of Risk
- Loss Distribution Approach
- Models for the Size of Losses
- Models for the Number of Losses
- Aggregate Loss Models
- Modelling the Random Variables/ Parameter estimation

- Using Extreme Value Theory to Quantify OP Risk
- Extreme Value Theory
- Maximum Likelihood Estimation
- Estimating the Tails of the Loss Distribution
- Expected Shortfall (ES)

- Multivariate Models
- Calibrating Models for Operational Risk
- Scenario Generations Using Multifactor Models
- Key Control Indicators and the Volatility of Losses
- Calculating Regulatory and Economic Capital for Operational Risk

- Purposes of Verification and Validation
- From “Trust Me” to “ Prove & Evidence Me World”
- The Nature of Operational Loss Distributions
- Consequences of Working with Heavy-Tailed Loss Data
- Data Sufficiency
- How to Model a Data Set Containing Extreme Values?
- How Accurate is a Model in the Ideal Situation?

- Loss Distribution Approach
- Dominance of High-Severity, Low-Frequency Losses
- Negative Diversification Benefits
- Sensitivity to Loss Categorization

- Validating Capital Models
- Identification
- Review Process
- Approval and Monitoring
- Input data, Assumptions, Logic and Theory, Computer Code, Output
- Back-testing and Out-of-Sample Testing

- OR Framework Validation
- Logic Check for Overall OR Framework and Framework Elements

- Best OR Management Practices
- Seven Guiding Principles for OR Management
- OR Governance Structure
- Responsibilities
- Senior Management
- Internal Audit
- Operational Risk Reporting
- Operational Risk Control
- Operational Cost Control

- The RCSA Process
- RCSA Planning Stage
- Conduct RCSA
- RCSA Review & Validation
- Inherent Risk Rating Matrix
- Continuous Improvement

- Active Operational Risk Management – Risk Capital, Capital Allocation and Performance Measurement

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