Operational Risk Capital Models - Product Image

Operational Risk Capital Models

  • ID: 3169268
  • Book
  • Region: Global
  • Risk Books
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Operational Risk Capital Models enables you to model your operational risk capital to ensure the model meets regulatory standards. It describes the process end to end, from the capture of the required data to the modelling and VaR calculation, as well as the integration of capital results into your institution’s daily risk management.

Chapters include:

- Modelling Challenges
- Regulatory Compliance and Supervision
- Operational Loss Modelling
- External Data Rescaling
- Scenario Analysis Framework and Modelling
- BEICFs Modelling and Integration into Capital Model
- Capital Results Integration into Business Planning and Risk Appetite
- Hybrid Model Construction: Integrating ILD, ED and SA

Operational Risk Capital Models is essential for the creation of op risk capital models for both regulatory compliance and improving risk management practices.The book addresses and resolves the challenges in the implementation of advanced operational risk capital models by presenting a highly detailed end-to-end process for the capital model construction, compliance and integration into management.

The first part of the book describes a robust framework for the definition and capture of the four data elements: Internal Loss Data, External Data, Scenario Analysis and Business Environment Internal Control Factors. This part includes topics such as the validation of scenario analysis and the use of business environment and internal control factors as inputs to the capital model. It provides insights for mitigating cognitive biases in scenario analysis and defines a common understanding for operational loss. It also presents state-of-the-art methods for expert judgment elicitation (Structured Expert Judgment) and their application into operational risk scenario analysis.

The second part presents the exhaustive modelling and integration of the four data elements to compute operational risk VaR, capital and depict the operational risk profile of the institution. This part addresses all standard and more advanced topics, such as the modelling of BEICFs and their use in capital allocation, correlation calculation and ex-post capital adjustment; modelling of scenario analysis including GoF, tail control and more; determination of the optimal modelling granularity and threshold (up to 8 different methods); fitting distributions with old and new loss data by the use of a decay factor; analysis of capital instability (the resampling and what-if methods); various methods for external data re-scaling; construction of a hybrid model using credibility theory; operational risk dependencies including frequency-severity dependence and the use of expert judgment in their elicitation; different methods for capital allocation (contribution to expected shortfall, Heuler allocation, incremental analysis and others); backtesting of severities, frequencies and total losses; stress testing under different approaches including the modified LDA, regression, historical analysis, scenario analysis based and more.

In the third part, the work turns into the integration of capital results into the day to day management: embedding of the operational risk profile into strategic and operational business planning process; operational risk appetite definition, cascading down, monitoring and adherence; and the risk/reward evaluation of the effectiveness of controls and mitigation plans (insurance, action plans, critical infrastructure protection, operational risk predictive models and the determination of the optimal mitigation strategy using adversarial risk analysis).

Finally, the book's appendices examine in detail the distributions used in operational risk modelling including truncated, shifted, mixtures, empirical and plain vanilla parametric distributions; different credibility theory models, optimization methods used in operational risk modelling and business risk modelling.
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PART I - Capture and Determination of the Four Data Elements

1. Collection of Operational Loss Data: ILD and ED
Brenda Boultwood

- Towards a Common Understanding of Operational Loss
- Completeness of Data Collection
- Consistency with Accounting
- External Data

2. Scenario Analysis Framework and BEIFCs Integration
Rafael Cavestany, Brenda Boultwood and Daniel Rodriguez

- Scenario Support Data and Preparation
- Scenario Rating
- Scenario Validation
- BEICFs as an Input into Scenario Analysis

PART II - General Framework for Operational Risk Capital Modelling

3. Loss Data Modelling: ILD and ED
Rafael Cavestany and Daniel Rodriguez

- Exploratory Analysis and Selection of a Homogeneous Data Sample
- Optimal Modelling Granularity
- Tail Shape and Threshold Determination through Extreme Value Theory
- Severity Distributions Fitting
- Frequency Distribution Fitting
- Goodness-of-Fit (GoF) Evaluation
- Stability Analysis of Capital Estimates, Distribution Parameters, and GoF
- Evaluating if the Capital Estimates are Realistic
- External Data Rescaling
- Definition of the Loss Data Modelling Process

4. Scenario Analysis Modelling
Rafael Cavestany

- Translating Scenario Analysis Questions into Distribution Characteristics
- Fitting a Full Distribution to Scenario Analysis
- Distribution Shape Control during the Scenario Distribution Fit
- Goodness of Fit in Scenario Analysis
- Splitting Scenario into Lower Organizational Entities

5. BEICFs Modelling and Integration into Capital Model
Rafael Cavestany

- Ex-post Capital Adjustment Driven by BEICFs
- Modelling BEICFs
- Qualitative and Structured Determination of Correlations based on BEICFs
- Capital Attribution Driven by BEICFs

6. Hybrid Model Construction: Integration of ILD, ED and SA
Rafael Cavestany, Daniel Rodriguez and Fabrizio Ruggeri

- Credibility Theory: Determining the Weights for ILD, ED and SA in the Hybrid Model
- The Mixture Approach
- The Bayesian Approach
- Tail Complementing with External Data Losses
- Tail Complementing with Scenarios
- Mixing Distribution Properties from Different Data Elements during the Fit

7. Derivation of the Joint Distribution and Capitalisation of Operational Risk
Rafael Cavestany

- Monte Carlo Simulation
- Single Loss Approximation: Analytical Derivation of the Loss Distribution
- Operational Risk Correlations
- Using Copulas for Replicating Operational Risk Dependencies
- Capitalization of Operational Risk
- Allocation of Operational Risk Capital
- Operational Risk Profile Measurement

8. Backtesting, Stress Testing and Sensitivity Analysis
Rafael Cavestany and Daniel Rodriguez

- Backtesting of Severities
- Backtesting of Annual Frequency
- Backtesting of Annual Total Losses
- Stress-testing of Severities and Frequencies
- Stress-testing of Operational Risk Correlations

9. Evolving from a Plain Vanilla to a State of the Art Model
Rafael Cavestany

PART III - Use Test, Integrating Capital Results into the Institution´s Day-To-Day Risk Management

10. Strategic and Operational Business Planning and Monitoring
Lutz Baumgarten, Rafael Cavestany and Brenda Boultwood

- Integrating the Operational Risk Profile into the Strategic and Operational Planning
- Integrating Capital Results into the GRC Risk Reporting
- ORA for Monitoring the Strategic and Business Plan

11. Risk/reward Evaluation of the Mitigation and Control Effectiveness
Rafael Cavestany and Javier Moguerza

- Insurance Programmes: Evaluation of their Mitigation Impact
- Risk/reward Evaluation of the Mitigation Impact of Action Plans
- Internal Audit Non-Conformities Evaluation
- Process Improvement: Six Sigma and Operational Risk
- Operational Loss Prediction Analytics
- Adversarial Risk Analysis: Linking Risk Measurement with Optimal Mitigation

Appendix I - Distributions for Modelling Operational Risk Capital
Daniel Rodriguez

Appendix II - Credibility Theory
Daniel Rodriguez

Appendix III – Mathematical Optimization Methods Required for Operational Risk Modelling and Other Risk Mitigation Processes
Laureano Escudero

Appendix IV – Business Risk Quantification
Lutz Baumgarten
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