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Operational Risk Capital Models

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Edited by: Rafael Cavestany, Brenda Boultwood, Escudero. Laureano F.

ISBN13: 9781782722014
Published: April 2015
Publisher: Risk Books
Country of Publication: UK
Format: Paperback
Price: £145.00

Usually despatched in 1 to 3 weeks.

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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Banking and Finance
Marcelo Cruz
Rafael Cavestany

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