Operational Risk Capital Models

Operational Risk Capital Models is a guide for the implementation of state of the art operational risk capital models suitable for regulatory approval.

For insurers, Solvency II implementation has created the need, in both highly developed and less developed markets, for the development of these models that help to better understand risks, safe capital and compliance. For the banking industry, regulators in many countries in Africa, Asia and Latin America (as well as Europe) are pressing their local banks to implement advanced operational risk capital models. Banks that have made early implementation are looking to improve their capital models with new advances to match the increasing regulatory requirements.

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.