In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind.
While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR).
The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following.
- The modeling and management of operational risk.
- Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory.
- The pricing and hedging of operational risk and/or any risk transfer techniques.
- Data modeling external loss data, business control factors and scenario analysis.
- Models used to aggregate different types of data.
- Causal models that link key risk indicators and macroeconomic factors to operational losses.
- Regulatory issues, such as Basel II or any other local regulatory issue.
- Enterprise risk management.
- Cyber risk.
- Big data.
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This paper sets out techniques for: (a) identifying systematically emerging threats, their timescales, and interrelationships (eg, feedback loops and domino effects); (b) quantifying operational risks through structured scenario analysis processes that…
This paper investigates cyber loss data and focuses on quantifying the direct financial and compensatory losses emanating from cyber risks.
This paper presents truncation probability estimates for loss severity data and a consistent quantile scoring function on annual loss data as useful severity distribution selection criteria that may stabilize regulatory capital.
The use of business intelligence and predictive analytics in detecting and managing occupational fraud in Nigerian banks
The goal of this paper is to illustrate how Nigerian banks, and indeed banks elsewhere, can develop solutions that incorporate both BI and predictive analytics techniques in detecting, predicting, preventing and managing occupational fraud.
In this paper, the authors review some of the existing methods used to quantify operational risks in the banking and insurance industries.
This paper compares the levels of operational risk disclosure in the banking industries of India and Romania.
This paper discusses the framework within which to study how sample dependence is transferred from the data to the premiums via the density.
The objective of this paper is to analyze cyber risk from an operational risk perspective and to measure cyber risk empirically.
This paper focuses on conceptual and modeling frameworks in an attempt to explore qualitative and quantitative risk management techniques for hierarchical SoS risks, exemplifying the production systems for demonstration.
The aim of this paper is to systematically investigate the stability of operational value-at-risk (OpVaR) models when fitting heavy-tailed distributions to the relatively small sample sizes found in operational loss data.
The aim of this paper is to provide a new operational risk management framework to identify and mitigate the operational risk exposure arising from a new product.
This paper proposes an approach, called the loss distribution approach with segmented dependence (LDA-SD), which can model the different dependencies of HFLI and LFHI losses in the framework of LDA.
In this paper, the authors provide a comprehensive review of the different approaches developed to model operational risk, specifically focusing on the actuarial approach.
Global perspectives on operational risk management and practice: a survey by the Institute of Operational Risk (IOR) and the Center for Financial Professionals (CeFPro)
This paper presents survey results which represent comprehensive perspectives on operational risk practice, obtained from practitioners in a wide range of countries and sectors.
This paper evaluates the operational risk capital requirements of large US banks to determine whether they are forward looking, sensitive to banks’ current exposures and designed to allow for risk mitigation.
In this paper, the authors look at B-tests: methods by which it is possible to identify internal fraud among employees and partners of the bank at an early stage.
This paper proposes an alternative framework for setting banks’ operational risk capital, which allows for forward-looking assessments and limits gaming opportunities by relying on an incentive-compatible mechanism.
In this paper, the authors apply a dynamic extreme value theory (EVT) model based on a nonhomogeneous Poisson process incorporating covariates to estimate frequency, severity and risk measures for operational risk.
The aim of this paper is to propose a model that describes the integration of knowledge-based risks (via the processes of knowledge-based risk identification, analysis, evaluation and education) and knowledge-based risk repositories to support risk…
This paper is a historical case study of the GAS scandal and is the first to analyze it from the perspective of operational risk.