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 outlines several approaches to benchmarking operational loss projections under stressed scenarios using both accounting metrics and historical loss experience.
Near misses represent a primary information source to analyze the operational risk exposure of a company, since they can reveal gaps in the control environment. The model proposed in this paper aims at identifying the most dangerous events that could…
This paper studies the growth by acquisition strategy embarked upon by a mid-sized UK bank, the Co-operative Bank; this strategy was a disaster, leaving a heretofore successful bank in dire trouble and on the block for buyers at a substantial discount to…
In this paper, the author takes a data-driven approach and combines the individual active taxonomies of sixty large financial institutions (fifty-eight for construction and two for validation) to create a coherent new reference taxonomy: the ORX…
What do risk disclosures reveal about banking operational risk processes? Content analysis of banks’ risk disclosures in the Visegrad Four countries
This paper provides a rationale for adopting quantitative buffer capital, designed to absorb variations due to measurement errors, especially those originating from the estimation risk.
Difference between the determinants of operational risk reporting in Islamic and conventional banks: evidence from Saudi Arabia
In this study, the author investigates the operational risk reporting practices of Islamic banking institutions (IBIs) and conventional banks (CBs) in Saudi Arabia. Moreover, the author explores the joint effect of banking characteristics, corporate…
The authors propose a model for conduct risk losses, in which conduct risk losses are characterized by having a small number of extremely large losses (perhaps only one) with more numerous smaller losses.
This paper points out the peculiarities of cyber insurance contracts compared with the classical nonlife insurance contracts from both the insurer’s and the insured’s perspectives. The main actuarial principles that are fundamental to any valuation in a…
Measuring expected shortfall under semi-parametric expected shortfall approaches: a case study of selected Southern European/Mediterranean countries
In this paper, the authors investigate the applicability of semi-parametric approaches for estimating expected shortfall.
The impact of enterprise risk management on the performance of companies in transition countries: Serbia case study
In this paper, seven hypotheses are defined, on the basis of which a theoretical model is developed to examine how different sources of enterprise risk affect the operational performance of Serbian companies and their risk of losing market position.
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.