The Basel Committee's 2014 revision of its operational risk capital framework, along with the multi-billion-dollar settlements that financial institutions had to make with financial authorities, has made operational risk the key focus of risk management. The Journal of Operational Risk stimulates active discussions of practical approaches to quantify, model and manage this risk, also discussing current issues in the discipline, and is essential reading for keeping practitioners and academics informed of the latest research in operational risk theory and practice.
The Journal of Operational Risk considers submissions in the form of research papers and forum papers, on the following, but not limited to, topics:
- Modelling and management of operational risk
- Recent advances in techniques used to model operational risk, for example: copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory
- Pricing and hedging of operational risk and/or any risk transfer techniques
- Data modelling external loss data, business control factors and scenario analysis
- Models used to aggregate the 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
The author of this paper explores the reasons for the pending demise of the advanced measurement approach (AMA) to operational risk.
In this paper, the author studies how asymptotic normality does, or does not, hold for common severity distributions in operational risk models.
The aim of this paper is to integrate prior information into a robust parameter estimation via OBR-estimating functions.
This paper uses simulation studies and an example of operational risk modeling to show the necessity and benefit of using RMT to fit high-dimensional t-copulas in risk modeling.
Operational risk and the Solvency II capital aggregation formula: implications of the hidden correlation assumptions
The authors of this paper analyze the Solvency II standard formula for capital risk aggregation in relation to the treatment of operational risk capital.
The author of this paper assesses operational loss data and its implications for risk capital modeling.
Comments on the Basel Committee on Banking Supervision proposal for a new standardized approach for operational risk
In this paper, the behavior of the SMA is studied under a variety of hypothetical and realistic conditions, showing that the simplicity of the new approach is very costly.
Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?
This paper discusses and studies the weaknesses and pitfalls of the SMA and the implicit relationship between the SMA capital model and systemic risk in the banking sector.
The authors of this paper assess the right-hand tail of an insurer’s loss distribution for a specified period (a year), presenting and analyzing six different approaches in doing so.
A simulation comparison of aggregation periods for estimating correlations within operational loss data
This paper investigates the differences in the values of correlations based on different aggregation periods of time series loss data.
How to turn uncertainties of operational risk capital into opportunities from a risk management perspective
Going beyond the regulatory requirements to operational risk measurement, the authors of this paper aim to provide relevant business applications to a bank.
This paper focuses on a comparison of the capital for Indian banks as required by the current regime for capital charge calculation, versus the possible revised Standardised Approach.
To enable autocorrelation in the frequency distribution, this paper proposes a significant generalization of the LDA model that involves treating operational risk as a Lévy jump-diffusion.
This paper empirically tests for correlations between fraud and the macroeconomy.
This paper examines and compares alternative ways of solving the problem of determining the density of aggregate losses.
A simulation comparison of quantile approximation techniques for compound distributions popular in operational risk
The objective of this paper is to compare numerical approximation techniques in terms of their practical usefulness and potential applicability in an operational risk context.
This paper proposes a new approach for determining OpVaR using an inhomogeneous counting process based on Panjer recursion as the frequency distribution.
This paper shows that it is an "inconvenient truth" that the largest losses by banks are not firm specific.
This paper focuses on the distribution of correlations among aggregate operational risk losses.
Application of the convolution operator for scenario integration with loss data in operational risk modeling
This paper addresses the uncertainty in scenario analysis and produces a combined loss distribution.
This paper studies alternative mixing models for external data for a particular risk class.
This paper identifies three steps in sourcing risk.
This paper makes use of the power-law mimicry properties of the truncated lognormal distribution and shows how they fit operational risk data considerably well.
A weighted likelihood estimator for operational risk data: improving the accuracy of capital estimates by robustifying maximum likelihood estimates
This paper proposes the use of a robust generalization of MLEs for the modeling of operational loss data.