Technical paper/Operational risk modelling
Integrating internal and external loss data via an equivalence principle
The authors put forward a means address data scarcity in operational risk modelling by supplementing internal loss data with external loss data.
Modeling systemic operational risk in the Covid-19 pandemic
This paper introduces existing and novel epidemiology models and investigates how government responses to the Covid-19 pandemic impacted these models.
On modeling contagion in the formation of operational risk loss
This paper models an overall operational risk loss caused by the accumulation of intermediate losses incurred at each process via a mechanism of network contagion across distinct processes within the boundary of a bank.
The economic cost of a fat finger mistake: a comparative case study from Samsung Securities’s ghost stock blunder
This paper quantifies the economic cost of Samsung Securities’s ghost stock blunder using the synthetic control method.
Is there anybody out there? Detecting operational outages from Large Value Transfer System transaction data
This paper develops a method to identify operational outages of participants in the Canadian Large Value Transfer System (LVTS).
On a family of weighted Cramér–von Mises goodness-of-fit tests in operational risk modeling
This paper applies classical theory to determine if limiting distributions exist for WCvM test statistics under a simple null hypothesis.
Operational risk models and asymptotic normality of maximum likelihood estimation
In this paper, the author studies how asymptotic normality does, or does not, hold for common severity distributions in operational risk models.
The benefit of using random matrix theory to fit high-dimensional t-copulas
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.
Random matrix theory applied to correlations in operational risk
This paper focuses on the distribution of correlations among aggregate operational risk losses.
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.
A forward-looking adjustment for op risk quantification
Anupam Sahay and Ashish Dev present a general discussion of the basic elements of a rational framework for operational risk quantification. Then they focus on modelling the effect of internal controls and business environment on operational events. The…
Operational risk modelling: aggregating loss distributions using copulas
Capturing the dependence structure between business line/risk event types is an extremely important step for any serious attempt to model operational risk. In this article we show how this can be achieved by using a powerful statistical technique known…
Op risk modelling for extremes
Part 2: Statistical methods In this second of two articles, Rodney Coleman, of Imperial College London, continues his demonstration of the uncertainty in measuring operational risk from small samples of loss data.