ABSTRACT Operational risk is hard to quantify, due to the presence of heavy tailed loss distributions. Extreme value distributions, used in this context, are very sensitive to the data, and this is a ...

Model complexity and opacity has led to lapses in the recognition and acceptance of model limitations by senior management and decision-makers. This leads to suboptimal and even irrational decisions ...

Bayesian networks is an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of ...

The study uncovers significant pricing disparities in existing underwriting systems. Premiums for low-income policyholders ...

However, operational risk is a relatively new field, so understandably financial institutions have made less progress in developing formal models for it. Therefore, the supervisory agencies have ...

However, operational risk is harder to quantify and model than market and credit risks. Over the past few years, improvements in management information systems and computing technology have opened the ...

A provision to standardize and clarify banks' operational risk obligations — which opponents say is excessively costly and may not be effective — is emerging as the focal point of the public debate on ...

Army Doctrine Publication 4-0, Sustainment, states that sustainment preparation of the operational environment (SPoOE) is "the analysis to determine infrastructure, environmental factors, and ...

Model risk can stem from using a model with bad specifications, programming or technical errors, or data or calibration errors. Model risk can be reduced with model management such as testing ...