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Description
This module aims to develop a practical understanding of operational risk modelling in both the banking and insurance sectors under the Basel II/III and Solvency II regulatory frameworks. It is primarily intended for third and fourth year undergraduates and taught postgraduates registered on the degree programmes offered by the Department of Statistical Science (including the MASS programmes).听For these students, the academic prerequisites for this module are met either through earlier compulsory study within (UG) or successful admission to (PGT) their current programme.
Intended Learning Outcomes
- be able to understand the fundamentals of risk modelling with a specific focus on practical applications in operational risk and insurance contexts;
- be able to gain insight into regulatory frameworks, including Basel II/III and Solvency II, while applying basic indicator, standardized, and advanced measurement approaches;
- be able to apply advanced techniques, including loss distribution approach (LDA) models for tail risks, truncation and splicing methods for accurate risk assessment, and parameter estimation techniques using real-world insurance examples;
- have a deeper theoretical understanding of the LDA modelling techniques (Level 7 only).
Applications - An integral part of modern financial risk involves operational risk, the third key risk type that financial institutions must model and hold capital for according to the international banking regulations of Basel II/III. The key set of concepts and mathematical modelling tools developed in this module will equip students with the appropriate mathematical and statistical background to undertake core modelling activities required in risk management, capital management and quantitative modelling in modern financial institutions.
Indicative Content - Basic indicator, standardized, and advanced measurement approaches. Loss Distributional Approach (LDA): quantiles, moments, frequency and severity distributions, heavy-tailed models, convolutions, characteristic functions, truncated and censored data modelling. Measures of risk: coherent and convex risk measures, comonotonic additive measures, value-at-risk, expected shortfall, spectral risk measures. Modelling dependence: dependence in the LDA framework, frequency and severity dependence. Loss aggregation: calculation of annual loss distribution. The module will use programming or readily available packages where possible within the operational risk framework to make theoretical concepts more practical and applicable.
Key texts - Available from .
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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