PS 2015
| Title | Description | |
|---|---|---|
| PS15001_Combining-internal-loss-data-and-scenario-data-in-operational-risk-quantification | Project goal: To determine which technique performs best when combining internal loss data and scenario loss datafor purposes of estimating operational risk capital in a LDA model. Problem statement Research proposal Article | |
| PS15002_Macro-economic-stress-testing-abstract | Project goal: To determine if quantitative techniques can be used to perform Macro-Economic Stress testing inrespect of Operational Risk, for example whether there exists a causal link between macroeconomicfactors and operational losses that can be used to construct a model. Problem statement Research proposal | |
| PS15003_Meeting-the-Basel-III-minimum-capital-requirements | Project goal: To develop a model to assist banks to optimally meet the Basel III minimum capital and liquidityrequirements and highlighting possible constraints or areas of concern the banks may face. Problem statement | |
| PS15004_Credit-curve-model | Project goal: To enable CIB Portfolio Management to accurately value, price and enhance strategic decision makingon loans by constructing accurate credit curves. Problem statement | |
| PS15005_Alternatives-to-splice-distributions-in-operational-risk-quantification | Project goal: To determine which statistical distributions, if any, can be used as an alternative to splice distributionsfor modelling the severities in operational risk quantification. Problem statement Research proposal | |
| PS15006_Pricing-and-risk-management-aspects-of-participating-life-insurance-contracts | Project goal: To gain insight into the pricing and hedging of minimum rate of returnguarantees (MRRG) embedded in participating (or profit-sharing) life insurance policies. Problem statement Research proposal 1Article 1 Research proposal 2 | |
| PS15007_A-simulation-comparison-of-quantile-approximation-techniques-for-compound-distributions-popular-in-Operational-Risk | Project goal: To compare different techniques for approximating the quantiles of compound distributions in terms oftheir practical usefulness. Problem statement Research proposal Article | |
| PS15009_The-use-of-PECDC-data-in-LGD-modelling-in-South-Africa | Project goal: To use the pooled credit data like the Global Credit Data (GCD) (formerly known as the Pan-EuropeanCredit Data Consortium (PECDC)) during the development of new models in corporate loss givendefault (LGD) modelling. Problem statement | |
| PS15010_Research-in-predictive-modelling-Binning-variable-selection-and-income-modelling | Project goal: To propose new methodologies (and compare existing methodologies) in thepredictive modelling context. Problem statement Research proposal | |
| PS15011_Semi-supervised-segmentation-within-a-predictive-modelling-context-inretail-credit | Project goal: To propose a semi-supervised segmentation method and to do empiricalstudies on the performance of this method. Problem statement Research proposal Article | |
| PS15012_Low-default-portfolios-estimation-of-the-probability-of-default | Problem statement | |
| PS15013_Multi-period-credit-portfolio-optimisation | Project goal: To develop a multi-period optimisation model for a credit portfolio in Business Banking. Problem statement Research proposal 1 Research proposal 2 | |
| PS15014_Efficient-practical-methods-for-pricing-and-risk-managing-derivatives-in-dynamic-markets | Project goal: To answer the question, "what is an efficient, practical method for pricing and risk-managing derivatives – and portfolioscontaining derivatives – in dynamic markets?" Problem statement Research proposal 1 Article 1 Research proposal 2 Article 2 |