Exploration of the concept of credit risk as part of risk profiles in the risk based capital framework in Uganda

Abstract

The dissertation project aims to explore the concept of credit risk as part of the risk profiles in the risk based capital framework in Uganda. The studybwill employ credit risk as a crucial aspect of the insurance industry and its effective management is essential for ensuring the financial stability and profitability of insurance companies. The document elaborates on the steps taken for computing capital charges for credit risk. It mentions the specific procedures for calculating capital charges for loans and reinsurance components. Assumptions explained, including interest rate categories and currency conversions. It outlines the data sources used,including times series data from the central bank and annual default rates from Moody’s. Capital charges for loans: the document describes the risk metrics employed,such as Value at Risk and Conditional tail expectation. The use of Microsoft excel for computations is also mentioned. The document summarizes the findings,indicating that 90% confidence level was adopted for the risk charges. It highlights the specific capital charge value of 13.13,signifying the company's risk mitigation strategy. It mentions that these charges are to be applied to the outstanding loan amount. This charge meant that the company is setting aside funds to absorb losses that are expected to occur only 10% of the time or less. Capital charge for reinsurance component: This section discusses the probability of default calculations and assumptions made for this component. It mentions that data source for annual default rates and the use of loss given default approach for risk metrics. The capital charges for this component were calculated by computing the average default rate of the ratings and they were applied to the exposure default. In summary,VaR focuses on the potential losses within normal market conditions,LGD assesses the impact of default on policies and the stress testing model method used in other research provides a comprehensive view of a portfolio's performance under extreme scenarios.