Defaulted retail mortgage loans as a percentage of retail mortgage loans and advances averaged 9 percent over 2010 as reported in the SARB Bank Supervision Annual report. Banks are in the business of risk taking and as a result need to constantly evaluate and review credit risk management to attain sustained profitability. In credit risk modelling, default risk is associated with client-specific factors particularly the client’s credit rating. However, Brent, Kelly, Lindsey-Taliefero, and Price (2011), have shown that variation in mortgage delinquencies reflect changes in general macroeconomic conditions. This study aims to provide evidence of whether macroeconomic factors such as the house price index, CPI, credit growth, debt to income ratio, prime interest rates, and unemployment, are key drivers of residential mortgage delinquencies and default in South Africa. In this study, data from an undisclosed bank is used to estimate three models that are supposed to capture the influence of several macroeconomic variables on 30 day, 60 day, and 90 day delinquency rates over the 2006-2010 period. In order to eliminate the potential bias introduced by those observations, a fourth model was estimated using aggregated banking industry published by the SARB. However, due to data constraints, only the severe mortgage delinquency state, that is the 90 day delinquency rate was modelled using this aggregate data. The SARB sample covers the period between 2008 and 2010. The choice of the date 2008 coincides with the introduction of the Basel 2 regulatory framework. Prior to 2008, the big four South African banks were governed by the Basel 1 framework, and measured their credit risk using the so-called Standardised Approach which has different loan categories and different default definitions compared to the Basel 2 Advanced Internal Ratings Approach adopted in 2008. The findings suggest that the two samples (i.e. the data from the individual bank and the SARB data) imply different explanatory macroeconomic factors. Prime interest rates were found to be the only important variable in determining 30 day and 60 day delinquency rates for the individual bank. The house price index, CPI, credit growth, and prime interest rates were found to be the main determinants of the 90 day delinquency rates for the undisclosed bank, while the house price index, CPI, and credit growth, determine the 90 day delinquency rates for the big four banks.

The expected yield to maturity on a defaultable obligation equals the nominal yield less expected default losses. However, in a mark-to-market world, one doesn't have the luxury of reporting one's performance on the basis of yield to maturity. Total return is calculated for an arbitrary holding period, and must reflect any mark-to-market gains or losses as at the close of the period-gains or losses that can be triggered by the bond's upgrade or downgrade. Thus to estimate expected total return, one must estimate not only expected default losses, but also the impact on capital price of expected ratings transitions. This paper begins with the observation that a bond which is blessed by more favourable transition characteristics is likely to produce a higher total return, and poses the question of how that benefit can be quantified. How much is it worth? To answer the question, I start by specifying a formal bond-pricing model reflective of ratings transitions. I survey various statistical methods and past research efforts to identify the ratings-transition matrix which best parametrises the model, and propose a novel test for selecting between competing matrices. Using this approach, I replicate several important studies of ratings transitions. I also use it to examine new published and unpublished data, testing for (and finding) ratings path-dependency, and otherwise exploring the effect of ratings changes on different bond sectors. I then turn to the question of whether it is possible to estimate bond-specific transition probabilities, and propose a way to do so. I combine these efforts into the specifications for a pricing model capable of answering the question: How much is it worth?

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On a basis of the development of credit risk models, this study aims to help managers of financial institutions understand the development of the models so as to develop their own model that will provide objective and reasonable references for banks to decide the lending rate. Furthermore, this study used "Utilization of Long-Term Funding" as the object and studied individual cases of approved loans. By using risk neutral evaluation method to study the difference between the lending rate of loans and the risk-free interest rate of public bonds, to extract implied probabilities of default and required credit risk premiums form actual market data on interest rates. These credit risk premiums of model were used to be compared with the actual markups of banks and the results are as follows: 1.Most values stated in credit risk premium are lower than the actual markups for banks usually consider the burden of other capital costs and the factor of liquidity premium when they set the rating for markup. 2.After a loan is approved, the assumed recovery rate upon application will adjust according to the market value of the collateral. When the recovery rate decreases, the expected loss rate on the loan will gradually increase. Moreover, the higher the assumed recovery rate, the larger the corrected expected loss rate after the loan is approved. 3.In recent years, the non-performing rate for banks in Taiwan has reached a record high. Even though banks face less credit risks when they make long-term loans in "Utilization of Long-Term Funding", the probability of default has increased in recent years, which has contributed to the increase of expected loss rate on the long-term loan. In sum, banks still face credits risks that should not be ignored when they manage long-term loans. Thus, it is necessary to improve loan review to enhance the quality of loans and to increase the efficiency of utilization of long-term fund.

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