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LD2668 .T4 1939 S31 / Master of Science

Call number: LD2668 .T4 1966 F852 / Master of Science

This study intends to assess determinants of nonperforming loans. The mixed research approach was adopted for the study. Survey was conducted with professionals engaged in both private and state owned Banks in Ethiopia holding different positions using a self administered questionnaire. In addition, the study used structured review of documents and records of banks and in-depth interview of senior bank officials in the Ethiopian banking industry. The findings of the study shows that poor credit assessment, failed loan monitoring, underdeveloped credit culture, lenient credit terms and conditions, aggressive lending, compromised integrity, weak institutional capacity, unfair competition among banks, willful default by borrowers and their knowledge limitation, fund diversion for unintended purpose, over/under financing by banks ascribe to the causes of loan default.

This thesis investigates correlations and linkages in credit risk that widely exist in all sectors of the financial markets. The main body of this thesis is constructed around four empirical chapters. I started with extending two main issues focused by earlier empirical studies on credit derivatives markets: the determinants of CDS spreads and the relationship between CDS spreads and bond yield spreads, with a special focus on the effect of the subprime crisis. By having observed that the linear relationship can not fully explain the variation in CDS spreads, the third empirical chapter investigated the dependence structure between CDS spread changes and market variables using a nonlinear copula method. The last chapter investigated the relationship between the CDS spread and another credit spread - the TED spread, in that a MVGARCH model and twelve copulas are set forth including three time varying copulas. The results of this thesis greatly enhanced our understanding about the effect of the subprime crisis on the credit default swap market, upon which a set of useful practical suggestions are made to policy makers and market participants.

Submitted in fulfillment of the requirements of the degree Doctor of Technology: Public Management, Durban University of Technology, Durban, South Africa, 2015. / In 2006, the government-supported microfinance programmes implemented by the Kenyan government started lending credit to Micro and Small Enterprises (MSEs) using a group-lending mode, a change which represented a paradigm shift from individual lending mode. The overall aim of this research is to provide an investigation of whether the transformation of this lending policy was backed by any theoretical and empirical support. Specifically, the entirety of this study is intended to give an insight of what might have influenced the change, what informed it and what might have been overlooked. To achieve clarity and the study aim, the research is compartmentalised into three discrete studies. In the first study, a historical investigation into the factors which hindered MSEs from acquiring credit was undertaken. The second study investigated the reasons MSEs were credit rationed. The third study investigated whether the problems experienced by MSEs, associated with lack of credit access (lack of credit demand and rationing), could have been mitigated by group lending. The research utilised quantitative research design, the first two studies utilised data derived from National MSEs Baseline survey conducted in 1999. The third study utilised primary data collected from micro credit groups of the Kenya Rural Enterprise Programme (K-REP) in 2006 in Nairobi, Kenya. Various economic models and regression analysis were utilised in analysing different outcomes. In particular, the research utilised Univariate Probit, Bivariate Probit and Heckman Two-Stage Models to model various credit access outcomes. The study found that group lending largely mitigated information asymmetry- the main cause of MSEs failure to access credit. However, the study concludes that asymmetric information was not the only source of credit failure in Kenya. For group lending to work, or to have worked, it required support by other pro-MSE programme dynamics. This suggested that the government decision to change policy was partially informed by theory and practice. / D

Mathematical programming (MP) can be used for developing classification models for the two–group classification problem. An MP model can be used to generate a discriminant function that separates the observations in a training sample of known group membership into the specified groups optimally in terms of a group separation criterion. The simplest models for MP discriminant analysis are linear programming models in which the group separation measure is generally based on the deviations of misclassified observations from the discriminant function. MP discriminant analysis models have been tested extensively over the last 30 years in developing classifiers for the two–group classification problem. However, in the comparative studies that have included MP models for classifier development, the MP discriminant analysis models either lack appropriate normalisation constraints or they do not use the proper data transformation. In addition, these studies have generally been based on relatively small datasets. This thesis investigates the development of MP discriminant analysis models that incorporate appropriate normalisation constraints and data transformations. These MP models are tested on binary classification problems, with an emphasis on credit scoring problems, particularly application scoring, i.e. a two–group classification problem concerned with distinguishing between good and bad applicants for credit based on information from application forms and other relevant data. The performance of these MP models is compared with the performance of statistical techniques and machine learning methods and it is shown that MP discriminant analysis models can be useful tools for developing classifiers. Another topic covered in this thesis is feature selection. In order to make classification models easier to understand, it is desirable to develop parsimonious classification models with a limited number of features. Features should ideally be selected based on their impact on classification accuracy. Although MP discriminant analysis models can be extended for feature selection based on classification accuracy, there are computational difficulties in applying these models to large datasets. A new MP heuristic for selecting features is suggested based on a feature selection MP discriminant analysis model in which maximisation of classification accuracy is the objective. The results of the heuristic are promising in comparison with other feature selection methods. Classifiers should ideally be developed from datasets with approximately the same number of observations in each class, but in practice classifiers must often be developed from imbalanced datasets. New MP formulations are proposed to overcome the difficulties associated with generating discriminant functions from imbalanced datasets. These formulations are tested using datasets from financial institutions and the performance of the MP-generated classifiers is compared with classifiers generated by other methods. Finally, the ordinal classification problem is considered. MP methods for the ordinal classification problem are outlined and a new MP formulation is tested on a small dataset.

Three decades ago, the first securitization product was introduced to the investing public in the United States by federal agencies and similar products showed up in Taiwan in 2002. It is a brand new concept for investors in Taiwan and material differences between the west and the east should be considered thoroughly. This research focus on must-knows and critical techniques of securitization of credit card receivables in Taiwan. The researcher discovered that, the competitiveness of originators is one crucial factor for credit card securitization to survive during serious conditions. Without the ability to refill declining credit card receivables in asset pool of securitization transactions, early amortization will be triggered and that’s where case closed. Cash flow forecasting model is another important player in credit card securitization. By predicting future cash flows needed and generated, originators can reap the most profit. However, the variables in this magic model are not easy to find. Through multiple analyses and one simulated case, the researcher found that the most important factors are yield rate, loss rate, repurchase rate, and monthly payment rate. / Three decades ago, the first securitization product was introduced to the investing public in the United States by federal agencies and similar products showed up in Taiwan in 2002. It is a brand new concept for investors in Taiwan and material differences between the west and the east should be considered thoroughly. This research focus on must-knows and critical techniques of securitization of credit card receivables in Taiwan. The researcher discovered that, the competitiveness of originators is one crucial factor for credit card securitization to survive during serious conditions. Without the ability to refill declining credit card receivables in asset pool of securitization transactions, early amortization will be triggered and that’s where case closed. Cash flow forecasting model is another important player in credit card securitization. By predicting future cash flows needed and generated, originators can reap the most profit. However, the variables in this magic model are not easy to find. Through multiple analyses and one simulated case, the researcher found that the most important factors are yield rate, loss rate, repurchase rate, and monthly payment rate.

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