The application of statistical classification to predict sovereign default
- Authors: Vele, Rendani
- Date: 2023-10-13
- Subjects: Statistical classification , Neural networks (Computer science) , Regression analysis , Logits , Probits , Multiple imputation (Statistics) , Markov chain Monte Carlo , Debts, Public
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424563 , vital:72164
- Description: When considering sovereign loans, it is imperative for a financial institution to have a good understanding of the sovereign they are transacting with. Defaults can occur if proper evaluation steps are not considered. To aid in the prediction of potential sovereign defaults, financial institutions, together with grading companies, quantify the risk associated with issuing a loan to a sovereign by developing sovereign default early warning systems (EWS). Various classification models are considered in this study to develop sovereign default EWS. These models are the binary logit, probit, Bayesian additive regression trees, and artificial neural networks. This study investigates the predictive performance of the various classification techniques. Sovereign information is not readily available, so missing data techniques are considered in order to counter the data availability issue. Sovereign defaults are rare, which results in an imbalance in the distribution of the binary dependent variable. To assess data sets with such characteristics, metrics for imbalanced data are considered for model performance comparison. From the findings, the Bayesian additive regression technique generated better results than the other techniques when considering a basic data analysis. Moreover when cross-validation was considered, the neural network technique performed best. In addition, regional models had better results than the global model when considering model predictive capability. The significance of this study is to develop sovereign default prediction models using various classification techniques focused on enhancing previous literature and analysis through the application of Bayesian additive regression trees. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Date Issued: 2023-10-13
- Authors: Vele, Rendani
- Date: 2023-10-13
- Subjects: Statistical classification , Neural networks (Computer science) , Regression analysis , Logits , Probits , Multiple imputation (Statistics) , Markov chain Monte Carlo , Debts, Public
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424563 , vital:72164
- Description: When considering sovereign loans, it is imperative for a financial institution to have a good understanding of the sovereign they are transacting with. Defaults can occur if proper evaluation steps are not considered. To aid in the prediction of potential sovereign defaults, financial institutions, together with grading companies, quantify the risk associated with issuing a loan to a sovereign by developing sovereign default early warning systems (EWS). Various classification models are considered in this study to develop sovereign default EWS. These models are the binary logit, probit, Bayesian additive regression trees, and artificial neural networks. This study investigates the predictive performance of the various classification techniques. Sovereign information is not readily available, so missing data techniques are considered in order to counter the data availability issue. Sovereign defaults are rare, which results in an imbalance in the distribution of the binary dependent variable. To assess data sets with such characteristics, metrics for imbalanced data are considered for model performance comparison. From the findings, the Bayesian additive regression technique generated better results than the other techniques when considering a basic data analysis. Moreover when cross-validation was considered, the neural network technique performed best. In addition, regional models had better results than the global model when considering model predictive capability. The significance of this study is to develop sovereign default prediction models using various classification techniques focused on enhancing previous literature and analysis through the application of Bayesian additive regression trees. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Date Issued: 2023-10-13
Evaluation of debt management policy implementation towards revenue management in government leased properties
- Mzekwa-Khiva, Nomonde Lindelani
- Authors: Mzekwa-Khiva, Nomonde Lindelani
- Date: 2013
- Subjects: Monetary policy , Debts, Public -- South Africa -- Eastern Cape , Financial crises -- South Africa -- Eastern Cape , Debts, Public
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: vital:8880 , http://hdl.handle.net/10948/d1020633
- Description: The study sought to evaluate debt management policy implementation towards revenue management in government leased properties of the Eastern Cape Provincial Treasury at the Transkei Development and Reserve Fund. Secondly, the study aimed at developing a tool for assisting policy-makers and officials involved in debt management and revenue collection. In order to address the research problem, a case study involving randomly selected 27 employees from the Eastern Cape Provincial Treasury and housing ward committee members was adopted. Self-administered questionnaires and interviews were the two data collection techniques utilised. All participants were involved in the study during tea and lunch breaks at the workplace; this constituted the employees’ natural environment. Both quantitative and qualitative designs were utilised in analysing data. Descriptive statistical analysis using excel was utilised to summarise the responses, analyse the demographic profiles of participants and their responses. The results were thus presented in the form of bar charts. Responses which could not be analysed using statistics were analysed qualitatively thus the advantages inherent in the two approaches were exploited. The evidence from the study suggests that government operational employees are aware of their roles and responsibilities as they relate to debt management and debt collection policy. The development of debt management policy promotes rental collection, improve property profitability and ensure the maintenance is in place to improve attractiveness of the government properties.
- Full Text:
- Date Issued: 2013
- Authors: Mzekwa-Khiva, Nomonde Lindelani
- Date: 2013
- Subjects: Monetary policy , Debts, Public -- South Africa -- Eastern Cape , Financial crises -- South Africa -- Eastern Cape , Debts, Public
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: vital:8880 , http://hdl.handle.net/10948/d1020633
- Description: The study sought to evaluate debt management policy implementation towards revenue management in government leased properties of the Eastern Cape Provincial Treasury at the Transkei Development and Reserve Fund. Secondly, the study aimed at developing a tool for assisting policy-makers and officials involved in debt management and revenue collection. In order to address the research problem, a case study involving randomly selected 27 employees from the Eastern Cape Provincial Treasury and housing ward committee members was adopted. Self-administered questionnaires and interviews were the two data collection techniques utilised. All participants were involved in the study during tea and lunch breaks at the workplace; this constituted the employees’ natural environment. Both quantitative and qualitative designs were utilised in analysing data. Descriptive statistical analysis using excel was utilised to summarise the responses, analyse the demographic profiles of participants and their responses. The results were thus presented in the form of bar charts. Responses which could not be analysed using statistics were analysed qualitatively thus the advantages inherent in the two approaches were exploited. The evidence from the study suggests that government operational employees are aware of their roles and responsibilities as they relate to debt management and debt collection policy. The development of debt management policy promotes rental collection, improve property profitability and ensure the maintenance is in place to improve attractiveness of the government properties.
- Full Text:
- Date Issued: 2013
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