A research report submitted to the Faculty of Science, School of Statistics and Actuarial Science in partial fulfilment of the requirements for the degree of Master of Science, University of the Witwatersrand. Johannesburg, 08 June 2017.
Mathematical
Statistics degree, 2017 / Country risk evaluation is a crucial exercise when determining the ability of
countries to repay their debts. The global environment is volatile and is filled
with macro-economic, financial and political factors that may affect a country’s
commercial environment, resulting in its inability to service its debt. This re
search report compares the ability of conventional neural network models and
traditional panel logistic regression models in assessing country risk. The mod
els are developed using a set of economic, financial and political risk factors
obtained from the World Bank for the years 1996 to 2013 for 214 economies.
These variables are used to assess the debt servicing capacity of the economies
as this has a direct impact on the return on investments for financial institu
tions, investors, policy makers as well as researchers. The models developed
may act as early warning systems to reduce exposure to country risk.
Keywords: Country risk, Debt rescheduling, Panel logit model, Neural net
work models / XL2017
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/23554 |
| Date | January 2017 |
| Creators | Ncube, Gugulethu |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (xii, 150 leaves), application/pdf |
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