MSc.SS Theses and Dissertations (2017)
Permanent URI for this collection
Browse
Recent Submissions
Now showing 1 - 4 of 4
- ItemDetermine the breaking point of Kenya debt an application of extreme value theory(Strathmore University, 2017) Mathenge, Jacqueline WachukaThe aim of the study is to determine the breaking point of Kenyan public debt through the use of Extreme Value Theorem (EVT). EVT focuses on the tail end of distributions to be able to identify maxima and minima points. With the rising debt levels since devolution, from Kenya Shillings (KES) 500 billion in 2013 to KES 2.5 trillion in 2015, and warnings from international bodies such as International Monetary Fund (IMF) and World Bank on rising debt levels, there is need to determine sustainability of debts beyond analyst speculations. The use of the special case of EVT known as Generalized Extreme Value (GEV) application looks at a degenerate distribution factor thus ensuring the tail end of the distribution, that is, the maxima converges to the GEV despite the distribution of the data set (no assumption on the distribution of the data set). From the study the Gumbel model was determined to be the most appropriate model and with a 95% threshold, the GEV projected total debt maxima to be KES 5 trillion. This is evidence that the current debt levels of KES 2.5 trillion is still sustainable but should however be monitored.
- ItemDeveloping pediatric prognostic model using finite mixture models(Strathmore University, 2017) Ogero, Morris OndiekiBackground: World Health Organization (WHO) guidelines recommend early identification of patients who have emergency features for early medical intervention with the aim of reducing child mortality and morbidity. Prognostic models have been developed to be used in clinical setups, but their performance in external validations has been dismal. These poor performances have been attributed to suboptimal statistical methods used for derivation of these scores. Methods: The Bayesian finite mixture model was used to succinctly identify subpopulations in a population of 47,596 patients from different geographical regions. Mixed models were used to derive a final prognostic model taking into account subgroups of the population. Clinically relevant yet routinely available prognostic factors were used in model development. Results: Amongst the 23 risk factors used, the AVPU scale which measures unconsciousness was the strongest predictor of mortality with odds of (AOR=2.94, 95% CI= 2.57 - 3.36). Oedema (AOR= 2.66, 95% CI= 2.18 - 3.24), pallor (AOR=2.09, 95% CI= 1.86 - 2.36) and the presence of >= 3 severe comorbidities (AOR=2.19, 95% CI= 1.73 - 2.74) were also associated with an increased risk of death. Conclusion: Given that patient are not alike, a statistical methodology that clusters patients into homogeneous subpopulations should be used to account for the inherent variability in the medical patients. Computational methodology such as mixture models should be used to identify inherent subpopulations that underlie the population of medical patients under study. Limitation: The use of diagnostic episodes as one of predictors in the model was based on the clinician’s impression (not a laboratory test) thus the possibility of false positives could not be ruled out.
- ItemModelling the structure of dependence of stock markets in BRICS & KENYA: Copula GARCH approach(Strathmore University, 2017) Otieno, Kevin OmondiBackground: Dependence structure is used widely to describe relationships between risks and provides estimation of risks for risk management purposes. Modeling dependence structure of stock returns is a difficult task when returns are having non elliptical distributions. Objective: To examine the dependence pattern between the Kenya stock market return and BRICS stock market returns. Methods: In this dissertation, we estimated the dependence using copula GARCH, an approach that combines copula functions and GARCH models. We applied this method to a stock market returns consisting of stock indices of Brazil, Russia, India, China and South Africa (BRICS) and Kenya stock market. We first used GARCH(1,1) to model the marginal distributions of each stock returns using different GARCH(1,1) specifications. Copula was then used to analyze the dependence between the BRICS stock market returns and Kenya stock market returns using the standardized marginal distributions derived from GARCH(1,1) residuals. The best fitting copula parameter was determined using the log likelihood or AIC.Results: Empirical results showed that GJR-GARCH model provided the best fit for Brazil, Russia, China and Kenya while E-GARCH model provided the best fit for India and South Africa. As for modeling the dependence structure, student t copula parameter provided the best fit for the marginal distributions of the returns. Conclusion: Marginal models showed presence of volatility clustering which vanishes after crisis. To capture the dependence structure for bi variate data sets, Student t copula was considered to be the appropriate copula function. Recommendation: Further research should be extended to examine the multivariate structure, a joint distribution of BRICS in terms of Multivariate GARCH. Also research should focus on specific time periods in order to ensure effectiveness in measurement and management of risks.
- ItemPredictive modelling in credit risk: a survival analysis case(Strathmore University, 2017) Omoga, Allan AnyonaSix survival analysis techniques are accessed by applying the techniques to a dataset consisting of 33,238 active credit facilities from a financial institution operating in Kenya. Namely, the Accelerated Failure Time (AFT) Models, Cox proportional hazard (PH) Model and the Mixture Cure Model (MCM) are considered in the comparisons. Evaluation of the techniques is conducted from a Statistical approach evaluation using the Area under the Curve (AUC) and financial evaluation using the annuity theory. The Cox Proportional Hazard (PH) and the Mixture cure model performs significantly well.