Value-at-Risk (VaR) model fails to predict financial risk accurately especially during financial crises. This is mainly due to the model’s inability to calibrate new market information and the fact that the risk measure is characterised by poor tail risk quantification. An alternative approach which comprises of the Expected Shortfall measure and the Lognormal Jump-Diffusion (LJD) model has been developed to address the aforementioned shortcomings of VaR. This model is called the Simulated-Expected-Shortfall (SES) model. The Maximum Likelihood Estimation (MLE) approach is used in determining the parameters of the LJD model since it’s more reliable and authenticable when compared to other nonconventional parameters estimation approaches mentioned in other literature studies. These parameters are then plugged into the LJD model, which is simulated multiple times in generating the new loss dataset used in the developed model. This SES model is statistically conservative when compared to peers which means it’s more reliable in predicting financial risk especially during a financial crisis. / Statistics / M.Sc. (Statistics)

This research investigates opportunities for Service Design in Education. The focus is on a particular type of change happening within education that of Education for Sustainable Development (ESD) where Service Design has little presence and limited knowledge. This research has been carried out through grounded theory and contextualised in English institutions of primary education. As a result it identified Service Design as an approach to enable transformational change within educational institutions that seek to move towards ESD. To establish the basis for the research, a literature review has been carried out on Service Design, the vision of ESD and its application in the context of English schools. As a result, Service Design capability to re-design services at organisational level was linked to the gap in normative re-educative change processes towards ESD in English schools. The rest of the research sought to build on these findings. In-depth case studies with five primary schools and a cross-case analysis have been carried out to establish an understanding of ESD change at organisational level. It focused on elements relevant to normative re-educative change processes, which included social and personal norms and values residing within organisational systems. From the case studies, principles, concepts and processes were identified that enabled schools to engage with ESD at the deepest level. The knowledge derived from the case studies was further developed in order to relate the ESD phenomenon to Service Design. Service thinking and organisational change theory were applied to develop a Sustainable Education as a Service Model (SES MODEL) to understand ESD as a phenomenon in a service system. A SES Model was presented back to Service Design community. The sense-making of ESD was undertaken with seven service design practitioners by conducting semi-structured interviews during which they explored the SES Model. The outcome of the interviews showed the model to build service designer s capacity to engage with ESD, while the use of the model showed that designers could envision using it at a normative re-educative change level. The research shows that ESD is a new concept, which is relevant to Service Design. It therefore offers opportunities for further service design research and practical applications.

Value-at-Risk (VaR) model fails to predict financial risk accurately especially during financial crises. This is mainly due to the model’s inability to calibrate new market information and the fact that the risk measure is characterised by poor tail risk quantification. An alternative approach which comprises of the Expected Shortfall measure and the Lognormal Jump-Diffusion (LJD) model has been developed to address the aforementioned shortcomings of VaR. This model is called the Simulated-Expected-Shortfall (SES) model. The Maximum Likelihood Estimation (MLE) approach is used in determining the parameters of the LJD model since it’s more reliable and authenticable when compared to other nonconventional parameters estimation approaches mentioned in other literature studies. These parameters are then plugged into the LJD model, which is simulated multiple times in generating the new loss dataset used in the developed model. This SES model is statistically conservative when compared to peers which means it’s more reliable in predicting financial risk especially during a financial crisis. / Statistics / M.Sc. (Statistics)