This dissertation investigates the critical success factors behind the successful introduction of Mobile Data Services to the South African market. An evaluation of the history of telecommunications found that progress in the global telecommunications industry has been characterised by innovation in technological hardware, followed by innovation in policies. Such innovation mainly contributed to the development of voice services in the mobile telecommunications industry. Wireless technology is no longer simply an extension of fixed line voice communication, but a mature independent technology. This maturity is also reflected by the declining state of revenues generated by voice services. Market incumbents expect data services to provide increased revenues. The current wireless industry business value chain is evolving and becoming more complex due to new incumbents in the market and new relationships that are formed between new and existing incumbents. Although a new structured value chain is presented, reality indicates a diffusion of functions within the value chain. The study has identified a number of the Critical Success Factors, which will be required to facilitate the introduction of Mobile Data Services to the South African market. Central to determining these Critical Success Factors is the design of a structured framework or model, which allows for this. The basic elements of this model are constructed from core concepts in the market, which became elevated during the literature study and industry survey. The model demonstrates the need to integrate the various components into a single coherent strategy, which is imperative for determining the Critical Success Factors. Copyright 2004, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Benn, LJ 2004, A strategic business model for the introduction of Mobile Data Services in an emerging economy – focus on the South African market, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-06102005-093913 / > / Dissertation (MSc (Technology Management))--University of Pretoria, 2006. / Graduate School of Technology Management (GSTM) / unrestricted

The South African interest rate market has mainly been focused on vanilla interest rate products and hence can be seen as underdeveloped in this regard when compared, for instance, to the associated equity market. Market participants subscribe this aspect to a lack of demand and sophistication of investors within the market. This is, however, expected to change given the influx of international banks into the South African market over the past couple of years. The current market methodology, for the pricing of vanilla interest rate options in the South African market, is the standard Black model with some mechanism to incorporate interest rate smiles. This mechanism is typically in the form of the SABR model. The most signi cant drawback of this approach is the fact that it models each forward rate in isolation. Hence, there is no way to incorporate the joint dynamics between different forward rates and consequently cannot be used for the pricing of exotic interest rate options. In anticipation of these new market developments, we explore the possibility of calibrating the LIBOR market model to the South African market. This dissertation follows a bottom up approach and hence considers all aspects associated with such an implementation. The work mainly focuses on the calibration to at-the-money interest rate options. A possible extension to the SABR model, while remaining within the LMM framework, is considered in the final chapter. Copyright / Dissertation (MSc)--University of Pretoria, 2012. / Mathematics and Applied Mathematics / unrestricted

Magister Commercii - MCom / This research attempts to discover whether the Adaptive Market Hypothesis theory is applicable in the South African financial market and explores the innovation and cyclical profitability implications of the Adaptive Market Hypothesis theory. This is achieved in two parts: first by determining if returns follow a random walk or not and second by analysing the consistency of technical and fundamental factors to explain the cross-section of equity returns between 1 January 1998 to 31 December 2017. The tests of stock return dependency include a total of five tests on the average monthly returns for each stock in the ALSI covering normality and random walk theory for the duration of the two sub-periods and entire examination period.

Magister Commercii - MCom / This study analyses the extent to which stock returns provide forecasts of changes in interest rates and inflation for the South African market. The period under investigation, January 1966 - February 1989, is characterised by structural changes in the South African economy, especially in the financial markets. The earnings yield on shares is used as a measure of the return on stocks. Stock returns of 10 specific industries are used in addition to the overall market return. Monthly inflation series were constructed by employing both the Consumer Price Index (CPI) and the Producer Price Index (PPI). Before examining that relationship, tests were done to examine the relationship between nominal stock returns and expected inflation. The relation between the stock market and expected inflation is estimated by using three measures of expected inflation. The results appear to suggest that the stock market reacted positively to expected inflation during the 1966 - 1982 period. Two proxies of expected inflation. Best results inflation are used to were obtained with measure future the Fama-Gibbons measure. In addition, the results suggest that stock returns provide additional information of future inflation to that contained in the Fama-Gibbons and interest rate models. Returns for specific industries, used in this study, appear to provide marginally better forecasts of inflation than the overall market return. The results also suggest that stock returns provide forecasts of changes in interest rates and inflation. There is no evidence that the specific industries used, provide consistent better forecasts of interest rate changes than the overall market.

Thesis (MComm)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Accurate estimation of Value at Risk (VaR) and Expected Shortfall (ES) is critical in the management of extreme market risks. These risks occur with small probability, but the financial impacts could be large. Traditional models to estimate VaR and ES are investigated. Following usual practice, 99% 10 day VaR and ES measures are calculated. A comprehensive theoretical background is first provided and then the models are applied to the Africa Financials Index from 29/01/1996 to 30/04/2013. The models considered include independent, identically distributed (i.i.d.) models and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) stochastic volatility models. Extreme Value Theory (EVT) models that focus especially on extreme market returns are also investigated. For this, the Peaks Over Threshold (POT) approach to EVT is followed. For the calculation of VaR, various scaling methods from one day to ten days are considered and their performance evaluated. The GARCH models fail to converge during periods of extreme returns. During these periods, EVT forecast results may be used. As a novel approach, this study considers the augmentation of the GARCH models with EVT forecasts. The two-step procedure of pre-filtering with a GARCH model and then applying EVT, as suggested by McNeil (1999), is also investigated. This study identifies some of the practical issues in model fitting. It is shown that no single forecasting model is universally optimal and the choice will depend on the nature of the data. For this data series, the best approach was to augment the GARCH stochastic volatility models with EVT forecasts during periods where the first do not converge. Model performance is judged by the actual number of VaR and ES violations compared to the expected number. The expected number is taken as the number of return observations over the entire sample period, multiplied by 0.01 for 99% VaR and ES calculations. / AFRIKAANSE OPSOMMING: Akkurate beraming van Waarde op Risiko (Value at Risk) en Verwagte Tekort (Expected Shortfall) is krities vir die bestuur van ekstreme mark risiko’s. Hierdie risiko’s kom met klein waarskynlikheid voor, maar die finansiële impakte is potensieel groot. Tradisionele modelle om Waarde op Risiko en Verwagte Tekort te beraam, word ondersoek. In ooreenstemming met die algemene praktyk, word 99% 10 dag maatstawwe bereken. ‘n Omvattende teoretiese agtergrond word eers gegee en daarna word die modelle toegepas op die Africa Financials Index vanaf 29/01/1996 tot 30/04/2013. Die modelle wat oorweeg word sluit onafhanklike, identies verdeelde modelle en Veralgemeende Auto-regressiewe Voorwaardelike Heteroskedastiese (GARCH) stogastiese volatiliteitsmodelle in. Ekstreemwaarde Teorie modelle, wat spesifiek op ekstreme mark opbrengste fokus, word ook ondersoek. In hierdie verband word die Peaks Over Threshold (POT) benadering tot Ekstreemwaarde Teorie gevolg. Vir die berekening van Waarde op Risiko word verskillende skaleringsmetodes van een dag na tien dae oorweeg en die prestasie van elk word ge-evalueer. Die GARCH modelle konvergeer nie gedurende tydperke van ekstreme opbrengste nie. Gedurende hierdie tydperke, kan Ekstreemwaarde Teorie modelle gebruik word. As ‘n nuwe benadering oorweeg hierdie studie die aanvulling van die GARCH modelle met Ekstreemwaarde Teorie vooruitskattings. Die sogenaamde twee-stap prosedure wat voor-af filtrering met ‘n GARCH model behels, gevolg deur die toepassing van Ekstreemwaarde Teorie (soos voorgestel deur McNeil, 1999), word ook ondersoek. Hierdie studie identifiseer sommige van die praktiese probleme in model passing. Daar word gewys dat geen enkele vooruistkattingsmodel universeel optimaal is nie en die keuse van die model hang af van die aard van die data. Die beste benadering vir die data reeks wat in hierdie studie gebruik word, was om die GARCH stogastiese volatiliteitsmodelle met Ekstreemwaarde Teorie vooruitskattings aan te vul waar die voorafgenoemde nie konvergeer nie. Die prestasie van die modelle word beoordeel deur die werklike aantal Waarde op Risiko en Verwagte Tekort oortredings met die verwagte aantal te vergelyk. Die verwagte aantal word geneem as die aantal obrengste waargeneem oor die hele steekproefperiode, vermenigvuldig met 0.01 vir die 99% Waarde op Risiko en Verwagte Tekort berekeninge.