3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Commodity prices and exchange rates in Southern African countries(2019) Theka, EdwardThis study empirically assesses the relationship between exchange rates movements with non-fuel commodity prices indices of five Southern African countries, namely; Botswana, Malawi, Mozambique, South Africa, and Zambia. Monthly International Monetary Fund (IMF) non-fuel commodity price indices (NFCI) and monthly exchange rates (expressed USD per unit of the local currencies) that span January 1996 to September 2018 were used for this study. The econometric techniques employed were EG two-stage and Johansen Trace cointegration tests, VAR(1) models, and Granger causality tests. The cointegration tests reveal no long-run relationship between commodity price index and exchange rates for all of the selected countries, which implies that in the long term there is equilibrium link between these two variables of interest for neither Botswana, Malawi, Mozambique, South Africa, nor Zambia. The study fails to reject that there is no Granger causality between the commodity price index and exchange rates in both directions for Botswana, Malawi, and Mozambique. At the 10% significance level commodity price index Granger cause exchange rates and exchange rates Granger cause commodity price index for South Africa; Zambia has only a uni-directional Granger causality from exchange rates to commodity price index. Thus, the interdependence between these two variables is not complete but partial. It is recommended that, for further studies, an endeavor be made to find the possible exogenous variables that serve as the determinants of commodity prices and exchange rates.Item Modelling and forecasting metal prices: evidence from developing African economies(2017) Handura, Vetjevera MercyDeveloping Africa has been heavily dependent on primary commodities for decades as these countries are rich in mineral resources and often tend to export that and little else. However, commodities are highly susceptible to volatility and their effects on these economies are enormous. This paper investigates the extent to which the GARCH and EGARCH models can accurately be employed to model and forecast metal prices. Also, a p-dimensional VECM is formulated in establishing the extent to which the metals are co-integrated. Seven metals - Aluminium, Copper, Gold, Lead, Nickel, Platinum and Zinc have been employed for the purpose of this study. The models yielded satisfactory prediction results, albeit mixed findings in terms of the superiority of the models. Nonetheless, we conclude that the results are sufficient in aiding African economies in deriving appropriate policies and trading strategies so as to capitalise on export revenues, resulting in increased GDP and overall economic growth and development of their countries. Key Words: volatility, GARCH, EGARCH, VECM forecast, metal pricesItem An in-depth validation of momentum as a dominant explanatory factor on the Johannesburg Stock Exchange(2017) Page, Moshe DanielThis study considers momentum in share prices, per Jegadeesh and Titman (1993, 2001), on the cross-section of shares listed on the JSE. The key research objective is to define whether momentum is significant, independent and priced. ‘Significant’ implies that momentum produces significantly positive nominal and risk-adjusted profits, ‘independent’ means that momentum is independent of other non-momentum stylistic factor premiums and finally, ‘priced’ suggests that momentum is a priced factor on the JSE and thereby contributes to the cross-sectional variation in share returns. In order to determine the significance of the momentum premium on the JSE, univariate momentum sorts are conducted that consider variation in portfolio estimation and holding periods, weighting methodologies as well as liquidity constraints, price impact and microstructure effects. The results of the univariate sorts clearly indicate that momentum on the JSE is both significant and profitable assuming estimation and holding periods between three and twelve months. Furthermore, consistent with international and local literature, momentum profits reverse assuming holding periods in excess of 24 months. In order to determine whether momentum is independent, bivariate sorts and time-series attribution regressions are conducted using momentum and six non-momentum factors, namely: Size, Value, Liquidity, Market Beta, Idiosyncratic Risk and Currency Risk. The results of the bivariate sorts and time-series attribution regressions clearly indicate that momentum on the JSE is largely independent of the nonmomentum stylistic factors considered. Lastly, cross-sectional panel regressions are conducted where momentum is applied, in conjunction with the considered non-momentum factors, as an independent variable in order assess the relationship between the factors and expected returns on a share-by-share basis. The results of the panel data cross-sectional regressions clearly indicate that momentum produces a consistently significant and independent premium, conclusively proving that momentum is a priced factor that contributes to the cross-sectional variation in share returns listed on the JSE.Item Individualism as a driver of overconfidence, and its effect on industry level returns and volatility across multiple countries(2016) Horne, ChadThis study attempts to determine the possible effects of individualism on industry volatility. The implications of this for behavioural finance are extensive, showing firstly that different industries react differently to behavioural biases and secondly that overconfidence is a possible driver of the positive effect of individualism on industry volatility. The country selection process was relatively objective, taking two countries with high individualism indexes and two with low indexes and including one with a medium index value. The result was a sample of the United States of America, the United Kingdom, South Africa, China and Taiwan. The industry selection process was more subjective. Industries were selected which should have a higher propensity to behavioural biases with lower book to market ratios (software and computer services industry and pharmaceutical and biotechnology industry) and other industries which should not be as strongly affected by behavioural biases (banks, mining, oil and gas producers, and mobile telecommunications industries). In order to correct for ARCH effects the series’ were modelled using a GARCH (1, 1) model. The resulting residuals, which showed no autocorrelation, were then used to conduct panel data regressions on each of the industries. The results confirmed that individualism had a positive effect on volatility in the industries which were expected (software and computer services and pharmaceuticals and biotechnology industries). However, it was also determined that the banks industry was significantly affected by individualism, an effect which it was hypothesised, was due to the individualism of employees as opposed to investors.Item Modeling and forecasting stock return volatility in the JSE Securities Exchange(2016) Masinga, Zamani CalvinModeling and forecasting volatility is one of the crucial functions in various fields of financial engineering, especially in the quantitative risk management departments of banks and insurance companies. Forecasting volatility is a task of any analyst in the space of portfolio management, risk management and option pricing. In this study we examined different GARCH models in Johannesburg Stock Exchange (JSE) using univariate GARCH models (GARCH (1, 1), EGARCH (1, 1), GARCH-M (1, 1) GJR-GARCH (1, 1) and PGARCH (1, 1)). Daily log-returns were used on JSE ALSH, Resource 20, Industrial 25 and Top 40 indices over a period of 12 years. Both symmetric and asymmetric models were examined. The results showed that GARCH (1, 1) model dominate other models both in-sample and out-of-sample in modeling the volatility clustering and leptokurtosis in financial data of JSE sectoral indices. The results showed that the JSE All Share Index and all other indices studied here can be best modeled by GARCH (1, 1) and out-of-sample for JSE All Share index proved to be best for GARCH (1, 1). In forecasting out-of-sample EGARCH (1, 1) proved to outperformed other forecasting models based on different procedures for JSE All Share index and Top 40 but for Resource 20 RJR-GARCH (1, 1) is the best model and Industrial 25 data suggest PGARCH (1, 1)