Retius Chifurira

This paper examines the presence of cointegration between South African gold mining index and USD/ZAR exchange rate. The results show that gold index and USD/ZAR exchange rate series are both I(1) and are cointegrated. The Granger causality test shows a two-way directional causality between gold index and USD/ZAR exchange rate for the period 9 June 2005-9 June 2015. By accounting for possible structural breaks, the Zivot-Andrews unit root test suggests two different breaking points in the data. By using the breaking dates to divide the dataset into 3 sub-periods, the results show that gold index and USD/ZAR exchange rate series are not cointegrated. The Granger causality test shows no causality between the two variables. This finding suggests that gold mining index does not play a key role in explaining the trends in the exchange rate and likewise exchange rate does not affect gold mining index.

Keywords: USD/ZAR exchange rate, gold mining index, unit root tests, breaking points, cointegration.
JEL Classification: F3, F4, F63, O47

Agriculture is the backbone of Zimbabwe’s economy with the majority of Zimbabweans being rural people who derive their livelihood from agriculture and other agro-based economic activities. Zimbabwe’s agriculture depends on the erratic rainfall which threatens food, water and energy access, as well as vital livelihood systems which could severely undermine efforts to drive sustainable economic growth. For Zimbabwe, delivering a sustainable economic growth is intrinsically linked to improved climate modelling. Climate research plays a pivotal role in building Zimbabwe’s resilience to climate change and keeping the country on track, as it charts its path towards sustainable economic growth. This paper presents a simple tool to predict summer rainfall using standardized Darwin sea level pressure (SDSLP) anomalies and southern oscillation index (SOI) that are used as part of an early drought warning system. Results show that SDSLP anomalies and SOI for the month of April of the same year, i.e., seven months before onset of summer rainfall (December to February total rainfall) are a simple indicator of amount of summer rainfall in Zimbabwe. The low root mean square error (RMSE) and root mean absolute error (RMAE) values of the proposed model, make SDSLP anomalies for April and SOI for the same month an additional input candidates for regional rainfall prediction schemes. The results of the proposed model will benefit in the prediction of oncoming summer rainfall and will influence policy making in agriculture, environment planning, food redistribution and drought prediction for sustainable economic development.

Keywords: sustainable economic growth, standardized Darwin sea level pressure anomalies, southern oscillation index, summer rainfall prediction, Zimbabwe.
JEL Classification: Q16, Q25, Q54, Q55, Q58

This study is aimed at investigating the volatility dynamics and the risk-return relationship in the South African market, analyzing the FTSE/JSE All Share Index returns for an updated sample period of 2009–2019. The study employed several GARCH type models with different probability distributions governing the model’s innovations. Results have revealed strong persistent levels of volatility and a positive risk-return relationship in the South African market. Given the elaborate use of the GARCH approach of risk estimation in the existing finance literature, this study highlighted several weaknesses of the model. A noteworthy property of the GARCH approach was that the innovation distributions did not affect parameter estimation. Analyzing the GARCH type models, this theory was supported by the majority of the GARCH test results with respect to the volatility dynamics. On the contrary, it was strongly unsupported by the risk-return relationship. More specifically, it was found that while the innovations of the EGARCH (1, 1) model could account for the volatile nature of financial data, asymmetry remained uncaptured. As a result, misestimating of risks occurred, which could lead to inaccurate results. This study highlighted the significance of the innovation distribution of choice and recommended the exploration of different nonnormal innovation distributions to aid with capturing the asymmetry.

South Africa’s economy has faced many downturns in the previous decade, and to curb the spread of the novel SARS-CoV-2, the lockdown brought South African financial markets to an abrupt halt. Therefore, the implementation of risk mitigation approaches is becoming a matter of urgency in volatile markets in these unprecedented times. In this study, a hybrid generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions, namely the Generalized Pareto Distribution (GPD) and the Nolan’s S0-parameterization stable distribution (SD), were fitted to the returns of three FTSE/JSE indices, namely All Share Index (ALSI), Banks Index and Mining Index, as well as the daily closing prices of the US dollar against the South African rand exchange rate (USD/ZAR exchange rate). VaR values were estimated and back-tested using the Kupiec likelihood ratio test. The results of this study show that for FTSE/JSE ALSI returns, the hybrid exponential GARCH (1,1) model with SD model (EGARCH(1,1)-SD) outperforms the GARCH-GPD model at the 2.5% VaR level. At VaR levels of 95% and 97.5%, the fitted GARCH (1,1)-SD model for FTSE/JSE Banks Index returns performs better than the GARCH (1,1)-GPD. The fitted GARCH (1,1)-SD model for FTSE/JSE Mining Index returns is better than the GARCH (1,1)-GPD at 5% and 97.5% VaR levels. Thus, this study suggests that the GARCH (1,1)-SD model is a good alternative to the VaR robust model for modeling financial returns. This study provides salient results for persons interested in reducing losses or obtaining a better understanding of the South African financial industry.