Delson Chikobvu

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 paper uses theValue at Risk (VaR) and the Expected Shortfall (ES) to compare the riskiness of the two currency exchange rate volatility, namely BitCoin against the US dollar (BTC/USD) and the South African Rand against the US dollar (ZAR/USD). The risks calculated are tail-related measures, so the Extreme Value Theory is used to capture extreme risk more accurately. The Generalized Pareto distribution (GPD) is assumed under Extreme Value Theory (EVT). The family of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models was used to model the volatility-clustering feature. The Maximum Likelihood Estimation (MLE) method was used in parameter estimation. Results obtained from the GPD are compared using two underlying distributions for the errors, namely: the Normal and the Student-t distributions. The findings show that the tail VaR on the BitCoin averaging 1.6 and 2.8 is riskier than on South Africa’s Rand that averages 1.5 and 2.3 at 95% and 99%, respectively. The same conclusion is made about tail ES, the BitCoin average of 2.3 and 3.6 is higher (riskier) than the South African Rand averages at 2.1 and 2.9 at 95% and 99%, respectively. The backtesting results confirm the model adequacy of the GARCH-GPD in the estimation of VaR and ES, since all p-values are above 0.05.

In this study, the RiskMetrics method is used to estimate Value at Risk for two exchange rates: BitCoin/dollar and the South African Rand/dollar. Value at Risk is used to compare the riskiness of the two currencies. This is to help South Africans and investors understand the risk they are taking by converting their savings/investments to BitCoin instead of the South African currency, the Rand. The Maximum Likelihood Estimation method is used to estimate the parameters of the models. Seven statistical error distributions, namely Normal Distribution, skewed Normal Distribution, Student’s T-Distribution, skewed Student’s T-Distribution, Generalized Error Distribution, skewed Generalized Error Distribution, and the Generalized Hyperbolic Distributions, were considered when modelling and estimating model parameters. Value at Risk estimates suggest that the BitCoin/dollar return averaging 0.035 and 0.055 per dollar invested at 95% and 99%, respectively, is riskier than the Rand/dollar return averaging 0.012 and 0.019 per dollar invested at 95% and 99%, respectively. Using the Kupiec test, RiskMetrics with Generalized Error Distribution (p > 0.07) and skewed Generalized Error Distribution (p > 0.62) gave the best fitting model in the estimation of Value at Risk for BitCoin/dollar and Rand/dollar, respectively. The RiskMetrics approach seems to perform better at higher than lower confidence levels, as evidenced by higher p-values from backtesting using the Kupiec test at 99% than at 95% levels of significance. These findings are also helpful for risk managers in estimating adequate risk-based capital requirements for the two currencies.