This study examines the dynamics of equity market volatility and economic policy uncertainty (EPU) in South Africa during the COVID-19 pandemic. Using daily return data for sectoral indices and the JSE All Share Index (ALSI) from 1 January 2020 to 31 March 2022, the analysis explores both market-wide and sector-specific volatility responses. Univariate GARCH-family models (GARCH (1,1), E-GARCH, and T-GARCH) are employed to capture volatility clustering, persistence, and asymmetry across sectors. The results show that volatility was highly persistent during the pandemic, with sectoral differences in sensitivity to shocks: Consumer Staples and Financials were particularly reactive to recent news, while Health Care and Basic Materials were more stable. Asymmetric models confirm that market sentiment was predominantly driven by negative news, except in the Energy sector, where positive recovery signals played a stronger role. Correlation analysis further indicates that most sectors were moderately correlated with the ALSI, while Energy and Health Care behaved more independently. In contrast, both the ALSI and sector returns exhibited weak and negative correlations with the South African EPU index, suggesting that uncertainty did not translate directly into equity market declines. Overall, the findings highlight the importance of sectoral heterogeneity in volatility dynamics and suggest that during extreme market events, investors can mitigate downside risk by reallocating portfolios toward more resilient sectors.

This study examined the impact of location differentials (LDs) on soybean futures trading in South Africa. This study uses a systematic approach, employing the Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to analyze relationships between soybean futures prices and LDs. The results suggest that LDs have caused price stabilization for most of the contract months, while the variability and occasional extremes in the spot price increased. Post-LD analysis showed that the volatility was lower, with a normalization of price structures, but, still, regional disparities were driven by transport costs and logistical issues. LDs also affected silo utilization, and the rates of re-delivery differed among regions, reflecting local market dynamics and operational efficiencies. This, in essence, suggests that LDs act to enhance the predictability of markets and price harmonization; LDs also equally require concerted interventions in regional disparities and optimization of market performances. Future studies need to determine the impact that LDs, over a long period, have on market efficiency, regional trade, and general economy-wide indicators like farmers’ incomes and rural development.

Purpose: This research study investigates how macroeconomic factors, such as short-term and long-term interest rates, the real exchange rate, and GDP growth, influence the pricing and volatility of agricultural derivatives, specifically focusing on white maize futures and options traded in South Africa. Agricultural derivatives are crucial for managing price risks in a country where agriculture plays a vital economic role. Design/Methodology/Approach: The study employs a quantitative research approach using secondary data from 2000 to 2023 sourced from reliable institutions like the South African Reserve Bank (SARB), Statistics South Africa, and the Johannesburg Stock Exchange (JSE). The Autoregressive Distributed Lag (ARDL) model is applied to evaluate both short-term and long-term relationships between the selected macroeconomic variables and derivative performance. Additionally, unit root tests (ADF and PP) are conducted to assess data stationarity, and diagnostic tests are used to verify the reliability of the results. Findings: The study highlights the dynamic interplay between macroeconomic factors and agricultural markets, suggesting that macroeconomic shifts, particularly during periods of economic instability, can substantially affect market volatility and risk management strategies. Practical Implications: The research contributes valuable insights for policymakers, investors, and stakeholders by offering practical implications for improving market efficiency, managing risks, and enhancing agricultural policy formulation in South Africa. It emphasizes the need for effective risk management strategies in the face of changing macroeconomic conditions to ensure market stability and sustainability. Originality/Value: This study provides new insights into the relationship between macroeconomic factors and agricultural derivatives in South Africa, particularly during periods of economic instability. The findings offer significant contributions for policymakers and market participants in improving risk management strategies and market efficiency.

The contagious COVID-19 disease not only brought about a global health crisis but also a disruption in the global economy. The uncertainty levels regarding the impact of the disease increased volatility. This study analyses stock market volatility and Economic Policy Uncertainty (EPU) of South Africa (SA) with that of the United States of America (USA) and Brazil, Russia, India, and China (BRIC) during the COVID-19 pandemic. The study aims to analyse volatility spillovers from a developed market (USA) to emerging markets (BRIC countries) and also to examine the causality between EPU and stock returns during the COVID-19 pandemic. By employing the GARCH-in-Mean model from a sample of daily returns of national equity market indices from 1 January 2020 to 31 March 2022, SA and China are shown to be the most volatile during the pandemic. By using the diagonal Baba, Engle, Kraft, and Kroner (BEKK) model to analyse spillover effects, evidence of spillover effects from the US to the emerging countries is small but statistically significant, with SA showing the strongest impact from US market shocks. From the Granger causality test, Brazil’s and India’s equity markets are shown to be highly sensitive to changes in EPU relative to the other countries.