Commodities

Price volatility in the South African fresh produce market poses significant risks to the entire value chain. This study examines the extent of price volatility and spillover effects in these markets to improve price risk management and enhance market stability. Using weekly price data for eight major vegetables (cabbages, carrots, garlic, onions, potatoes, sweet potatoes, spinach, and tomatoes) collected from 19 regional fresh produce markets, volatility patterns were initially assessed with descriptive statistics. Time-varying volatility persistence was modelled using ARCH and GARCH frameworks. The DCC-GARCH framework was used to evaluate spillover effects between markets, and cointegration analysis is employed to determine both short- and long-run interdependencies. The results confirm the existence of spillover effects and patterns of price volatility in the fresh produce markets. We found volatility spillovers between key regional markets. For example, Johannesburg and Tshwane fresh produce markets (large central markets) transmit to several smaller markets, as indicated by significant DCC-GARCH spillover coefficients. Cointegration results show the partial integration of fresh produce markets, suggesting that price movements and volatility are interconnected across regions. This empirical result underscores the importance of understanding price risk management strategies in fresh produce markets and helps value chain decision makers better understand, anticipate, or test the possible effects of price volatility in fresh produce markets at any given time. Policy makers and other stakeholders in the value chain are equipped with knowledge of how best to serve society.

This study looks at the impact of geopolitical risk on the volatility of wheat futures returns over the period 2012–2023, while controlling for inventories, shipping rates, and speculative activity. Using the volatility of CBOT first nearby futures returns, we apply a quantile regression approach to assess the impact of the variables on different parts of the volatility distribution. More specifically, we adopt the Quantile Autoregressive Distributed Lag (QARDL) model, which allows for examining the dynamic short- and long-run effects. We find that geopolitical risk has a non-linear, large positive effect on the top quartile of the distribution of wheat futures returns. We also show that the response of the volatility of wheat futures to shocks in the control variables is mostly non-linear across the conditional quantiles, significant in the tails and not around the median.

The urgent need to mitigate climate change has elevated green hydrogen as a sustainable alternative to fossil fuels, while green cryptocurrencies have emerged to address the environmental concerns of traditional cryptocurrency mining. This study investigates the dynamic correlation between the green hydrogen market and selected green cryptocurrencies (Cardano, Stellar, Hedera, Algorand, and Chia) from July 2021 to April 2024, utilizing the Dynamic Conditional Correlation GARCH (DCC-GARCH) model with robustness checks using EGARCH and GJR-GARCH specifications. Our findings reveal significant correlations, with peaks reaching up to 50% in 2022, a period likely influenced by the Russia-Ukraine conflict. Subsequently, a decline in these correlations was observed in 2023. These results underscore the interconnectedness of sustainability-driven markets, suggesting potential contagion effects during periods of global instability. The high persistence of correlation shocks (α + β values approaching unity) indicates that correlation regimes tend to be long- lasting, with important implications for portfolio diversification and risk management strategies. Robustness checks using EGARCH and GJR-GARCH specifications confirmed qualitatively similar patterns, reinforcing the validity of our findings into the evolving landscape of green finance and energy.