Analysing white maize hedging strategies in South Africa

Abstract

The number of derivative-based hedging strategies available to maize producers or advocated by role-players in the maize market are endless. Hedging strategies are often based on a short-term market view or a very optimistic one-sided perspective of certain influential factors. These factors may include current and projected local and/or international stock levels, exchange rate expectations, as well as the maize producers' own financial situation. One of the most important determinants of an optimal hedging strategy is to ensure that maize producers understand the risk involved, as well as the purpose of imposing a hedging strategy. The main purpose of a hedging strategy is to protect the value of the physical commodity, and to lock in favourable, preferably profitable, price levels. This emphasises the importance for a maize producer to implement an optimised hedging strategy based on an informed decision. The reality in South Africa is that maize producers are reluctant to adopt derivative instruments, which are the only means available to them to manage their price risk on SAFEX. The reason for this phenomenon is that maize producers do not always understand derivative instruments, what the outcome of these hedging strategies entail, and the risks associated with utilising derivative instruments. This aggravates their distrust of the market structure. This distrust is further exacerbated when the same strategies perform differently in different production seasons, as futures price formation may differ based on the influence of price determinant factors. The result is that maize producers struggle to see the advantages of hedging versus not hedging, causing them to distrust the use of derivative instruments which leads to avoidance of any form of marketing plan or hedging strategy. Inevitably, the absence of a structured hedging strategy leads to a scenario where producers sell most of their produce closer to market lows due to fear of further price declines. In order to address these challenges, literature suggests that maize producers' general attitude could be changed if their perceptions about price risk management could be improved by the provision of reliable price formation predictions and the identification of more optimal hedging strategies. This study accomplished this feat by establishing a structured approach in the form of a filter model to enable producers to derive an informed price risk management decision. Firstly, the structured approach required the identification of seasonal similarities based on influential price determinant factors in order to identify a more probable price formation expectation. The identification of seasonal similarities by means of the filter model was enabled through the synergy provided by percentile rank grouping analyses and cluster analyses of the influential price determinant factors. The second step in the structured approach was to identify the more optimal course of price risk management action. The initial approach ranked hedging strategy returns by means of a performance measure analysis in order to determine whether specific hedging strategies would be more optimal to deploy in a specific type of production season. The performance measure results remained nonsensical despite an attempt to establish more logical rankings by changing the way in which hedging strategy returns were calculated. However, a comparison of hedging strategy realised prices to the average of the relevant July white maize futures contract price established the means to distinguish between more optimal hedging strategies to deploy, given the seasonal price formation expectation (upwards, downwards or sideways). The established decision-making tool in the form of a filter model was able to combine all of these inputs in a meaningful manner. An example of the successful application of the model to link seasons based on factor similarities was provided in an ex-ante analysis of the 2018/2019 production year. The ability of the filter model to enable a thorough analysis of all the influential market factors in order to make an informed hedging strategy decision based on the expected price progression of the following production year, proved meaningful.