AI Summary of Peer-Reviewed Research

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SOI spillovers were strongest for SAFEX maize

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Research area:Financial economicsEconomics and EconometricsAgricultural risk and resilience

What the study found

Southern Oscillation Index (SOI) signals, which are climate measures linked to El Niño and La Niña events, were more strongly connected to SAFEX maize than to CBOT corn and soybean futures. The study reports that SOI-based signals can act as early warning signals for grain futures volatility.

Why the authors say this matters

The authors conclude that SOI provides early warning signals for grain market hedging. They also state that the findings highlight regional asymmetries in climate vulnerability.

What the researchers tested

The researchers examined time-frequency transmission between ENSO-linked climate signals, proxied by the SOI, and grain futures traded on CBOT and SAFEX. They used CBOT corn and soybean futures and SAFEX maize data, together with partial wavelet coherence, multi-scale wavelet decomposition, Granger causality, and wavelet quantile regression, while controlling for macro-financial confounders.

What worked and what didn't

The results show pronounced scale-dependent SOI spillovers in SAFEX maize, with co-movements intensifying at medium- to long-term horizons. Both 30-day and 90-day SOI aggregates had statistically significant forecasting power for SAFEX maize returns and volatility, while CBOT corn and soybean futures showed weaker and more episodic sensitivity.

What to keep in mind

The available summary does not describe specific limitations beyond the focus on these three grain futures markets. The findings are reported for the studied futures contracts and the climate signals examined in this article.

Key points

  • SOI spillovers were strongest for SAFEX maize.
  • 30-day and 90-day SOI aggregates significantly forecast SAFEX maize returns and volatility.
  • CBOT corn and soybean futures showed weaker, more episodic sensitivity to SOI signals.
  • The study used partial wavelet coherence, wavelet decomposition, Granger causality, and wavelet quantile regression.
  • The authors say SOI-based signals may provide early warning for grain market hedging.

Disclosure

Research title:
SOI spillovers were strongest for SAFEX maize
Authors:
Ayesha Sayed, Chun‐Sung Huang, Christo Auret
Institutions:
University of Cape Town, University of the Witwatersrand
Publication date:
2026-02-27
OpenAlex record:
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AI provenance: This post was generated by OpenAI. The original authors did not write or review this post.