Effects of the climate-related sentiment on agricultural spot prices: Insights from Wavelet Rényi Entropy analysis

• Published in: Energy economics Vol. 142; p. 108146
• Main Authors: Mastroeni, Loretta, Mazzoccoli, Alessandro, Quaresima, Greta
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2025
• Subjects: Agricultural commodities; Predictability; Sentiment; Wavelet entropy; C63; C22; C32; D53; C02; Predictability; Wavelet entropy; Agricultural commodities; C18; Sentiment
• ISSN: 0140-9883
• DOI: 10.1016/j.eneco.2024.108146

The economic impact of climate change on agriculture is complex and multifaceted, with public sentiment playing a crucial role. Public perception of climate events can significantly influence consumer behavior and investment decisions, adding uncertainty and volatility to agricultural markets. Beyond the direct effects of climate change, it is essential to understand how public reactions can shape and amplify economic consequences. This study analyzes the impact of climate-related sentiment and equity market performance on agricultural commodity spot prices using a novel approach that examines the most important intrinsic properties of time series related to their deterministic, stochastic, or chaotic behavior. We focus on the predictability of time series, applying our techniques to the spot prices of soybean, cotton, corn, wheat, coffee, and orange juice. Our method combines Rényi entropy and wavelet analysis to capture low- and high-probability events and distinguish between short-term and long-term trends. The main finding suggests that climate-related sentiment and equity market performance help to predict extreme events in long-term agricultural spot price distributions, though predictability decreases for short-term fluctuations. This has important implications with regard to forecasting models in agricultural markets. •Wavelet analysis is useful for decomposing time series in the time-frequency domain.•Wavelet energy based measure can be used to assess the predictability of a time series.•Application to energy commodities and relation to climate sentiment.