The use of Markov-Switching GARCH models in a Mexican rice spot price hedging algorithm with CME rice futures

Oscar V. Torre-Torres (), Leticia Bollain‑Parra () and Amador Durán-Sánchez ()
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Oscar V. Torre-Torres: Universidad Michoacana de San Nicolás de Hidalgo (UMSNH)
Leticia Bollain‑Parra: Fundación Coppel
Amador Durán-Sánchez: Instituto Universitario de Investigación para el Desarrollo Territorial Sostenible (INTERRA), Universidad de Extremadura

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 3, No 15, 2253-2283

Abstract: Abstract The present paper tests the effectiveness of using symmetric Markov-Switching GARCH (MS-GARCH) and asymmetric MS-EGARCH models in a hedging decision algorithm for the Mexican spot rice price. The rationale of the simulated algorithm is to hedge the rice price with a short, one-month Chicago Mercantile (CME) rice future if the producer forecasts a high volatility or distress scenario at t + n (the models assumed a two-scenario context). Such forecasts were made with the MS, MS-GARCH, or MS-GARCH model. Also, the simulations assumed an unconditional non-switching location parameter (arithmetic mean) or a conditional parameter estimated with the impact of the CME rice future price and its speculaton ratio. With weekly simulations and t + 1 and t + 4 weeks hedging horizons, the simulations found that using Gaussian MS-GARCH in t + 1 and t+ 4 adds additional income due to hedging, but this result only holds in the short-term and due to market issues. Consequently, this work is among the first to test the benefits of the MS-GARCH model for agricultural non-commodity price hedging and for food security applications.

Keywords: Markov-Switching GARCH; Active hedging decisions; Algorithmic trading; Mexican rice market; Agricultural futures; Food security (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02169-9

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