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<p>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 <i>t</i> + <i>n</i> (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 <i>t</i> + 1 and <i>t</i> + 4&#xa0;weeks hedging horizons, the simulations found that using Gaussian MS-GARCH in <i>t</i> + 1 and <i>t</i>+ 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Oscar V. De la Torre-Torres,
  • Leticia Bollain‑Parra,
  • Amador Durán-Sánchez

摘要

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.