{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T12:28:23Z","timestamp":1766406503931,"version":"3.48.0"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Chair ACTIONS under the aegis of the Risk Foundation, a joint initiative of BNP Paribas Cardif and by the Institut des Actuaires"},{"name":"FCT-Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["project\/unit UID\/06522\/2025"],"award-info":[{"award-number":["project\/unit UID\/06522\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Commodities"],"abstract":"<jats:p>We study one of the world\u2019s largest cattle markets by revisiting and extending previous work on the forecasting of Brazil\u2019s Boi Gordo Index (BGI). Using an updated daily dataset (July 2006\u2013September 2025, inflation-adjusted), we evaluate classical and machine learning (ML) approaches for price prediction. Methods include Exponential Smoothing (Simple, Holt, and Holt\u2013Winters), ARMA\/ARIMA\/SARIMA, GARMA variants, GARCH, Theta, Prophet, and XGBoost; models are compared under a strictly chronological 90\/10 holdout (~476 test days) using RMSE, MAE, and MSE, with the AIC guiding within-family selection. Results show that, for the full out-of-sample window, GARMA delivers the best overall accuracy, with ARMA and Holt\u2013Winters close behind, while Prophet and XGBoost perform comparatively worse in this volatile setting. Performance is horizon-dependent: in the first 180 test days, prior to the late-2024 level shift, Holt attains the lowest RMSE\/MSE, and XGBoost achieves the lowest MAE. No method anticipates the October\u2013November 2024 exogenous jump and subsequent correction, highlighting the difficulty of structural breaks and the need for timely re-specification. We conclude that GARMA is a robust default for long, turbulent windows, whereas smoothing and ML methods can be competitive on shorter horizons. These findings inform risk measurement and risk mitigation strategies in Brazil\u2019s cattle futures market.<\/jats:p>","DOI":"10.3390\/commodities5010001","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T11:43:31Z","timestamp":1766403811000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting Boi Gordo Index Futures: Long-Run Daily Data, Structural Breaks, and a Comparative Evaluation of Classical and Machine Learning Time-Series Models"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7495-8057","authenticated-orcid":false,"given":"Renata G.","family":"Alcoforado","sequence":"first","affiliation":[{"name":"Department of Accounting and Actuarial Science, Universidade Federal de Pernambuco, Recife 50670-901, Brazil"},{"name":"ISEG Research, ISEG Lisbon School of Economics & Management, Universidade de Lisboa, 1200-781 Lisbon, Portugal"},{"name":"Chaire ACTIONS & Institut de Math\u00e9matique de Marseille, Aix-Marseille Universit\u00e9, 13003 Marseille, France"}]},{"given":"Hudo L. S. G.","family":"Alcoforado","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universidade Federal Rural de Pernambuco, Recife 52171-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2533-1343","authenticated-orcid":false,"given":"Alfredo D.","family":"Eg\u00eddio dos Reis","sequence":"additional","affiliation":[{"name":"ISEG Research, ISEG Lisbon School of Economics & Management, Universidade de Lisboa, 1200-781 Lisbon, Portugal"}]},{"given":"Pedro A. d. L.","family":"Ten\u00f3rio","sequence":"additional","affiliation":[{"name":"Department of Accounting and Actuarial Science, Universidade Federal de Pernambuco, Recife 50670-901, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"398","DOI":"10.3390\/commodities2040023","article-title":"Modelling risk for commodities in Brazil: An application for live cattle spot and futures prices","volume":"2","author":"Alcoforado","year":"2023","journal-title":"Commodities"},{"key":"ref_2","unstructured":"(2025, October 09). Our World in Data. Number of Cattle. Available online: https:\/\/ourworldindata.org\/grapher\/cattle-livestock-count-heads."},{"key":"ref_3","first-page":"66","article-title":"The Brazilian economic crisis during the period 2014\u20132016: Is there precedence of internal or external factors","volume":"12","author":"Vartanian","year":"2019","journal-title":"J. Int. Glob. Econ. 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