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Modelling Risk for Commodities in Brazil: An Application for Live Cattle Spot and Futures Prices

Author

Listed:
  • Renata G. Alcoforado

    (ISEG and CEMAPRE, Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisbon, Portugal
    Department of Accounting and Actuarial Sciences, Universidade Federal de Pernambuco, Recife 50670-901, Brazil)

  • Alfredo D. Egídio dos Reis

    (ISEG and CEMAPRE, Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisbon, Portugal)

  • Wilton Bernardino

    (Department of Accounting and Actuarial Sciences, Universidade Federal de Pernambuco, Recife 50670-901, Brazil)

  • José António C. Santos

    (ESGHT and CIEO, School of Management, Hospitality and Tourism, Universidade do Algarve, 8005-139 Faro, Portugal)

Abstract

This study analyses a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective is to develop a model that best portrays this commodity’s behaviour to estimate futures prices more accurately. The database created contains 2010 daily entries in which trade in futures contracts occurs, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transaction results, investors must analyse fluctuations in asset values for longer periods. Bibliographic research reveals that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2021, this sector moved BRL 913.14 billion (USD 169.29 billion). In that year, agribusiness contributed 26.6% of Brazil’s total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors’ reach produce more effective risk management. The methodology is based on Holt–Winters exponential smoothing algorithm, autoregressive integrated moving-average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving-average (GARMA) models. More specifically, five different methods are applied that allow a comparison of 12 different models as ways to portray and predict the BGI commodity behaviours. The results show that GARMA with order c (2,1) and without intercept is the best model. Investors equipped with such precise modelling insights stand at an advantageous position in the market, promoting informed investment decisions and optimising returns.

Suggested Citation

  • Renata G. Alcoforado & Alfredo D. Egídio dos Reis & Wilton Bernardino & José António C. Santos, 2023. "Modelling Risk for Commodities in Brazil: An Application for Live Cattle Spot and Futures Prices," Commodities, MDPI, vol. 2(4), pages 1-19, November.
    • Handle: RePEc:gam:jcommo:v:2:y:2023:i:4:p:23-416:d:1276488
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    References listed on IDEAS

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