[论文解读] Modelling risk for commodities in Brazil: An application to live cattle spot and futures prices

Renata G. Alcoforado, Alfredo D. Egı́dio dos Reis+2 more|arXiv (Cornell University)|Jul 15, 2021

Market Dynamics and Volatility参考文献 24被引用 4

一句话总结

本研究开发并比较了多种时间序列模型，利用牛 fattening 指数（BGI）预测巴西活牛价格，应用了 Holt-Winters、ARIMA、ARIMAX、GARCH 和 GARMA 技术。不含截距项的 GARMA(2,1) 模型在预测 BGI 现货与期货价格方面优于所有其他模型，为农业企业参与者提供了更优的风险管理方案。

ABSTRACT

This study analysed a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective was to develop a model that best portrays this commodity's behaviour to estimate futures prices more accurately. The database created contained 2,010 daily entries in which trade in futures contracts occurred, 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 transactions' results, investors must analyse fluctuations in assets' value for longer periods. Bibliographic research revealed 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 2017, this sector moved 523.25 billion Brazilian reals (about 130.5 billion United States dollars). In that year, agribusiness contributed 22% 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 was 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, 5 different methods were applied that allowed a comparison of 12 different models as ways to portray and predict the BGI commodity's behaviour. The results show that GARMA with order c(2,1) and without intercept is the best model.

研究动机与目标

• 为巴西活牛商品开发一个稳健的风险建模框架，重点关注牛 fattening 指数（BGI）。
• 比较多种时间序列模型在高精度预测 BGI 现货与期货价格方面的表现。
• 通过改进的价格预测，支持经济主体做出更明智的风险管理决策。
• 解决以往缺乏使用先进统计模型对巴西活牛价格动态进行综合实证研究的问题。

提出的方法

• 本研究使用了 2006 年 12 月至 2015 年 4 日期间共 2,010 个 BGI 现货与期货价格的每日观测数据。
• 应用五种建模方法：Holt-Winters 指数平滑、ARIMA、ARIMAX、GARCH 和广义自回归移动平均（GARMA）模型。
• GARMA 模型设定为阶次 c(2,1)，并分别在含截距项与不含截距项的条件下进行测试。
• 通过统计准则评估模型表现，以识别最适合预测的模型。
• 比较涵盖五种方法论下的 12 种不同模型变体，以确保结果的稳健性。

实验结果

研究问题

• RQ1 哪种时间序列模型最能捕捉巴西牛 fattening 指数中活牛现货与期货价格的动态特征？
• RQ2 不同模型设定，尤其是含截距项与不含截距项的 GARMA 模型，在预测 BGI 价格走势方面表现如何？
• RQ3 先进统计模型在多大程度上可提升巴西农业企业商品的风险估计与价格预测能力？
• RQ4 GARCH 与 GARMA 模型在建模牛价数据条件异方差性方面的相对表现如何？

主要发现

• 不含截距项的 GARMA(2,1) 模型在所有测试的 12 个模型中拟合最佳且预测精度最高。
• 该模型在捕捉 BGI 价格波动性与趋势动态方面显著优于 ARIMA、ARIMAX、GARCH 和 Holt-Winters 模型。
• GARMA 模型中省略截距项提升了模型效率并降低了残差方差。
• 结果证实，GARMA 模型特别适用于具有长记忆性与条件异方差性的商品价格序列建模。
• 本研究为巴西牛价市场风险建模建立了基准，有助于支持更明智的套期保值与交易决策。