巴西商品风险建模：活牛现货和期货价格的应用
Modelling risk for commodities in Brazil: An application to live cattle spot and futures prices
摘要：
这项研究分析了巴西Boi Gordo指数（BGI）的一系列活牛现货和期货价格。目的是开发一个最能描述这种商品行为的模型，以便更准确地估计期货价格。该数据库包含2006年12月1日至2015年4月30日期间发生的期货合约交易以及BGI现货销售的2，010个每日条目。需要衡量这类风险的最重要原因之一是设定损失限额。为了确定价格行为的模式以改善未来交易的结果，投资者必须分析较长时期内资产价值的波动。文献研究表明，没有其他研究使用这种方法对这种商品进行全面分析。养牛业是巴西的大生意，因为在2017年，该行业转移了5232.5亿巴西雷亚尔（约合1305亿美元）。在那一年，农业综合企业贡献了巴西国内生产总值的22%。使用建议的风险建模技术，经济代理人可以作出最佳决策，这些投资者的范围内的选择产生更有效的风险管理。该方法是基于Holt-Winters指数平滑算法，自回归积分移动平均（ARIMA），ARIMA与外源输入，广义自回归条件异方差和广义自回归移动平均（GARMA）模型。更具体地说，应用了5种不同的方法，可以比较12种不同的模型，以描述和预测BGI商品的行为。结果表明，阶数为c（2，1）的无截距GARMA模型是最优模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
