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中国从澳大利亚和巴西进口的大部分铁矿石使用好望角型船运输,准确预测该两条航线的即期运费对航运相关企业很有意义。运用自回归模型(AR)、自回归移动平均模型(ARMA)和向量自回归模型(VAR)三种模型预测即期价格,并对三种模型的预测误差进行比较,结果发现:三种模型所得到的预测结果的平均误差百分比均小于2%,完全可以满足企业预测即期运费的精度要求;拥有远期运费滞后因子的VAR模型的预测精度并不比单变量模型(AR,ARMA)高,可见远期运费市场对即期市场的价格引领作用有限;即期运费受到滞后一期自身的影响大于滞后一期远期价格,且统计上显著。
Most of the iron ore that China imports from Australia and Brazil use Capesize to accurately predict the spot shipping rates of these two routes is of great significance to the shipping related enterprises. The spot prices were predicted by AR models, ARMA models and VAR models, and the prediction errors of the three models were compared. The results showed that the three models The average error percentage of the forecast results obtained is less than 2%, which can fully meet the accuracy requirements of the enterprise spot freight forecasting; the prediction accuracy of the VAR model with the forward freight lag factor is not higher than that of the univariate model (AR, ARMA) The forward freight market has a limited effect on the price of the spot market. The impact of the delayed freight on the spot freight period is greater than the one lagging forward price, which is statistically significant.