近年來波羅的海散裝運價指數波動劇烈，尤其是2007年與2008年，指數自2007年2月之4219點攀升至11月之11039點之歷史新高，漲幅高達161%，但旋即在2008年11月跌至891點，跌幅達92%。過去幾年因全球經濟成長，工業與農業用之原物料呈現供不應求狀態，因此漲聲不斷。因散裝船噸與原物料供需失衡，導致運費與原物料價格波動劇烈，因此，本研究利用原物料價格建構預測運費指數模型，本模型將可提供建議給散裝市場相關之利害關係人以利其制定相關政策來規避風險或有效降低投資風險或營運成本。本論文利用原物料價格為變數來建構預測運費指數之模型，不僅預測波羅的海運價指數(BDI)也預測了BCI,BPI以及BSI，研究期間自2003年1月至2007年12月，研究方法採用了X12，H-P，MAXR以及非線性，本研究亦採用MAPE來判斷預測模型之配適度是否良好。
根據實證研究顯示，在本研究期間內，每一個預測模型之顯著變數皆為鐵礦砂，因此本論文採用鐵礦砂來建構非線性模型並預測散裝航運運價指數。在模型預測能力之驗證方面，根據平均絕對誤差百分比MAPE(mean absolute percentage error)之顯示，本文所建立之預測模型其值皆低於10%，為良好之預測模型，是故，本論文建構之模型為一穩健之預測模型可提供預測散裝航運運價指數，並可幫助相關使用者因利用此模型而達避險或營運或投資規劃之參考。

In recent years, tramp shipping has had a high level of volatility in the dry bulk market. From February 2007 to November 2008, the Baltic Dry Index (BDI) rose 161 percent from 4219 to a historical high of 11039. But the index dropped to 891 in November 2008, record a loss of 92% in a year. Over the past few years due to the global economic growth, most commodities have presented shortage. Therefore, their prices are also reaching their peak. As a result of excess demand, the freight rate and raw materials prices has a high level of volatility. Hence this paper would construct prediction models with raw materials prices, it might provide the suggestions for the stakeholders that relate with the dry bulk market to make policy to avoid risk or reduce costs. The purpose of this study is not only using the price of raw materials prices to predict the dry bulk freight rate index but also BCI, BPI and BSI over the period January 2003 to December 2007 using X12, H-P filter, maximum R-square improvement (MAXR), nonlinear for predicting models with different freight rate (BDI, BCI, BPI and BSI). This research also introduces mean absolute percentage error (MAPE) values as a criterion to judge the model’s fitness in the different prediction models.
The empirical results of those models show iron ore as the significant variable in this paper during the research period. Therefore, this paper adopts the iron ore to construct nonlinear models and predict dry bulk freight rate indexes. It also shows all the mean absolute percentage error (MAPE) values are below 10%. According to the MAPE value, it clearly shows these prediction models could be robust to predict the freight rate indexes and it also could provide useful information for stakeholders to avoid opera risk or deal their asset by portfolio.

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