本研究以Lamont (2001)提出的Economic Tracking Portfolio (ETP)追蹤波羅的海指數 (Baltic Dry Index)。 根據先前關於ETP的研究，如Christoffersen (2000), Hayes (2001), Junttila (2004) 與 Raunig (2007)，ETP皆以一國境內的資產報酬率預測國內總體經濟變數。Junttila (2007)將此方法延伸至以多國資產報酬率預測總體經濟變數。而本研究也以此概念追蹤波羅的海指數，並探討追蹤績效，基於不同的預測期間與估計期間，其中控制變數並無加入投資組合當中。研究結果顯示，無論資料頻率為何，遞迴視窗之追蹤績效優於滾動視窗。相較於其他產業，採礦業與鋼鐵業之報酬率包含最多關於波羅的海指數之資訊。整體而言，此追蹤投資組合能夠補捉波羅的海指數之趨勢，可被使用為避險工具以規避此風險。

In this paper, I apply the economic tracking portfolio (ETP) approach developed by Lamont (2001) to track Baltic Dry Index (BDI). According to previous studies of ETP, such as Christoffersen (2000), Hayes (2001), Junttila (2004) and Raunig (2007), ETP is tested in closed-economy, using domestic equity as base assets. Junttila (2007) extends this approach to forecast the macroeconomic variables by using international equity returns. Our study also utilizes this concept to forecast BDI, control variables ignored here, and investigates the tracking performance based on different data frequency, forecast horizon, and training period. The results show that, no matter what data frequency is, the performance of recursive window is better than that of rolling window. The returns of diversified mining and iron steel contain more information than other industries about BDI. As a whole, the tracking portfolio can capture the trend of BDI, and also can be used as hedging tool by the practitioners.

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