| dc.description.abstract | This study uses time series methods, including unit root test, cointegration test, granger causality, chow test, vector autoregressive model, and vector error correction model to explore the interaction between bunker fuel prices and freight rates. We selected IFO380 as the shipping bunker prices, and the shipping price index consists of three major freight indexes: Shanghai Containerized Freight Index (SCFI), Baltic Dry Index (BDI), Baltic Dirty Tanker Index (BDTI). The study period was from January 1, 2017, to December 31 2022 week of data.
The results of the empirical analysis found that: 1. the variables are non-stationary state, rendered after a first-order differential steady state. 2. Through Johansen′s cointegration test, only SCFI is not co-integrated with other freight rates and bunker indices after the structural break. In contrast, all other freight rates and bunker indices are co-integrated. 3. The Chow test shows that all the variables have structural breaks. The structural break date of IFO380 is on March 9, 2020; SCFI is on August 31, 2020; BDTI is on June 1, 2020; and BDI is on June 15, 2020. 4. We use the VECM and VAR models to capture the short-term relationships between variables before and after the structural break point and find significant changes in the short-term relationships between variables before and after the structural break point. Therefore, we hypothesize that the occurrence of a major event may change the short-term relationship between variables. 5. The Granger causality test for each shipping market before and after the structural breakpoint is as follows: In the container market, the SCFI leads the IFO380 and BDTI before and after the structural break point. In the bulk market, BDI leads IFO380 before the structural break point. However, there is no granger causality between BDI and IFO380 after the structural break point. In the tanker market, IFO380 leads BDTI before the structural break point, while BDTI leads IFO380 after the structural break point.
This study aims to allow carriers, shipowners, and investors to track the relationship between bunker prices and shipping prices to plan their fleet operations and achieve the goal of reducing investment risk. | en_US |