AIS data and the price of oil : a study of predictive feasability
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- Master Thesis 
Compared to the oil market, physical movement of oil-carrying vessels is very precise and reflects the real production rates more timely than official reports. In this paper, we examine whether detailed information on crude oil movements, obtained from AIS tracking system, can be used to better predict the oil price. We use a variety of model specifications and introduce a novel instrument for the role of expectations to this question. This instrument is based on vessel speed, and it offers insights into the apparent lack of empirical indications of speed optimization. We show that the AIS data can contribute to predicting the oil price. We also explore Kilian’s (2009) hypothesis that the model of the oil price should include three factors: expectations of future prices in addition to supply and demand. We triangulate his instrument with one that we construct independently. We have explored several different specifications for the relationship between inter- and intraregional oil ship traffic and the oil price and found that a statistically significant relationship exists. Our findings indicate that the correlation between variables make OLS an unsuitable tool for this analysis since endogeneity bias will suppress the actual relationship. However, we have found that the relationship is robust to different VAR specifications. The contribution to explanatory power as measured by Factor Error Variance Decomposition is marginal, but it might still be a small improvement on present methods. We have examined the apparent paradox of non-optimal ship speed behavior. We found that a less stringent specification apparently resolved the paradox; the freight rates do indeed influence ship speed if lags and correlation are allowed. The short time-span is preventing us from conclusively saying that the issue is resolved, but it appears at least to be worthy of further investigation. We assessed the validity of using ship opportunity cost as a measure for GDP. While we cannot address the question of possible bias created by Kilian’s use of the Baltic index, we nonetheless offer conceptual support, as our unrelated instrument for the same opportunity cost showed strong statistical significance.