Investigating the predictive ability of AIS-data : the case of arabian gulf tanker rates
Master thesis
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http://hdl.handle.net/11250/2454692Utgivelsesdato
2017Metadata
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- Master Thesis [4378]
Sammendrag
This thesis investigates whether information derived from AIS-data incorporates superior information
about future freight rates. Specifically, we assess if such data improve the ability to predict
TD3 spot rates between August 2015 and mid-February 2016. The ability to anticipate short-term
fluctuations in freight rates is a key component to long-term profitability for both shipowners and
charterers, making the purpose of this thesis an important objective.
The AIS-data contain information about 81,728 individual shipments of crude oil between 2013
and mid-February 2016, and are reduced to 53,116 observations after proper cleansing. For analysis
purposes, information deemed relevant are converted into weekly time series, ending up with
162 observations in total. Data-driven selection tools are then used to identify the most powerful
predictors of future TD3 rates, and a multivariate VAR is specified in line with these results. In order
to investigate the relative performance of information derived from AIS-data, a one-step-ahead
forecast is conducted, and evaluated against an univariate ARMA and a multivariate VAR solely
based on publicly available data.
Our results suggest that multivariate models perform relatively better than univariate models to predict
future freight rates. Further, comparing error measures from the two multivariate VAR models
specified, we find weak evidence in favour of using information from AIS-derived data for predictive
purposes.