Forecasting spot prices in bulk shipping using multivariate and univariate models

This paper employs an applied econometric study concerning forecasting spot prices in bulk shipping in both markets of tankers and bulk carriers in a disaggregated level. This research is essential, as spot market is one of the most volatile markets and there is a great uncertainty about the future development of spot prices. This uncertainty could be reduced by using estimates of ex-post and ex-ante forecasts. Econometric analysis focuses in the comparison of different econometric models from two important categories of econometrics: (1) multivariate models (VAR and VECM) and (2) univariate time series models (ARIMA, GARCH and E-GARCH) in order to derive the best predicting model for each ship type. Also, forecasts can be modified to yield an improved performance of forecasting accuracy via the theory of combining methods. Ex-post and ex-ante forecasts are estimated on the basis of best predicting model's performance. Results show that the combining methodology can reduce even more the forecasting errors. The results of empirical analysis could also be useful from the specialization, identification, estimation, and evaluation of previous econometric models' point of view. Also, ex-ante forecasts, which are taking into consideration the present economic crisis, can be used for the formation of efficient economic policy from decision-makers of shipping industry reducing even more spot markets' risk.