Panamax 4TC Rate Forecasting: A Kalman-Filter Approach
22 Pages Posted: 9 Dec 2012
Date Written: May 20, 2010
Abstract
This paper compares a series of Vector Autoregressive Models, Vector Error Coerrection Models and ARIMA to State space models using the Kalman filter routine to Forecast Spot Panamax 4TC rates. The null hypothesis of this investigation is that VAR and ARIMA models with lagged spot and Forward Freight Agreements (FFA) returns provide the best forecast for spot rates. The alternative hypothesis is that they do not. In that respect, if the null the hypothesis holds then VAR or ARIMA models calculated through a Maximum likelihood OLS will be the best estimates of expected spot prices. The second Null hypothesis is that the FFA returns can be used to provide better forecasts than regressing the spot returns to its lagged first differences i.e. either through OLS or not. Although it is found that FFAs do contain information about the spot, as indicated by past literature, linear dynamic models of lagged spot returns exhibit a lower Root Mean Squared error and directional forecasting Accuracy than all other models for daily forecasts. However, results of weekly and monthly forecasts are mixed, indicating that incremental reduction in RMSE is higher when including forward contracts of longer maturities as explanatory variables. In addition, it is found that chained timeseries of contracts with longer maturity contain more information about the spot.
Keywords: Forward Freight Agreement, Panamax 4TC, Econometrics, Time-series Forecasting, Kalman Filter
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