A novel forecasting model for the Baltic dry index utilizing optimal squeezing

Marine transport has grown rapidly as the result of globalization and sustainable world growth rates. Shipping market risks and uncertainty have also grown and need to be mitigated with the development of a more reliable procedure to predict changes in freight rates. In this paper, we propose a new forecasting model and apply it to the Baltic Dry Index (BDI). Such a model compresses, in an optimal way, information from the past in order to predict freight rates. To develop the forecasting model, we deploy a basic set of predictors, add lags of the BDI and introduce additional variables, in applying Bayesian compressed regression (BCR), with two important innovations. First, we include transition functions in the predictive set to capture both smooth and abrupt changes in the time path of BDI; second, we do not estimate the parameters of the transition functions, but rather embed them in the random search procedure inherent in BCR. This allows all coefficients to evolve in a time‐varying manner, while searching for the best predictors within the historical set of data. The new procedures predict the BDI with considerable success.

[2]  Chi-Chen Wang,et al.  The dynamic analysis of Baltic exchange dry index , 2014 .

[3]  Kevin Cullinane,et al.  A portfolio analysis of market investments in dry bulk shipping , 1995 .

[4]  A. Alizadeh,et al.  Predictive power and unbiasedness of implied forward charter rates , 2007 .

[5]  Manolis G. Kavussanos,et al.  Seasonality patterns in dry bulk shipping spot and time charter freight rates , 2001 .

[6]  Michael Beenstock,et al.  An econometric model of the world market for dry cargo freight and shipping , 1989 .

[7]  K. R. Kadiyala,et al.  Numerical Methods for Estimation and Inference in Bayesian VAR-models , 1997 .

[8]  Kevin Cullinane,et al.  A Time-Varying Risk Premium in the Term Structure of Bulk Shipping Freight Rates , 2005 .

[9]  Jørgen Randers,et al.  GREENHOUSE GAS , 2019 .

[10]  R. Tsay.  Testing and modeling multivariate threshold models , 1998 .

[11]  J. Geweke,et al.  Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns , 2008 .

[12]  Manolis G. Kavussanos,et al.  The forward pricing function of the shipping freight futures market , 1999 .

[13]  Dimitris Korobilis,et al.  Large Time-Varying Parameter VARs , 2012 .

[14]  Roy Batchelor,et al.  Forecasting spot and forward prices in the international freight market , 2007 .

[15]  Emanuel Parzen,et al.  ARARMA models for time series analysis and forecasting , 1982 .

[16]  Roar Adland,et al.  Are Spot Freight Rates Stationary , 2006 .

[17]  Harriet Black Nembhard,et al.  EUROPEAN JOURNAL OF OPERATIONAL , 1998 .

[18]  Derek W. Bunn,et al.  Synthesis or selection of forecasting models , 1982 .

[19]  David B. Dunson,et al.  Bayesian Compressed Regression , 2013, ArXiv.

[20]  Stratos Papadimitriou,et al.  A Novel Approach to Forecasting the Bulk Freight Market , 2017 .

[21]  H. Geman,et al.  Shipping Markets and Freight Rates: An Analysis of the Baltic Dry Index , 2012, The Journal of Alternative Investments.

[22]  Kevin Cullinane,et al.  A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited , 1999 .