Home > Research > Publications & Outputs > A Novel Forecasting Model for the Baltic Dry In...

Electronic data

  • Accepted manscruipt

    Rights statement: This is the peer reviewed version of the following article: Makridakis, S, Merikas, A, Merika, A, Tsionas, MG, Izzeldin, M. A novel forecasting model for the Baltic dry index utilizing optimal squeezing. Journal of Forecasting. 2019; 1– 13. 10.1002/for.2613 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/for.2613 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 840 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing. / Makridakis, Spyros; Merikas, Andreas; Merika, Anna et al.

In: Journal of Forecasting, Vol. 39, No. 1, 01.01.2020, p. 56-68.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Makridakis, S, Merikas, A, Merika, A, Tsionas, M & Izzeldin, M 2020, 'A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing', Journal of Forecasting, vol. 39, no. 1, pp. 56-68. https://doi.org/10.1002/for.2613

APA

Vancouver

Makridakis S, Merikas A, Merika A, Tsionas M, Izzeldin M. A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing. Journal of Forecasting. 2020 Jan 1;39(1):56-68. Epub 2019 May 22. doi: 10.1002/for.2613

Author

Makridakis, Spyros ; Merikas, Andreas ; Merika, Anna et al. / A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing. In: Journal of Forecasting. 2020 ; Vol. 39, No. 1. pp. 56-68.

Bibtex

@article{e3b5b6dd72bf4e0a8b7fa43848a12854,
title = "A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing",
abstract = "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.",
keywords = "Forecasting, Bayesian methods, Compressed Regression, Baltic Dry Index, Maritime Shipping",
author = "Spyros Makridakis and Andreas Merikas and Anna Merika and Mike Tsionas and Marwan Izzeldin",
note = "This is the peer reviewed version of the following article: Makridakis, S, Merikas, A, Merika, A, Tsionas, MG, Izzeldin, M. A novel forecasting model for the Baltic dry index utilizing optimal squeezing. Journal of Forecasting. 2019; 1– 13. 10.1002/for.2613 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/for.2613 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.",
year = "2020",
month = jan,
day = "1",
doi = "10.1002/for.2613",
language = "English",
volume = "39",
pages = "56--68",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - A Novel Forecasting Model for the Baltic Dry Index Utilizing Optimal Squeezing

AU - Makridakis, Spyros

AU - Merikas, Andreas

AU - Merika, Anna

AU - Tsionas, Mike

AU - Izzeldin, Marwan

N1 - This is the peer reviewed version of the following article: Makridakis, S, Merikas, A, Merika, A, Tsionas, MG, Izzeldin, M. A novel forecasting model for the Baltic dry index utilizing optimal squeezing. Journal of Forecasting. 2019; 1– 13. 10.1002/for.2613 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/for.2613 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Forecasting

KW - Bayesian methods

KW - Compressed Regression

KW - Baltic Dry Index

KW - Maritime Shipping

U2 - 10.1002/for.2613

DO - 10.1002/for.2613

M3 - Journal article

VL - 39

SP - 56

EP - 68

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 1

ER -