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

Author

Listed:
  • Spyros Makridakis
  • Andreas Merikas
  • Anna Merika
  • Mike G. Tsionas
  • Marwan Izzeldin

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.

Suggested Citation

  • Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:1:p:56-68
    DOI: 10.1002/for.2613
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    3. Miao Su & Keun Sik Park & Sung Hoon Bae, 2024. "A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 21-43, March.
    4. Yanhui Chen & Ailing Feng & Shun Chen & Jackson Jinhong Mi, 2024. "Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 802-815, April.
    5. Elie Bouri & Rangan Gupta & Luca Rossini, 2022. "The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index," Working Papers 202229, University of Pretoria, Department of Economics.
    6. Hakan Yilmazkuday, 2023. "COVID-19 effects on the S&P 500 index," Applied Economics Letters, Taylor & Francis Journals, vol. 30(1), pages 7-13, January.
    7. Ahundjanov, Behzod B. & Akhundjanov, Sherzod B. & Okhunjanov, Botir B., 2021. "Risk perception and oil and gasoline markets under COVID-19," Journal of Economics and Business, Elsevier, vol. 115(C).

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