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Time series forecasting methods for the Baltic dry index

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  • Christos Katris
  • Manolis G. Kavussanos

Abstract

This paper forecasts the daily Baltic Dry Index (BDI) using time series and machine learning methods. Significant business cycles and freight rate volatility present in the ocean‐going shipping industry make the ability to forecast freight rates and cycles extremely important for business decisions. Data‐driven model selection based on data characteristics is performed through ARIMA, fractional ARIMA (FARIMA), and ARIMA and FARIMA models with GARCH and EGARCH errors. The corresponding machine learning techniques utilized are feed‐forward fully connected artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Among others, FARIMA and MARS models are used for the first time in forecasting the BDI. Diebold–Mariano tests reveal that time series and machine learning approaches have comparable performance. However, combinations of forecasts of the selected models lead to better forecasting accuracy overall. Bai and Perron tests are utilized to check the robustness of the results over different cycles through the detection of breakpoints in the series.

Suggested Citation

  • Christos Katris & Manolis G. Kavussanos, 2021. "Time series forecasting methods for the Baltic dry index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1540-1565, December.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:8:p:1540-1565
    DOI: 10.1002/for.2780
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    as
    1. Nash, John C., 2014. "On Best Practice Optimization Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i02).
    2. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    3. André A P Santos & Luciano N Junkes & Floriano C M Pires Jr, 2014. "Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(1), pages 72-91, March.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Batchelor, Roy & Alizadeh, Amir & Visvikis, Ilias, 2007. "Forecasting spot and forward prices in the international freight market," International Journal of Forecasting, Elsevier, vol. 23(1), pages 101-114.
    6. Manolis G. Kavussanos & Ilias D. Visvikis (ed.), 2016. "The International Handbook of Shipping Finance," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-46546-7, March.
    7. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    8. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    9. Yordan Leonov & Ventsislav Nikolov, 2012. "A wavelet and neural network model for the prediction of dry bulk shipping indices," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(3), pages 319-333, September.
    10. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    11. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    12. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    13. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    14. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    15. Fotis Papailias & Dimitrios D. Thomakos & Jiadong Liu, 2017. "The Baltic Dry Index: cyclicalities, forecasting and hedging strategies," Empirical Economics, Springer, vol. 52(1), pages 255-282, February.
    16. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    17. Qingcheng Zeng & Chenrui Qu & Adolf K.Y. Ng & Xiaofeng Zhao, 2016. "A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(2), pages 192-210, June.
    18. Manolis Kavussanos & Nikos Nomikos, 2003. "Price Discovery, Causality and Forecasting in the Freight Futures Market," Review of Derivatives Research, Springer, vol. 6(3), pages 203-230, October.
    19. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    20. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    21. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    22. K P B Cullinane & K J Mason & M Cape, 1999. "A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 1(2), pages 15-39, December.
    23. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    24. Shun Chen & Hilde Meersman & Eddy van de Voorde, 2012. "Forecasting spot rates at main routes in the dry bulk market," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(4), pages 498-537, December.
    25. Benoit B. Mandelbrot, 1972. "Statistical Methodology for Nonperiodic Cycles: From the Covariance To R/S Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290, National Bureau of Economic Research, Inc.
    26. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    27. N.D. Geomelos & E. Xideas, 2014. "Forecasting spot prices in bulk shipping using multivariate and univariate models," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-37, December.
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    Cited by:

    1. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    2. 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.
    3. Abakah, Emmanuel Joel Aikins & Abdullah, Mohammad & Dankwah, Boakye & Lee, Chi-Chuan, 2024. "Asymmetric dynamics between the Baltic Dry Index and financial markets during major global economic events," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    4. Bangzhu Zhu & Jingyi Zhang & Chunzhuo Wan & Julien Chevallier & Ping Wang, 2023. "An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 741-755, July.
    5. Manolis Kavussanos & Siri Pettersen Strandenes & Helen Thanopoulou, 2022. "Special issue: ends of eras and new beginnings: twenty-first century challenges for shipping," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 347-367, June.
    6. 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.
    7. Georgios I. Papayiannis, 2022. "Static Hedging of Freight Risk under Model Uncertainty," Papers 2207.00862, arXiv.org.

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