Maritime Economics & Logistics

, Volume 14, Issue 3, pp 319–333 | Cite as

A wavelet and neural network model for the prediction of dry bulk shipping indices

  • Yordan Leonov
  • Ventsislav Nikolov
Original Article


Shipping markets are typically volatile in nature, as manifest in the dynamics of freight rates. According to shipowners’ risk propensity, volatility-related decisions vary from what kind of contract (time/voyage charter) to engage in to whether to enter/exit the business. After a sharp drop in freight rates at the end of 2008, a discussion about appropriate risk management concepts and statistical tools is needed. Owing to the magnitude of investment required in shipping, any additional information regarding the future direction of market volatility is of the utmost importance. The ambition of this article is exactly the same: to study fluctuations in the freight rates of the Baltic Panamax route 2A and the Baltic Panamax route 3A, using a tool of analysis that is new to shipping economics: a hybrid model of wavelets and neural networks. The wavelet multiscale decomposition of time series reveals volatility dynamics across different time frequencies and will uncover patterns that will be used by neural networks for prediction.


dry bulk shipping wavelet neural network 


  1. Alizadeh, A.H. and Nomikos, N.K. (2009) Shipping Derivatives and Risk Management in Shipping. London: Palgrave Macmillan.CrossRefGoogle Scholar
  2. Bollerslev, T. (1986) Generalised autoregressive conditional heteroskedastic. Journal of Econometrics 31 (3): 307–327.CrossRefGoogle Scholar
  3. Chatfield, C. (1996) The Analysis of Time Series. An Introduction, 5th edn. London: Chapman & Hall/CRC.Google Scholar
  4. Crowley, P. (2005) An Intuitive Guide to Wavelets for Economists. Bank of Finland Research Discussion Papers 1.Google Scholar
  5. Cullinane, K.P.B. (1995) A portfolio analysis of market investments in dry bulk shipping. Transportation Research B: Methodology 29B (3): 181–200.CrossRefGoogle Scholar
  6. Daubechies, I. (1992) Ten Lectures on Wavelets. Philadelphia, PA: SIAM.CrossRefGoogle Scholar
  7. Fausett, L. (1994) Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  8. Gencay, R., Selcuk, R. and Whitcher, B. (2001) An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. San Diego, CA: Academic Press.Google Scholar
  9. Greene, W.H. (2002) Econometric Analysis, 5th edn. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  10. Kavussanos, M.G. (1996a) Comparison of volatility in the dry-cargo ship-sector. Journal of Transport Economics and Policy 30 (1): 67–82.Google Scholar
  11. Kavussanos, M.G. (1996b) Price risk modelling of different size vessels in the tanker industry. Transportation Research E: Logistics and Transportation Review 32 (2): 161–176.Google Scholar
  12. Mallat, S.G. (1999) Theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7): 674–693.CrossRefGoogle Scholar
  13. Mandelbrot, B. (1963) The variation of certain speculative prices. Journal of Business XXXVI: 392–417.Google Scholar
  14. Nomikos, N., Alizadeh, A. and van, Dellan S. (2009) An investigation into the correct specification for volatility in the shipping freight rate markets, paper presented at the IAME Annual Conference; 25 June, Copenhagen.Google Scholar
  15. Percival, D.B. and Walden, A.T. (2000) Wavelet Methods for Time Series Analysis. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  16. Ramsey, J.B. (1999) The contribution of wavelets to the analysis of economic and financial data. Philosophical Transactions of the Royal Society 357 (1760): 2593–2602.CrossRefGoogle Scholar
  17. Taleb, N.N. (2007) Black swans and the domains of statistics. The American Statistician 61 (3): 198–200.CrossRefGoogle Scholar
  18. Tang, Z. and Fishwick, P.A. (1993) Feed-forward neural nets as models for time series forecasting. ORSA Journal on Computing 5 (4): 374–385.CrossRefGoogle Scholar
  19. Zhang, G.P. (2004) Neural Networks in Business Forecasting. Hershey, PA: Idea Group Publishing.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2012

Authors and Affiliations

  • Yordan Leonov
    • 1
  • Ventsislav Nikolov
    • 2
  1. 1.Department of NavigationTransport Management and Waterways Preservation, Technical University of VarnaVarnaBulgaria
  2. 2.Department of Computer Science and EngineeringTechnical University of VarnaVarnaBulgaria

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