Skip to main content
SpringerLink
Log in

Forecasting Tanker Freight Rate

  • Conference paper
  • First Online:
Operations Management for Social Good (POMS 2018)

Part of the book series: Springer Proceedings in Business and Economics ((SPBE))

  • 991 Accesses

Abstract

The practiced freight rates have a great impact on the international trade of crude oil and oil products. This paper aims to verify the performance of dynamic regression models in short-term maritime freight forecasts in the spot market of a crude oil export route.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baltic Exchange. Available at https://www.balticexchange.com/ (2018). Accessed in June, 2018

  2. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control, 5 edn. Wiley

    Google Scholar 

  3. Clarksons: Shipping Intelligence Network. Londres. Available at https://sin.clarksons.net/ (2018). Accessed in June, 2018

  4. Eslami, P., Jung, K., Lee, D., Tjolleng, A.: Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm. Marit. Econ. Logistics 19(3), 538–550 (2017)

    Article  Google Scholar 

  5. Geomelos, N.D., Xideas, E.: Forecasting spot prices in bulk shipping using multivariate and univariate models. Cogent Econ. & Finan. 2(1) (2014)

    Google Scholar 

  6. Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice. Available at https://otexts.org/fpp/ (2014). Accessed in May, 2018

  7. Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice. Available at https://otexts.org/fpp2/ (2016). Accessed in May, 2018

  8. Ji, M.-J., Zhang, H.-Y., Wang, Q.-B.: Analysis and forecasting of the tanker freight rates based on combined forecasting model. J. Transp. Syst. Eng. Inf. Technol. 12(1), 199–204 (2012)

    Google Scholar 

  9. Li, J., Parsons, M.: Forecasting tanker freight rate using neural networks. Marit. Policy Manage. 24(1), 9–30 (1997)

    Article  Google Scholar 

  10. Santos, A.A.P., Junkes, L.N., Pires Jr., F.C.M.: Forecasting period charter rates of VLCC tankers through neural networks: a comparison of alternative approaches. Marit. Econ. Logistics 16(1), 72–91 (2014)

    Article  Google Scholar 

  11. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    Article  Google Scholar 

  12. Souza, R.C., Camargo, M.E.: Análise e Previsão de Séries Temporais: os Modelos ARIMA, 2 edn. Rio de Janeiro (2004)

    Google Scholar 

  13. Stellwagen, E., Goodrich, R.: Forecast Pro Statistical Reference Manual. Business Forecast Systems, Belmont (2011)

    Google Scholar 

  14. Worldscale. Available at https://www.worldscale.co.uk/bookpage/preamble (2018). Accessed in April, 2018

  15. Zanini, A.: Redes neurais e regressão dinâmica: um modelo híbrido para previsão de curto prazo da demanda de gasolina automotiva do Brasil. Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro (2000)

    Google Scholar 

  16. Zhang, J., Zeng, Q.: Modelling the volatility of the tanker freight market based on improved empirical mode decomposition. Appl. Econ. 49(17), 1655–1667 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. PETROBRAS–Petroleo Brasileiro S.A., Av. Henrique Valadares, 28 Centro, Rio de Janeiro, RJ, 20031-030, Brazil

    Rodrigo Ferreira Bertoloto

  2. Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22451-900, Brazil

    Rodrigo Ferreira Bertoloto & Fernando Luiz Cyrino Oliveira

Authors
  1. Rodrigo Ferreira Bertoloto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Luiz Cyrino Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rodrigo Ferreira Bertoloto .

Editor information

Editors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

    Adriana Leiras

  2. Universidad Nacional de Colombia at Medellin (UNAL-Medellin), Medellín, Colombia

    Carlos Alberto González-Calderón

  3. Universidade Estadual Paulista at São José dos Campos (ICT-UNESP), São Paulo, Brazil

    Irineu de Brito Junior

  4. Universidad de Los Andes (UniAndes), Bogotá, Colombia

    Sebastián Villa

  5. Universidade de São Paulo (USP), São Paulo, Brazil

    Hugo Tsugunobu Yoshida Yoshizaki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bertoloto, R.F., Oliveira, F.L.C. (2020). Forecasting Tanker Freight Rate. In: Leiras, A., González-Calderón, C., de Brito Junior, I., Villa, S., Yoshizaki, H. (eds) Operations Management for Social Good. POMS 2018. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-23816-2_39

Download citation

Publish with us

Policies and ethics

Search

Navigation