IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/32572.html

An application of dynamic factor model to dry Bulk Market - focusing on the analysis of synchronicity and idiosyncrasy in the sub-markets with different ship - size

Author

Listed:
  • Ko, Byoung Wook

Abstract

BDI actually has weighed more on larger-size market. So, calculating the synchronicity of dry bulk sub-markets by using BDI as reference indicator could lead to mistake. Therefore, for the analysis of synchronicity and idiosyncrasy of dry bulk markets, this paper constructs a dynamic factor model of the change rate of BDI’s constituting indices and then it performs maximum likelihood estimation. One important finding is that, for such larger ships as Capesize and Panamax, there has been a significant increase in their synchronicity with global common factor after the 2008 global financial crisis, but for the other smaller ships, the opposite phenomenon has been observed. This paper suggests two important future research topics. One is extending the suggested dynamic factor model with the structural change (regime switching). The other is constructing a new index for the level, not the change rate, of the status of global dry bulk market. The author believes that the combination of these issues could produce an alternative index to BDI.

Suggested Citation

  • Ko, Byoung Wook, 2010. "An application of dynamic factor model to dry Bulk Market - focusing on the analysis of synchronicity and idiosyncrasy in the sub-markets with different ship - size," MPRA Paper 32572, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:32572
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/32572/1/MPRA_paper_32572.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shun Chen & Hilde Meersman & Eddy van de Voorde, 2010. "Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 12(1), pages 65-90, March.
    2. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    3. Amir H. Alizadeh & Nikos K. Nomikos, 2009. "Shipping Derivatives and Risk Management," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-23580-9, July.
    4. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    5. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    6. F. Javier Fernandez Macho & Andrew C. Harvey & James H. Stock, 1987. "Forecasting and Interpolation Using Vector Autoregressions with Common Trends," Annals of Economics and Statistics, GENES, issue 6-7, pages 279-287.
    7. repec:adr:anecst:y:1987:i:6-7:p:12 is not listed on IDEAS
    8. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ghiorghe Batrinca & Gianina Cojanu, 2013. "The Dynamics of the Dry Bulk Sub-Markets," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 3(6), pages 1-2, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    2. Mestekemper, Thomas & Windmann, Michael & Kauermann, Göran, 2010. "Functional hourly forecasting of water temperature," International Journal of Forecasting, Elsevier, vol. 26(4), pages 684-699, October.
    3. Byrne, Joseph P. & Davis, E. Philip, 2004. "Permanent and temporary inflation uncertainty and investment in the United States," Economics Letters, Elsevier, vol. 85(2), pages 271-277, November.
    4. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    5. Johannes Tang Kristensen, 2013. "Diffusion Indexes with Sparse Loadings," CREATES Research Papers 2013-22, Department of Economics and Business Economics, Aarhus University.
    6. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    7. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    8. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    9. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    10. Tsouknidis, Dimitris A., 2016. "Dynamic volatility spillovers across shipping freight markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 90-111.
    11. Chen, Shyh-Wei, 2011. "Current account deficits and sustainability: Evidence from the OECD countries," Economic Modelling, Elsevier, vol. 28(4), pages 1455-1464, July.
    12. Bai, Jushan & Wang, Peng, 2012. "Identification and estimation of dynamic factor models," MPRA Paper 38434, University Library of Munich, Germany.
    13. Petrevski, Goran & Exterkate, Peter & Tevdovski, Dragan & Bogoev, Jane, 2015. "The transmission of foreign shocks to South Eastern European economies: A Bayesian VAR approach," Economic Systems, Elsevier, vol. 39(4), pages 632-643.
    14. Ko, Byoung-Wook, 2018. "Dynamic patterns of dry bulk freight spot rates through the lens of a time-varying coefficient model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 319-330.
    15. Choi, In, 2012. "Efficient Estimation Of Factor Models," Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
    16. Smith, Ron P. & Zoega, Gylfi, 2008. "Global Factors, Unemployment Adjustment and the Natural Rate," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-29.
    17. Borus Jungbacker & Siem Jan Koopman, 2008. "Likelihood-based Analysis for Dynamic Factor Models," Tinbergen Institute Discussion Papers 08-007/4, Tinbergen Institute, revised 20 Mar 2014.
    18. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    19. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    20. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • F0 - International Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:32572. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.