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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

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  • 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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
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    7. Amir H. Alizadeh & Nikos K. Nomikos, 2009. "Shipping Derivatives and Risk Management," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-23580-9, October.
    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, April.
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    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.

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    More about this item

    Keywords

    Dynamic Factor Model; Synchronicity and Idiosyncrasy; Structural Change;
    All these 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

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