Skip to main content
SpringerLink
Log in

Determinants and international influences of the Chinese freight market

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

The past decades witness the impressive development of China. Along with the huge demand of China for commodities, the Chinese shipping market becomes more and more important. This paper investigates the determinants and the interactions of the Chinese shipping market with the international shipping market. We utilize the relatively large-dimensional Chinese commodities data as part of the controlled variables. The principal component analysis is applied to the commodities data to reduce the size of the data dimension. It is found that the controlled variables, including fuel cost, financial markets, and prices of large commodities, have the explanatory capability for the Chinese and the international shipping markets. There are mutual influences between the two shipping markets, with the Chinese market more sensitive to the movements of the international market. The Chinese shipping market has integrated into the international shipping market to some extent, instead of being isolated.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. We thank an anonymous referee for raising this point.

  2. Wind Information Co., Ltd., is headquartered in Shanghai, China. It is a leading service provider of financial data and information, especially for those related to China. More information could be found: https://www.wind.com.cn/En/aboutus.html#.

  3. We also evaluate by including the three components of nonferrous metals group. As reported in “Appendix,” the estimated coefficients for the third component are insignificant and the regression result is qualitatively the same as the case using two components.

  4. We are grateful for the suggestion of one of the anonymous reviewers.

References

  • Akaike H (1998) Information theory and an extension of the maximum likelihood principle. In: Selected papers of hirotugu akaike, Springer, pp 199–213

  • Alizadeh AH, Talley WK (2011) Microeconomic determinants of dry bulk shipping freight rates and contract times. Transportation 38(3):561–579

    Article  Google Scholar 

  • Angelopoulos J, Sahoo S, Visvikis ID (2020) Commodity and transportation economic market interactions revisited: new evidence from a dynamic factor model. Transp Res Part E Logist Transp Rev 133:101836

    Article  Google Scholar 

  • Beenstock M, Vergottis A (1989) An econometric model of the world market for dry cargo freight and shipping. Appl Econ 21(3):339–356

    Article  Google Scholar 

  • Bildirici ME, Kayıkçı F, Onat IŞ (2016) BDI, gold price and economic growth. Procedia Econ Finance 38:280–286

    Article  Google Scholar 

  • Chen P (2015) Global oil prices, macroeconomic fundamentals and China’s commodity sector comovements. Energy Policy 87:284–294

    Article  Google Scholar 

  • Chi J (2016) Exchange rate and transport cost sensitivities of bilateral freight flows between the US and China. Transp Res Part A Policy Pract 89:1–13

  • Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a):427–431

  • Drobetz W, Tegtmeier L (2013) The development of a performance index for KG funds and a comparison with other shipping-related indices. Maritime Econ Logist 15(1):32–71

    Article  Google Scholar 

  • Drobetz W, Richter T, Wambach M (2012) Dynamics of time-varying volatility in the dry bulk and tanker freight markets. Appl Financial Econ 22(16):1367–1384

    Article  Google Scholar 

  • Dungey M, Fry-McKibbin R, Linehan V (2014) Chinese resource demand and the natural resource supplier. Appl Econ 46(2):167–178

  • Erdogan O, Tata K, Karahasan BC, Sengoz MH (2013) Dynamics of the co-movement between stock and maritime markets. Int Rev Econ Finance 25:282–290

    Article  Google Scholar 

  • Fernández A, González A, Rodríguez D (2018) Sharing a ride on the commodities roller coaster: common factors in business cycles of emerging economies. J Int Econ 111:99–121

    Article  Google Scholar 

  • Gavriilidis K, Kambouroudis DS, Tsakou K, Tsouknidis DA (2018) Volatility forecasting across tanker freight rates: the role of oil price shocks. Transp Res Part E Logist Transp Rev 118:376–391

  • Giannarakis G, Lemonakis C, Sormas A, Georganakis C et al (2017) The effect of Baltic Dry Index, gold, oil and USA trade balance on Dow Jones sustainability index world. Int J Econ Financial Issues 7(5):155–160

    Google Scholar 

  • Gu Y, Chen Z, Lien D (2019) Baltic Dry Index and iron ore spot market: dynamics and interactions. Appl Econ 51(35):3855–3863

  • Gu Y, Chen Z, Lien D, Luo M (2020) Quantile hedge ratio for forward freight market. Transp Res Part E Logist Transp Rev 138:101931

  • Kavussanos MG, Visvikis ID, Dimitrakopoulos DN (2014) Economic spillovers between related derivatives markets: the case of commodity and freight markets. Transp Res Part E Logist Transp Rev 68:79–102

    Article  Google Scholar 

  • Kim H (2011) Study about how the Chinese economic status affects to the Baltic Dry Index. Int J Bus Manag 6(3):116–123

    Google Scholar 

  • Lim KG, Nomikos NK, Yap N (2019) Understanding the fundamentals of freight markets volatility. Transp Res Part E Logist Transp Rev 130:1–15

    Article  Google Scholar 

  • Lin F, Sim NCS (2013) Trade, income and the Baltic Dry Index. Eur Econ Rev 59:1–18

  • Lombardi MJ, Osbat C, Schnatz B (2012) Global commodity cycles and linkages: a FAVAR approach. Empir Econ 43(2):651–670

    Article  Google Scholar 

  • Michail NA, Melas KD (2021) Market interactions between agricultural commodities and the dry bulk shipping market. Asian J Ship Logist 37(1):73–81

    Article  Google Scholar 

  • Papailias F, Thomakos DD, Liu J (2017) The Baltic Dry Index: cyclicalities, forecasting and hedging strategies. Empir Econ 52(1):255–282

  • Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75(2):335–346

  • Ruan Q, Wang Y, Lu X, Qin J (2016) Cross-correlations between Baltic Dry Index and crude oil prices. Physica A 453:278–289

    Article  Google Scholar 

  • Sartorius K, Sartorius B, Zuccollo D (2018) Does the Baltic Dry Index predict economic activity in South Africa? A review from 1985 to 2016. S Afr J Econ Manag Sci 21(1):1–9

    Article  Google Scholar 

  • Sims C (1980) Macroeconomics and reality. Econometrica 48(1):1–48

    Article  Google Scholar 

  • Sun X, Liu C, Wang J, Li J (2020) Assessing the extreme risk spillovers of international commodities on maritime markets: a GARCH-Copula-CoVaR approach. Int Rev Financial Anal 68:101453

    Article  Google Scholar 

  • Tsioumas V, Papadimitriou S (2018) The dynamic relationship between freight markets and commodity prices revealed. Maritime Econ Logist 20(2):267–279

    Article  Google Scholar 

  • United Nations Conference on Trade and Development, (2018) “Trade and Development Report”

Download references

Acknowledgements

This research receives funding support from the Major Project of National Social Science Foundation, China (Grant No. 19ZDA093).

Author information

Authors and Affiliations

  1. Department of Electronic Business, South China University of Technology, Guangzhou, China

    Yimiao Gu

  2. School of Economics and Finance, South China University of Technology, Guangzhou, China

    Zhenxi Chen

  3. Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore

    Qingyang Gu

Authors
  1. Yimiao Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenxi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingyang Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenxi Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In the main text, the first two components of nonferrous metal are used in the regression. Here, we evaluate using the first three components which explains more than 75% of the total variance of the price growth rates of the nonferrous metal group. The estimation result is reported in Table 8. Compared to the results reported in Table 5 in the main text, including the third principal component, \(metal\_3\), does not change the result. All the estimates are qualitatively the same. Coefficients for the third component are all insignificant.

Table 8 VAR estimation results with three components of nonferrous metal group included
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gu, Y., Chen, Z. & Gu, Q. Determinants and international influences of the Chinese freight market. Empir Econ 62, 2601–2618 (2022). https://doi.org/10.1007/s00181-021-02089-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-021-02089-1

Keywords

JEL Classification

Search

Navigation