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An approach for Baltic Dry Index analysis based on empirical mode decomposition

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  • Qingcheng Zeng
  • Chenrui Qu

Abstract

The bulk shipping market is seasonal, cyclical and highly volatile. Due to the nonstationary and nonlinear nature of price series and the complexity of influencing factors, it is difficult to analyse the fluctuations in the bulk shipping market. In this study, a method based on empirical mode decomposition (EMD) is proposed to investigate the volatility of the Baltic Dry Index (BDI). In this method, the original freight price series is decomposed into several independent intrinsic modes, using EMD first. Then, the intrinsic modes are composed into three components: short-term fluctuations caused by normal market activities, the effect of extreme events and a long-term trend. Numerical experiments indicate that the proposed method can effectively reveal the characteristics of bulk freight price series with different economic meanings and decrease error accumulation. Meanwhile, by decomposition of intrinsic modes, the complexity of the model formulation can be controlled and the operability of the model can be improved.

Suggested Citation

  • Qingcheng Zeng & Chenrui Qu, 2014. "An approach for Baltic Dry Index analysis based on empirical mode decomposition," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(3), pages 224-240, May.
    • Handle: RePEc:taf:marpmg:v:41:y:2014:i:3:p:224-240
    DOI: 10.1080/03088839.2013.839512
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    Cited by:

    1. Liquan Guo & Zhongzhen Yang, 2019. "Relationship Between Shipping Accessibility and Maritime Transport Demand: the Case of Mainland China," Networks and Spatial Economics, Springer, vol. 19(1), pages 149-175, March.
    2. Melike Bildirici & Işıl Şahin Onat & Özgür Ömer Ersin, 2023. "Forecasting BDI Sea Freight Shipment Cost, VIX Investor Sentiment and MSCI Global Stock Market Indicator Indices: LSTAR-GARCH and LSTAR-APGARCH Models," Mathematics, MDPI, vol. 11(5), pages 1-27, March.
    3. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    4. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.

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