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New Evidence on the Information and Predictive Content of the Baltic Dry Index

Author

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  • Nicholas Apergis

    (Department of Banking and Financial Management, University of Piraeus, 80 Karaoli & Dimitriou, Piraeus 18534, Greece)

  • James E. Payne

    (Department of Economics and Finance, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148, USA)

Abstract

This empirical study analyzes the information and predictive content of the Baltic Dry Index (BDI) with respect to a range of financial assets and the macroeconomy. By using panel methodological approaches and daily data spanning the period 1985–2012, the empirical analysis documents the joint predictability capacity of the BDI for both financial assets and industrial production. The results reveal the role of the BDI in predicting the future course of the real economy, yielding a link between financial asset markets and the macroeconomy.

Suggested Citation

  • Nicholas Apergis & James E. Payne, 2013. "New Evidence on the Information and Predictive Content of the Baltic Dry Index," IJFS, MDPI, vol. 1(3), pages 1-19, July.
    • Handle: RePEc:gam:jijfss:v:1:y:2013:i:3:p:62-80:d:27458
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    2. Batrinca Ghiorghe & Cojanu Gianina & Surugiu Ioana, 2013. "Application of autoregressive models for forecasting the Baltic Exchange Dry Index," Constanta Maritime University Annals, Constanta Maritime University, vol. 20(2), pages 205-208.
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    4. Miao Su & Keun Sik Park & Sung Hoon Bae, 2024. "A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 21-43, March.
    5. Han, Liyan & Jin, Jiayu & Wu, Lei & Zeng, Hongchao, 2020. "The volatility linkage between energy and agricultural futures markets with external shocks," International Review of Financial Analysis, Elsevier, vol. 68(C).
    6. Sheng, Xin & Kim, Won Joong & Gupta, Rangan & Ji, Qiang, 2023. "The impacts of oil price volatility on financial stress: Is the COVID-19 period different?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 520-532.
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    8. Anderl, Christina & Caporale, Guglielmo Maria, 2024. "Shipping cost uncertainty, endogenous regime switching and the global drivers of inflation," International Economics, Elsevier, vol. 178(C).
    9. Graham, Michael & Peltomäki, Jarkko & Piljak, Vanja, 2016. "Global economic activity as an explicator of emerging market equity returns," Research in International Business and Finance, Elsevier, vol. 36(C), pages 424-435.
    10. Adewuyi, Adeolu O. & Adeleke, Musefiu A. & Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel, 2023. "Dynamic linkages between shipping and commodity markets: Evidence from a novel asymmetric time-frequency method," Resources Policy, Elsevier, vol. 83(C).
    11. Cai, Wenxue & Liang, Fenfen & Wan, Yanchun & Zhong, Huiling & Gu, Yimiao, 2021. "An innovative approach for constructing a shipping index based on dynamic weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
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    13. Arunava Bandyopadhyay & Prabina Rajib, 2023. "The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 217-237, June.
    14. Pao-Lan Kuo & Chien-Liang Chiu & Chan-Sheng Chen & Mei-Chih Wang, 2020. "The Dynamic Relationships between the Baltic Dry Index and the BRICS Stock Markets: A Wavelet Analysis," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(3), pages 340-351, March.
    15. Anderl, Christina & Caporale, Guglielmo Maria, 2024. "Shipping cost uncertainty, endogenous regime switching and the global drivers of inflation," International Economics, Elsevier, vol. 178(C).
    16. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.
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