The Baltic Dry Index: Cyclicalities, Forecasting and Hedging Strategies
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- Fotis Papailias & Dimitrios D. Thomakos & Jiadong Liu, 2017. "The Baltic Dry Index: cyclicalities, forecasting and hedging strategies," Empirical Economics, Springer, vol. 52(1), pages 255-282, February.
References listed on IDEAS
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Citations
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Cited by:
- Zaili Yang & Esin Erol Mehmed, 2019. "Artificial neural networks in freight rate forecasting," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 390-414, September.
- Arthur J. Lin & Hai-Yen Chang, 2020. "Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
- Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
- Ding, Qian & Huang, Jianbai & Chen, Jinyu, 2021. "Dynamic and frequency-domain risk spillovers among oil, gold, and foreign exchange markets: Evidence from implied volatility," Energy Economics, Elsevier, vol. 102(C).
- 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.
- 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.
- Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
- Gu, Bingmei & Liu, Jiaguo, 2022. "Determinants of dry bulk shipping freight rates: Considering Chinese manufacturing industry and economic policy uncertainty," Transport Policy, Elsevier, vol. 129(C), pages 66-77.
- Jason Angelopoulos, 2017. "Time–frequency analysis of the Baltic Dry Index," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(2), pages 211-233, June.
- Elie Bouri & Rangan Gupta & Luca Rossini, 2022. "The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index," Working Papers 202229, University of Pretoria, Department of Economics.
- Yimiao Gu & Zhenxi Chen & Qingyang Gu, 2022. "Determinants and international influences of the Chinese freight market," Empirical Economics, Springer, vol. 62(5), pages 2601-2618, May.
- 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).
- Christos Katris & Manolis G. Kavussanos, 2021. "Time series forecasting methods for the Baltic dry index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1540-1565, December.
- 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.
- 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.
- Kyriazi, Foteini & Thomakos, Dimitrios D. & Guerard, John B., 2019. "Adaptive learning forecasting, with applications in forecasting agricultural prices," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1356-1369.
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More about this item
Keywords
Baltic Dry Index; Commodities; Concordance; Cyclical Analysis; Forecasting; Hedging; Turning Points;All these keywords.
JEL classification:
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-FOR-2014-01-10 (Forecasting)
- NEP-RMG-2014-01-10 (Risk Management)
Statistics
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