Prediction of Baltic Capesize Freight Index Based on GARCH Model

Abstract:

This paper analyzed the statistical data of the Baltic Capesize Freight Index (BCI) and the daily return rate sequences to improve forecast reliability of the international dry bulk shipping market. It used the first-order logarithmic difference method to get the BCI daily return rate sequence, showing that the BCI daily return sequence had a leptokurtosis and fat-tail fluctuation feature and other features such as integration. To analyze volatility persistence, sensitivity and hysteresis of the sequence, a GARCH (1, 1) model was introduced. The GARCH (1, 1) model constructed the forecast method of BCI sequence, and it predicted the BCI daily return rate by optimizing lag phases. The logarithm sequences of BCI daily return rate finally reverted to BCI, which was forecasted at last. The conclusion is supposed to improve the international dry bulk shipping market forecasting method.

• Periodical: Applied Mechanics and Materials (Volumes 488-489)
• Pages: 1494-1497
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.488-489.1494
• Online since: January 2014
• Authors: Yong Hui Fan*, Yu Wei Xing, Hua Long Yang
• Keywords: Brain Computer Interface (BCI), GARCH Model, Prediction, Volatility

References

[1] Jiang Li. Capesize ships freight index prediction [D]. Dalian maritime university, (2010).

[2] Lu Ke-cong, from the Baltic capesize ship freight index fluctuation, journal of Shanghai maritime university [J], 2008, 29 (4) 29-33.

[3] Berg-Andreassen, J.A. The relation between period and spot rates in international maritime market[J]. Maritime Poliey & Management, 1997, 23(4): 381-395.

DOI: 10.1080/03088839700000042

[4] Veenstra A.W. and Franses, P.H. A co-integration approach to forecasting freight rates in the dry bulk shipping sector[J]. Transportation Researeh Part E, 1997, 31(6): 447-458.

DOI: 10.1016/s0965-8564(97)00002-5

[5] Manolis G. Kavussanos, Amir H. Alizadeh-M. Seasonality Patterns in Dry Bulk Shipping Spot and time Charter Freight Rates[J]. Transportation Researeh Part E, 2001, 35(6): 443-467.

DOI: 10.1016/s1366-5545(01)00004-7

[6] Zhang Xia. Capesize ships transportation market analysis and the management countermeasure research [D]. Dalian maritime university, (2010).

[7] Du Zhao-xi, li Yang, Jin Zhihong. Baltic dry freight index prediction and empirical analysis[J]. Journal of dalian maritime university(Social Sciences Edition), 2009, 8(1): 77-80.

[8] Nie Jin-long Li Xuying. The Baltic dry freight index of ARFIMA model research [J]. China water transport, 2009, 9 (4) : 57-58.