The shipping index has the characteristics of violent fluctuation, so its volatility is difficult to predict. To better predict the volatility of the shipping market, this paper proposes an AR-SVR-GARCH model, which combines traditional time series analysis and modern machine learning methods. This model overcomes linear limitations of traditional methods. Meanwhile, this paper proposes 1another AR-SVR-GJR model which can explain the leverage effect. Empirical results show that the two models proposed in this paper have good volatility prediction ability in the dry bulk shipping market, the crude oil shipping market and the shipping stock market. This indicates that the proposed models have portability among different shipping markets. In addition, the AR-SVR-GARCH model and the AR-SVR-GJR model have stable volatility prediction performance in shipping markets during the financial crisis and in the recent time.
Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach
J. LIU ET AL.
MARITIME POLICY & MANAGEMENT
Maritime Policy & Management ; 49 , 6 ; 864-881
18.08.2022
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Transportation Research Record | 2018
|Trading volume and volatility in the shipping forward freight market
Online Contents | 2013
|Shipping market forecasting by forecast combination mechanism
Taylor & Francis Verlag | 2022
|Modelling and forecasting the demolition market in shipping
Taylor & Francis Verlag | 2016
|