Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach
Author ORCID Identifier
Wenlian Gao: https://orcid.org/0000-0003-0795-9890
Publication Title
Maritime Policy and Management
ISSN
03088839
E-ISSN
14645254
Document Type
Article
Abstract
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.
First Page
864
Last Page
881
Publication Date
1-1-2022
DOI
10.1080/03088839.2021.1898689
Keywords
ar-svr-garch model, BDI, crude oil shipping market, volatility forecasting
Recommended Citation
Jiaguo Liu, Zhouzhi Li, Hao Sun, Lean Yu & Wenlian Gao (2022) Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach, Maritime Policy & Management, 49:6, 864-881, DOI: 10.1080/03088839.2021.1898689
Original Citation
Jiaguo Liu, Zhouzhi Li, Hao Sun, Lean Yu & Wenlian Gao (2022) Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach, Maritime Policy & Management, 49:6, 864-881, DOI: 10.1080/03088839.2021.1898689
Department
Department of Finance
