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Volatility forecasting for the shipping market indexes: an AR-SVR-GARCH approach

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Listed:
  • Jiaguo Liu
  • Zhouzhi Li
  • Hao Sun
  • Lean Yu
  • Wenlian Gao

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.

Suggested 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, Taylor & Francis Journals, vol. 49(6), pages 864-881, August.
    • Handle: RePEc:taf:marpmg:v:49:y:2022:i:6:p:864-881
    DOI: 10.1080/03088839.2021.1898689
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    Cited by:

    1. Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).

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