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Crude Oil Spot Price Forecasting Using Ivanov-Based LASSO Vector Autoregression

Author

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  • Yishan Ding
  • Dongwei He
  • Jun Wu
  • Xiang Xu
  • Fanlin Meng

Abstract

This paper proposes a forecasting methodology that investigates a set of different sparse structures for the vector autoregression (VAR) model using the Ivanov-based least absolute shrinkage and selection operator (LASSO) framework. The variant auxiliary problem principle method is used to solve the various Ivanov-based LASSO-VAR variants, which is supported by parallel computing with simple closed-form iteration and linear convergence rate. A test case with ten crude oil spot prices is used to demonstrate the improvement in forecasting skills gained from exploring sparse structures. The proposed method outperformed the conventional vector autoregressive model.

Suggested Citation

  • Yishan Ding & Dongwei He & Jun Wu & Xiang Xu & Fanlin Meng, 2022. "Crude Oil Spot Price Forecasting Using Ivanov-Based LASSO Vector Autoregression," Complexity, Hindawi, vol. 2022, pages 1-10, November.
    • Handle: RePEc:hin:complx:5011174
    DOI: 10.1155/2022/5011174
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    Cited by:

    1. Hasnain Iftikhar & Aimel Zafar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models," Mathematics, MDPI, vol. 11(16), pages 1-19, August.

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