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Forecasting crude oil price volatility in India using a hybrid ANN-GARCH model

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  • Sujoy Bhattacharya
  • Arshad Ahmed

Abstract

In this paper the volatility forecasts of crude oil commodity price returns are analysed. Various GARCH family models are used to forecast the volatility and the output in terms of return vectors of these models are used as inputs for a neural network. The return forecasting performance of the GARCH family models are compared with GARCH-ANN models using root mean square error as the criteria. The results show that the hybrid model of ANN and EGARCH gives the best performance. An explanatory variable, the exchange rate between Indian Rupee and Saudi Arabia Riyal is used as input for the neural network model for the second scenario and using the same criteria of root mean square error, it is observed to have no improvement over the previous ANN-GARCH models.

Suggested Citation

  • Sujoy Bhattacharya & Arshad Ahmed, 2018. "Forecasting crude oil price volatility in India using a hybrid ANN-GARCH model," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 4(4), pages 446-457.
    • Handle: RePEc:ids:ijbfmi:v:4:y:2018:i:4:p:446-457
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    Cited by:

    1. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    2. Shuyu Li & Xuan Yang & Rongrong Li, 2019. "Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
    3. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.

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