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

Authors: Sujoy Bhattacharya; Arshad Ahmed

Addresses: Vinod Gupta School of Management, IIT Kharagpur, West Bengal 721302, India ' Vinod Gupta School of Management, IIT Kharagpur, West Bengal 721302, India

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.

Keywords: GARCH models; ANN-GARCH; crude oil; volatility forecasting; India.

DOI: 10.1504/IJBFMI.2018.095154

International Journal of Business Forecasting and Marketing Intelligence, 2018 Vol.4 No.4, pp.446 - 457

Received: 06 Jan 2018
Accepted: 11 Feb 2018
Published online: 01 Oct 2018 *

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