Forecasting Volatility of Crude oil Prices using Box-Jenkins’s Autoregressive Moving Average: Evidence from Indian Chemical Industry
Rakesh Kumar Sharma
Rakesh Kumar Sharma , School of Humanities and Social Sciences, Thapar Institute of Engineering and technology (Deemed University), Patiala, India.
Manuscript received on 8 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 60-68 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3876098319/19©BEIESP | DOI: 10.35940/ijrte.C3876.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The current paper deals with to forecast volatility in crude oil prices in Indian economy. In the current study volatility is measured through change in monthly crude oil prices per barrel. The monthly data of crude oil price have taken from January 1995 to May, 2017. The different unit root tests are applied to test check change in crude oil price series is stationary or non stationary. Box-Jenkins’s Autoregressive Moving Average of Box-Jenkins methodology has been used for developing a forecasting model. Minimum Akaike Information Criteria (AIC) has been opted to arrive at fit good ARMA model. According to this criteria (4, 3)(0,0) was observed as one of the best model to predict the volatility in future crude oil prices. Forecasted volatility in prices may be utilized for calculating future spot price and hedging future risk. Moreover, forecasted prices volatility of crude oil will also beneficial to oil companies, policy makers for formulating different economic policies and taking some crucial economic decision.
Key words: Akaike Information Criteria, Augmented Dickey Fuller, Philips Perron Test. JEL Classification: C22, C82, C53
Scope of the Article: Classification