Abstract

Crude oil is a crucial component of India’s energy basket after coal. The increasing demand for crude oil in India is met through imports. Crude oil price changes affect the social stability, economic development, and national security of the country. Therefore, it is crucial to devise suitable methods to forecast crude oil price movements accurately.Thus, the purpose of this study is to evaluate the forecasting performance of linear and non-linear time series models. In the study Box Jenkins methodology is used to obtain a best fit ARIMA and GARCH type models and further use it to forecast the crude oil (Brent) prices. The study shows that the crude oil price series is volatile over the time trend and therefore uses the GARCH class models as well which are capable of capturing volatility clustering typical of oil price series. Performance of ARIMA & GARCH class modes is then compared to find out which model better forecasts the crude oil prices. Indian economy being vulnerable to volatility in the international crude oil market requires a methodology to accurately forecast the price volatility and therefore to fill this gap this study for forecasting and studying the behavior of crude oil price series was conducted.

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