Forecasting Butyl Price: A Case of India's Tire Industry

Abstract

In this work, a modest attempt was taken to forecast Butyl prices in setting market prices of tires more accurately because it is an important issue for managers of firms in the tire industry. In this context, two types of models were taken into consideration on the basis of sources of information (i.e. analogous time series or conventional time series). A linear regression model and a Box – Jenkins autoregressive integrated moving average (ARIMA) model were fitted to data on Exxon Butyl price (EBP), Russian Butyl price (RBP), and Crude oil price (RBRTE). Results showed that the ARIMA model was superior to the regression model in case of predicting Butyl prices. Marketing practitioners will benefit from the findings of this work in various aspects such as in setting tire prices more precisely. Moreover, the findings should assist managers in managing inventory costs more accurately. This study showed how a significant improvement can be achieved at a much lower cost and with a much lesser effort for forecasting Butyl prices in case of the tire industry.