The textile sector in India is the oldest manufacturing sector. As the raw materials for this sector are sourced from the petrochemical industries, the earnings of Indian textile companies are dependent on the crude oil price. The crude price in the international market has become more volatile and hence, the equity price of Indian textile companies has become more volatile. This study aims to develop two price risk management strategies for Indian textile equities. Using the vector autoregressive (VAR) model, a price forecast model, further the possibility of cross hedge for textile equities with the help of crude futures is examined using the Granger causality test and Pearson correlation statistics. The results of the study showed that crude futures price in India is one of the price determinants of textile industry stock prices.

Various rubber products are used in the textile industry. Due to increased foreign supply and synthetic rubber production, the price of natural Rubber in India has become more volatile. This paper aims to develop an appropriate model to predict the weekly price using the Box Jenkins methodology. The weekly price for Indian RSS-1 Rubber for the sample period from January 2002 to December 2019 has been collected from the official website of the Indian Rubber Board. ACF and PACF correlograms check the series stationarity and identify the model parameters. A model with the maximum number of significant coefficients, lowest volatility, lowest Akaike's information criterion (AIC), lowest Schwarz criterion and highest Adjusted R-squared is tentatively selected as the appropriate model and for the same model diagnostic check is carried out. An appropriate model to forecast the weekly price for the RSS-1 variety of Rubber is ARIMA (1, 1, 4).

This study aimed to analyse the normal and abnormal loss of a jeans manufacturing company in India. Personal interview and observation method are used in this study. Abnormal loss in quantity and rupee value is computed for 40 days of production based on the observed data. Mean abnormal losses are computed and one sample t-test is applied to test the hypotheses that the mean abnormal loss is not equal to zero. The study revealed that a normal loss of 3 to 5% is expected in any garment manufacturing company due to loss during the cutting and shrinkage process. The p-values of one sample t-test were less than 0.05 for all the tested hypotheses, hence, all the null hypotheses (H01 to H05 mean abnormal losses equal to zero) were rejected. Further, it was found that fabric is the big contributor in terms of abnormal loss. Hence, proper training for workers and recruiting of trained workers are advised to reduce abnormal losses