Time-varying industry beta in Indian stock market and forecasting errors

Purpose – The purpose of this paper is to empirically estimate industry beta in Indian stock market with three alternative models and compare the accuracy of forecasting error to find the most suitable model for time-varying beta estimation. Design/methodology/approach – The paper applies the standard regression model, Kalman filter model, other statistical approaches and secondary material. Findings – The paper finds that the existence of dynamic beta in Indian market. The results also indicate systematic risk or beta of Indian industries is susceptible to the global economic effect. Finally, the Kalman filter generates the lower forecasting error compared to the other method for almost all the industries. Practical implications – The accurate estimation of beta which is a measure of systematic risk helps investors to make investment decision easier. The implication of this result is important for finance practitioners such as portfolio managers, investment advisors and security analysts. This study will help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India. Originality/value – This paper reliably estimate industry portfolio beta for India. The time-varying beta is estimated using Kalman filter method which is rarely applied in Indian literature. This paper contributes by extending the knowledge of existing literature by introducing a new data set with Indian data which is not affected by the “data snooping” bias. This study will also help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India.