Testing Predictability and Nonlinear Dependence in the Indian Stock Market

This paper suggests a systematic approach to studying predictability and nonlinear dependence in the context of the Indian stock market, one of the most important emerging stock markets in the world. The proposed approach considers nonlinear dependence in returns and envisages appropriate specification of both the conditional first- and second-order moments, so that final conclusions are free from any probable statistical consequences of misspecification. To this end, a number of rigorous tests are applied on the returns, based on four major daily indices of the Indian stock market. It is found that the Indian stock market is predictable, and this observed lack of efficiency is due to serial correlation, nonlinear dependence, day-of-the week effects, parameter instability, conditional heteroskedasticity (GARCH), daily-level seasonality in volatility, the short-term interest rate (in some subperiods of some indices), and some dynamics in the higher-order moments.