Modelling and Forecasting of Stock Price Volatility – an Analysis

K. Kannan, Dr. S. Balamurugan

Price Volatility; Modelling and Forecasting; NIFTY 50; GARCH Family Models

The current study uses the daily adjusted closing price for the period from 1 April 2011 to 31 March 2021 to model and forecast the price volatility of the NIFTY 50 companies listed on the Indian stock market using the GARCH family of models. Analytical research design is the method employed for the study. Purposive sampling was employed for the study’s sample design, and the researcher chose one significant sector from the NIFTY 50 index that was listed on March 31, 2021. According to sector weightage, key sectors like financial services were chosen. In this study, several combinations of GARCH and ARCH lags were utilised, and high-order models were generally examined to determine which model was most appropriate.The study took into account the forecasting models Generalized Autoregressive Conditional Heteroscedasticity-symmetric GARCH (2,1), Exponential GARCH-EGARCH (2,1), and Threshold GARCH-TGARCH (2,1). Heteroscedasticity tests, such as the Lagrange Multiplier (LM) test for ARCH, were employed to determine the presence of heteroscedasticity in the return series’ residual in order to observe the ARCH effect. We can employ ARCH/GARCH models if the ARCH effect is present. To determine whether the return series are stationary, tests for stationarity such the Augmented Dicky-Fuller test were done. To determine if the return series were normally distributed, tests for normality such as the Jarque-Bera test were applied.Using the Mean Absolute Error and Root Mean Square Error error statistics, the effectiveness of these GARCH models was assessed. TGARCH scored well based on these evaluations and it aids in capturing the leverage impact, volatility clustering, forecasting accuracy, and differentiating the asymmetric influence between good and negative news.

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