Abstract
A study of volatility behaviour of S&P BSE BANKEX return in India: A pragmatic approach using GARCH model
Highlights
The regression equation Y on X shows that every unit change in X that is SENSEX return there is 1.340233 units change in Y that is S&P Bombay Stock Exchange (BSE) Bankex return
Using GARCH (1, 1) model to check volatility of S & P Bankex return and the factors affecting the return of S&P BSE Bankex return in India
The researcher estimated mean and variance equation under the study. This model used generated by using Eq 1, residual have been examined by using correlogram of squared residual, Q-statistics at different lags and the application of LM-test and JarquaBera test for normally distributed residual gave the best-suited model under Normal distribution method
Summary
Financial modeling highlights the facts that the stock price or market movement exhibits certain major formalized facts such as volatility clustering, conditional Heteroscedasticity, asymmetric volatility effect, unconditional time-varying movement. The arch model commonly used in financial modeling of time series data that exhibits time varying volatility clustering. A variety of new and existing ARCH models are compared and estimated with daily Japanese stock return data to determine the shape of the News Impact Curve. A pragmatic approach using Garch (1, 1) model has been applied to test the volatility in return, by using residual or error term, the estimation of volatility is made at the macro level on four major indices around the world, namely S&P BSE SENSEX, NASDAQ composite, SSE composite Index, FTSE100. The fitted model is evaluated in term of its forecasting accuracy on these four indices
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