We examined and compared forecasting ability of GARCH and Stochastic Volatility models represented in the state space form using Kalman Filter as an estimator for the models. The models are applied in the context of Indian stock market. For estimation purpose, daily values of Sensex form Bombay Stock Exchange (BSE) are used as the input. The results confirmed the volatility forecasting capabilities of both models. Finally, we interpreted that which model performs better in the out-of-sample forecast for
h-step ahead forecast. Forecast errors of the volatility were found in favour of SV model for a 30-day ahead forecast. This also shows that Kalman filter can be used for better estimates and forecasts of the volatility using state space models. Finally the numerical results make evident the effectiveness and relevance of the proposed state space estimation.
Keywords: GARCH; Kalman Filter; State Space; Stochastic Volatility
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