Modelling and Forecasting Volatility of Equity Market Indices And Gold Using Univariate GARCH Models: Evidence from SingaporeTools Kyaw Swar, Linn (2020) Modelling and Forecasting Volatility of Equity Market Indices And Gold Using Univariate GARCH Models: Evidence from Singapore. [Dissertation (University of Nottingham only)]
AbstractThis paper estimates and forecasts the conditional volatility of Singaporean equity indices namely STI, FSTS_SI, FSTM_SI, and gold quoted in Singaporean dollar (XAUSGD) by using GARCH (1,1), GJR-GARCH (1,1), and EGARCH (1,1) models. The period of the daily return data ranges from 2nd September 1999 to 31st July 2020 (5457 data points) for each equity index and from 4th January 2000 to 31st July 2020 (5369 data points) for XAUSGD. The objectives of this study are to check the presence of the leverage effect in the return series and to investigate which model performs the best for the purpose of estimating and forecasting the conditional volatility. Form the estimation results, it is found that the leverage effect is present in all the equity indices, except for XAUSGD. GJR-GARCH (1,1) performs the best in the conditional volatility estimation for the equity indices, while GARCH (1,1) performs the best for XAUSGD based on the SIC criterion. For best forecasting model selection based on the minimum RMSE value, GARCH (1,1) model provides the best forecast for the equity indices while EGARCH (1,1) performs the best for XAUSGD.
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