Improving GARCH-based Volatility Forecasts for Taiwanese Stock Markets with Daily and Intraday Trading Information

• Main Authors: Chun-Wei Wu, 吳俊緯
• Other Authors: Chien-Liang Chiu
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/27758997173646398709

碩士 === 淡江大學 === 財務金融學系碩士班 === 102 === Estimating the true volatility of assets returns is a difficult task since financial assets are well known to have stylized characteristics of volatility clustering and heteroskedasticity. Based on the GARCH （generalized autoregressive conditional heteroskedasticity, GARCH） framework, this thesis considers two GARCH volatility model specifications: （i） the traditional GARCH（1,1） model, （ii） the GARCH-X model which augments the traditional GARCH model by respectively incorporating daily price ranges （PK, GK, and RS）, realized volatility （RV）, realized bipower variation （RBP）, implied volatility and overnight volatility （ONV） as explanatory variable into the GARCH variance equation. These models are used to investigate the information value of the daily/intraday trading prices that is embodied in the aforementioned volatility estimators for improving forecasts of TWSE and OTC stock markets volatilities at daily horizon. This study adopts ARET （absolute returns）, PK range and RV volatility proxy measures for used in empirical exercise. The out-of-sample forecast evaluation is conducted using various proxy measures in terms of MAE and LL loss error statistics. Particularly, this study also employs benefit statistics to further examine the information values of the various estimators for improving GARCH-based volatility forecasts. The empirical results show that to predict fluctuations in performance results of the ARET proxy variables except, both the prediction of PK and RV performance results are almost the same, the predictive power of the model begin with GARCH-RBP are the best, i.e. GARCH model can enhance more accuracy of the volatility forecasting.