波動度估計向來為金融市場重要的議題，無論是在金融商品定價、資產配置、抑或是風險管理上，皆扮演舉足輕重的角色。本研究以S&P500期貨與黃金期貨為樣本，並採用融合已實現波動概念之realized GARCH模型，搭配日內資料之已實現變異數(realized variance, RV)、已實現變幅變異數(realized range-based variance, RRV)與已實現雙冪次變異(realized bi-power variation, RBV)，分別計算波動度，再藉由波動擇時策略衡量其個別經濟價值並加以比較。本研究以日內資料來估計RV、RRV與RBV，並進一步探討高頻率資料的效果。經實證結果得知，realized GARCH模型搭配已實現變幅變異數(realized range-based variance)之波動估計表現較佳，其帶來的經濟價值會優於傳統靜態模型。
This paper reinforces the realized GARCH which is included the realized volatility (RV), realized range volatility (RRV) and realized bi-power variation (RBV). The data was consisted of S&P500 futures and gold futures in this empirical study. The RV, RRV and RBV are intraday-based volatilities, so that we can examine how the high frequency data influence. It took the volatility based method of the realized GRACH model as well as RV, RRV and RBV have already forecasted the volatility. It was measured volatility forecast through volatility timing strategies in order to compare with others which economic value is better. Furthermore, the timing strategy had better economic benefits when it compared with the static strategy.
