由於金融資產的波動性在金融市場(尤其是在台灣這個具有高波動性的市場)扮演重要角色，無論是在風險管理或是衍生性商品的訂價、選擇權的交易與避險等等方面，如何準確預測波動性，一直都是相當重要的課題。本文使用一般化自我迴歸條件異質變異數模型(GARCH)及其修正模型(GJR-GARCH)與台灣期交所所編制台指選擇權波動性(VIX)指數之預測能力來比較，希望找出適合描述台灣金融市場波動性模型。 實證發現，以日資料進行預測績效時，GJR-GARCH模型表現較為優秀；若以日內資料來估計時，各個模型的解釋能力皆明顯提高許多且誤差也相對改善。因此，我們可以肯定頻率越密集的資料可獲得更高的解釋能力、更低的預測誤差、與更多的資訊內涵。綜合本文研究，日內報酬台指選擇權波動性指數(VIX)在平均絕對誤差(MAE)與均方根誤差(RMSE)的評估上，雖然並非最好，但似乎是預測真實波動性的不偏估計值。 Volatility forecasting is very important to derivative pricing, hedging, and risk management. This paper using GARCH, GJR-GARCH models and the VIX index of TAIEX Options to compare their forecasting ability. The empirical evidence show that using daily data to forecast the performance, GJR-GARCH model is superior, while using intraday data, the explanatory power of all models are obviously enhanced and errors are also improved. Therefore, we approve that the more intensive data can obtain the higher explanatory power, lower forecasting errors and more information content. In summary, we find that the VIX index of TAIEX Options using intraday data seems an unbiased estimator to forecast the real volatility, although it does not have the best performance than other models in MAE and RMSE testing indicators.
