鑑於波動度無論在金融衍生性商品訂價、風險值的估算、投資組合的選擇以及動態避險策略的推估上皆有密切的關係，因此本文在常態分配與一般化誤差分配（GED）兩種分配下，驗證GARCH、GJR-GARCH、E-GARCH、I-GARCH與Q-GARCH等五種傳統計量模型與其結合類神經模型之五種複合式模型在波動度預測上之能力，且為解決真實波動度（true volatility)之代理問題（volatility proxy），本文導入以日內60分鐘報酬最高與最低計算之Realized Range-Based Volatility，並分別以MAE、MSE、MME與VaRE做為預測能力衡量指標，另外並以預測之波動度代入Black-Sholes公式反推出選擇權理論價格與真實價格計算其MAE，最後加入SPA (Superior Predictive Ability)檢定，以改善大量模型比較所可能存在最佳模型選取錯誤之疑問，期望找出一個可以精準預測波動度之模型。實證結果為：台灣日資料的股票波動，以類神經結合Q-GARCH模型預測能力最佳，I-GARCH最差；而在選擇權驗證方面，其結果完全相反，以類神經結合I-GARCH模型預測能力最佳。顯示由統計之觀點與財務之觀點來看波動度預測能力會得到不同的結論，而唯一可以確定的就是無論在損失函數方面與選擇權驗證方面，類神經模型之導入皆可增進模型之預測能力。 We compare the predictive performance of various GARCH family models and Neural Networks. The models are compared out-of-sample using Taiwan Stock Exchange Capitalization Weighted Stock Index（TAIEX）data. We substitute the Realized Range-Based Volatility for the latent true volatility and choose six statistical loss functions to compare the predictive performance. We also use the forecasting volatilities into Black-Scholes formula to evaluate the theoretical option prices and compare with real option prices. To control for the fact that as the number of models increase, so does the probability of finding superior predictive ability among the collection of models, we implement the Superior Predictive Ability Test of Hansen（2005）. We find that, for four loss function, Neural Networks nested Q-GARCH model seems dominate. For two VaR based loss function, GJR-GARCH and GARCH models are preferred. For option pricing, Neural Networks nested I-GARCH model, which performs the worst in the six loss function, seems to be the best performer.
