金融市場波動性的預測，於理論上，一般皆認為其報酬的行為是隨機的，且通常假設為一常態分配及變異數為固定的情境下，然就實證結果而言，其報酬率常呈一高狹峰且變異數隨時間變動而改變。然近年來對波動性的研究所示，其不但是隨時間而改變，且金融資產價格波動具有可預測性的，因此許多預測波動的研究方法被提出。
本文以採用條件限制最小平方模型，來檢驗於金融市場商品價格波動性的預測能力，是否優於異質變異家族的模型。其研究樣本資料採用橫跨三個不同市場中，十二個市場標的的每日收市價為研究樣本，比較歷史標準差模型、指數加權移動平均模型、一般化自我回歸條件異質變異數模型及限制最小平方估計模型，共四種波動估計模型，以實證比較何者的預測績效最佳。此外，採用平方根預測誤差為評估預測績效的標準。透過上述實證分析，期待獲得一廣泛性預測較佳的模型，進一步提供投資決策者掌握金融資產報酬波動性的可行管道。
實證研究發現於比較採用一般化自我回歸條件異質變異數模型及歷史標準差模型所得之樣本預測值各據勝場，另指數加權移動平均模型所得預測值卻普遍優於一般化自我回歸條件異質變異數模型，而採用限制最小平方估計模型預測樣本市場標的結果，於所有樣本市場標的中所得預測值，均優於其他的預測模型。

Volatility forecasting in financial assets is important to traders, investors and risk managers. In theory，the return volatility of financial assets are random，normal distribution and variance is fixed. In fact，the returns is leptokurtosis，and variance changes from time to time. The forecasting volatility ability of time-series volatility forecasting models recent period in econometrics literature changes from time to time，and the price volatility of financial assets may forecast .
Using Ederington and Guan（2005）apply the RLS（Restricted Least Squares） volatility forecasting model，to estimates the ability of price volatility in financial assets，and compare the ability of GARCH model . We compare the ability of these four forecasting models for three financial markets in 12 financial assets price，to find the best ability in which model. We also choice Ederington and Guan（2005） apply the RMSFE（Root Mean Squared Forecast Error）to measure the forecasting ability in difference between actual and forecast annualized standard deviation of returns.
After compared the estimation results，we can not find the difference on the forecasting ability between GARCH（1,1） model and STD model，and the EWMA model is better than GARCH（1,1） model，the RLS model is better than other models in whole 12 financial assets.

目   錄
第一章 緒論
第一節 研究背景與動機…………………………………………………1
第二節 研究目的  ………………………………………………………3
第三節 研究限制…………………………………………………………5
第四節 論文架構…………………………………………………………6
第五節 研究流程…………………………………………………………7
第二章 文獻回顧	
第一節 波動性的特性……………………………………………………8
第二節 波動性估計模型的發展…………………………………………9
第三節 國內的研究實證 ………………………………………………14
第四節 國外的研究實證 ………………………………………………17
第三章 研究方法
第一節 資料來源與處理 ………………………………………………22
第二節 常態檢定 ………………………………………………………23
第三節 序列相關檢定 …………………………………………………24
第四節 實證模型介紹 …………………………………………………25
第五節 預測績效的評估標準 …………………………………………30
第四章 實證結果
第一節 實證步驟 ………………………………………………………31
第二節 資料分析 ………………………………………………………31
第三節 模型的參數估計 ………………………………………………37
第四節 樣本配適之比較 ………………………………………………40
第五章 結論  ………………………………………………………… 44
參考文獻  ………………………………………………………………45
表 目 錄
表4-1 每日收盤價之基本統計檢定量…………………………………35
表4-2 日報酬之基本統計檢定量………………………………………36
表4-3 GARCH（1,1）模型之參數估計值………………………………37
表4-4 RLS模型之參數估計值 …………………………………………39
表4-5 RMSFE 績效評估結果  …………………………………………42
圖 目 錄
圖1-1 研究流程圖………………………………………………………7
圖4-1 每日收盤價之走勢圖 …………………………………………32
圖4-2 日報酬之走勢圖 ………………………………………………33
圖4-3 道瓊工業指數之GARCH和RLS模型隱含係數圖 ………………39

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