本文採用RiskMetrics模型與GARCH模型及馬可夫轉換模型估算避險基金指數之風險值，並進一步以RiskMetrics模型與GARCH模型及馬可夫轉換模型所估出之風險值進行比較，用以探討何種模型有較佳的預測能力及績效，使投資大眾於面臨風險時，能正確的評估與控管，以避免承擔超過預期的損失，實證結果如下：
1.由回溯測試的結果可知，RiskMetrics模型與GARCH模型及馬可夫轉換模型都能有效的估計風險值，風險控管能力均有一定的水準，其中又以馬可夫轉換模型在信心水準99%表現最佳。
2.就均方差標準根檢定而言，馬可夫轉換模型的表現為三模型中最優異的，推斷其原因為馬可夫模型採用馬可夫鏈做為狀態轉換的機制，相較於RiskMetrics模型與GARCH模型，更能夠考慮資料序列前後期狀態與相關訊息，進而對報酬分配有較精確的掌握。

This paper investigates the Valu-at-Risk(VaR) of returns on hedge fund indexes using the RiskMetrics , the GARCH and the Markov Switching Models. Furthermore, we compared the Valu-at-Risk(VaR) between the RiskMetrics , the GARCH  and  the Markov Switching Models. The purpose is to find out which of three models has better prediction and performance for investors to evaluate and to take control in order to avoid unexpected lost while minimizing damage. The result of this study shows the following:
1.The back-test shows that the RiskMetrics model, the GARCH model and the Markov Switching Model can estimate Valu-at-Risk(VaR) effectively which proves that the ability to control risk is at a good standard. Besides, the empirical results show Markov Switching Model can capture the distribution better than the others with a 99% confidence level under the back-test.
2.According to the RMSE, the Markov Switching Model erforms better than either the GARCH model or the RiskMetrics model. We infer that the Markov Switching Model can  well capture the distribution  resulting from the adoption of the transformation mechanism of Markov chain. The Markov chain contains more relative information of time serial data than other models do.

目錄………………………………………………………………………I 
圖目錄……………………………………………………………………Ⅲ 
表目錄……………………………………………………………………Ⅳ
第一章緒論
第一節研究動機…………………………………………………………1 
第二節研究目的…………………………………………………………3
第三節研究架構…………………………………………………………5
第四節研究流程…………………………………………………………6

第二章理論基礎與文獻回顧
第一節避險基金…………………………………………………………7
第二節風險值的意義及概念……………………………………………10
第三節常見之風險值模型及其估算方法………………………………13
第四節相關文獻…………………………………………………………18

第三章研究方法
第一節研究程序…………………………………………………………26
第二節資料檢驗…………………………………………………………26
第三節風險值的估算及使用模型………………………………………30
第四節風險值預測模型…………………………………………………31
第五節風險值的評價方式與預測績效…………………………………35


第四章實證結果分析
第一節資料來源與處理…………………………………………………37
第二節基本統計量特性分析……………………………………………38
第三節單根檢定與ARCH效果檢定………………………………………40
第四節RiskMetrics、GARCH與馬可夫轉換模型估計…………………45
第五節風險值估算與預測績效…………………………………………49
第五章結論………………………………………………………………67
參考文獻…………………………………………………………………69

表    目    錄
【表2.3.1】四種估算風險值方法特性………………………………17
【表4.2.1】避險基金指數報酬率基本統計特性……………………39
【表4.3.1】ADF及PP單根檢定（水準項）……………………………41
【表4.3.2】ADF及PP單根檢定（差分項）……………………………42
【表4.3.3】LM檢定與Q檢定……………………………………………44
【表4.4.1】RiskMetrics模型參數估計結果…………………………46
【表4.4.2】GARCH模型參數估計結果 ………………………………47
【表4.4.3】馬可夫轉換模型參數估計結果…………………………48
【表4.5.1】99%信賴水準下各模型回溯測試之失誤率檢定與均方誤差 ………50
【表4.5.2】95%信賴水準下各模型回溯測試之失誤率檢定與均方誤差 ………52
【表4.5.3】90%信賴水準下各模型回溯測試之失誤率檢定與均方誤差 ………54

圖    目    錄
【圖1.3.1】研究流程圖………………………………………………6
【圖2.2.1】風險值示意圖……………………………………………12
【圖4.5.1】RiskMetrics 模型避險基金指數估計風險值與實際報酬的比較 …58
【圖4.5.2】GARCH模型避險基金指數估計風險值與實際報酬的比較………62
【圖4.5.3】馬可夫轉換模型避險基金指數估計風險值與實際報酬的比較 ……65

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