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研究生:陳俊儒
論文名稱:交易量對動態風險值估計之影響:以期貨市場為例
論文名稱(外文):The Effect of Volume on the Estimation of a Dynamic VaR: Evidences from Future Markets
指導教授:黃明祥黃明祥引用關係
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:54
中文關鍵詞:極值理論動態極值理論期貨市場交易量
外文關鍵詞:Extreme Value TheoryDynamic Extreme Value TheoryFuture MarketVolume
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許多動態極值理論文獻(Dynamic Extreme Value Theory DEVT)皆指出,金融資產不對稱性極值理論模型(如GJR+GPD、EGARCH+GPD)的表現相較以一般化自我迴歸條件異質性變異數模型(Generalized Autoregression Conditional Heteroskedasticity,GARCH)為基礎的傳統動態極值理論法還佳,以經濟理論的觀點來看,任何資產的最終交易價格均由價量均衡所決定的;而依據混合分配假說(Mixture Distribution Hypothesis)指出,交易量(Trading Volume)具有資訊到達(Information Arrival)之功能,因此,就理論觀點來看,金融資產之交易量應當與報酬及波動有顯著的關聯。本研究旨在探討「價量關係導入極值理論法」,使用外生變數於交易量上並結合不對稱GARCH族群的極值理論方法是否能改善風險值估計的精確性。
本研究的觀察期間為1997年1月初到2001年12月底,並以NASDAQ INDEX期貨、S&P 500 INDEX期貨與NATURAL GAS期貨等三個期貨資產為觀察樣本,本研究採用採GARCH+GPD+V、GJR+GPD+V、EGARCH+GPD+V來進行風險值估計,估計結果使用回溯測試(Back Testing)來判斷模型的優劣程度,再輔以均方差根來評估模型的精確性。實證結果顯示,使用外生變數於交易量上並結合不對稱GARCH族群的極值理論方法相較傳統動態極值理論法的表現來得佳,此外,在三組模型中,以失誤率與均方差根方面GJR+GPD+V的表現是最佳的。
EVT-based GARCH family models, such as GJR+GPD and EGARCH+GPD, have been well-documented as preferred standard approach in the estimation of value at risk (VaR). However, the standard dynamic VaR model failed to account for an important nature of the return volatility driven by asymmetric volume changes in the financial markets. Thus, it may result in a biased estimation on VaR.
The objective of this study is to investigate whether the incorporation of trading volumes as an explanatory variable in the variance equation in the standard dynamic VaR framework can improve the accuracy of the VaR modeling.
Using the three futures markets, NASDAQ INDEX, S&P 500 INDEX and NATURAL GAS, as samples covering the period from Jan. 1997 to Dec 2001, the study adopts alternative dynamic EVT-based GARCH family VaR models including GARCH+GPD+V, GJR+GPD+V and EGARCH+GPD+V, to estimate value at risks of the underlying sample markets. The reliabilities and accuracies of alternative models are further validated via the back testing and root mean square error (RMSE), respectively.
In supporting our hypothesis, the findings indicate that the proposed alternative dynamic EVT-based GARCH family VaR models with volumes, in general, outperform the standard dynamic VaR model. In particular, GJR+GPD+V is the best model among the other underlying models in terms of both rate of failure and RMSE .
中文摘要 Ⅰ
英文摘要 Ⅱ
目錄 Ⅲ
圖目錄 Ⅳ
表目錄 Ⅴ
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 2
第三節 論文架構 3
第二章 相關文獻探討 4
第一節 風險值的概念與定義 4
第二節 風險值模型文獻 5
第三節 極值理論之相關實證研究文獻 8
第四節 金融資產價量關相關文獻 10
第三章 研究方法 12
第一節 資料來源 12
第二節 檢定分析 12
第三節 傳統風險值模型 15
第四節 超越門檻值法 17
第五節 條件變異數模型 20
第六節 模型檢測 25
第四章 實證結果分析 28
第一節 樣本資料處理 28
第二節 樣本特徵統計分析 32
第三節 風險值估計結果與分析 42
第五章 結論與建議 49
第一節 結論 49
第二節 建議 50
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