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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蘇永成(Yong-Chern Su) | |
| dc.contributor.author | Lin-Chi Hsien | en |
| dc.contributor.author | 林佶賢 | zh_TW |
| dc.date.accessioned | 2021-06-15T04:08:14Z | - |
| dc.date.available | 2013-02-11 | |
| dc.date.copyright | 2010-02-11 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-02-04 | |
| dc.identifier.citation | References
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45192 | - |
| dc.description.abstract | 市場風險值VaR是近年來在國際間被廣泛應用的一種風險管理工具，本篇論文主要採用了兩種不同效果的不對稱GARCH模型，分別為具有旋轉效果的GJR-GARCH模型與具有平移效果的NA-GARCH模型，將期預測的結果與對稱的GARCH模型相比較，以期探討何種GARCH模型對於VaR的預測有較佳的表現。此外為探討不同報酬結構對於VaR的預測是否會產生影響，本研究將四種報酬結構導入前述三種GARCH模型，分別為ARMA(1,1)，AR(1)，MA(1)與「in-mean」。
資料我們採用的是代表NASDAQ-100指數的ETF QQQQ，資料觀察期間為2002年1月14日到2009年2月27日之每日報酬共1800筆樣本點.我們使用前1200筆樣本來做配適模型與模型的參數估計，使用後600筆樣本點則用以在99％與95％的信賴水準下比較各GARCH模型所估計出的市場風險值，並比較其預測能力。 本研究的主要成果如下： (1) 不對稱的GARCH模型並非總是優於對稱GARCH模型，本篇的對稱GARCH模型預測結果優於不對稱的NA-GARCH模型。 (2) 實證結果發現GJR-GARCH 模型不論是在95%或是99%信心水準下都有較佳的預測能力。在四種不同報酬結構的GJR-GARCH 模型中又以ARMA(1,1)-GJR-GARCHM(1,1)表現最佳。 (3) 以過去資料配適建構的GARCH模型無法非常準確的預測次級貸款金融風暴期間時的報酬波動，然而可採用較近期的資料配適模型來解決此一問題。 | zh_TW |
| dc.description.abstract | VaR is a newly developed tool for risk management that had been used international wide. In this paper we adopt two asymmetric GARCH models, with GJR-GARCH represent the rotation asymmetric effect, and NA-GARCH for the shift asymmetric effect, to compare their performance with VaR forecasting of the symmetric GARCH model. In addition, we employ different mean equations which are ARMA(1,1), AR(1), MA(1), and “in-mean” in order to find out a more appropriate GARCH method in estimating VaR of QQQQ.
We pick up the latest 1800 daily information of QQQQ from 2002/01/14 to 2009/02/27. We use 1200 observations for parameters estimates; the rest 600 will be used by different models to forecast VaR in 99% and 95% confidence level then been evaluated. Our major findings contain several aspects as follows: (1) Asymmetric GARCH models do not always outperform symmetric GARCH models (GARCHM model). (2) The empirical results show that GJR-GARCH models have better performance in estimating VaR through the forward test analysis under 95% and 99% confidence level. Among GJR-GARCH models with four types of mean equations, we distinctly find out ARMA(1,1)-GJR- GARCHM(1,1) is the best fitted model. (4) The GARCH models which are estimated by past data cannot forecast the VaR precisely during subprime crisis. However, this problem can be solved by adopting update models which are estimated by recent data. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T04:08:14Z (GMT). No. of bitstreams: 1 ntu-99-R96723074-1.pdf: 1314773 bytes, checksum: d0d5694851c1def87bd42ecb6cb76b4a (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Content
Chapter 1 Introduction 5 1.1 Purposes and Motivation 5 1.2 Framework 7 Chapter 2 Basel Accord and Market Risk 8 2.1 The Basel Committee 8 2.2 1988 Basel I Accord 9 2.3 1996 Amendment 10 2.4 Basel II Accord 11 Chapter 3 Literature Review 14 3.1 Value-at-Risk 14 3.2 Volatility Modeling with GARCH Effect 15 3.3 Related Literature 16 Chapter 4 Data 21 4. 1 Introduction to NASDAQ-100 index and QQQQ 21 4. 2 Holding Period and Daily P&L 22 Chapter 5 Methodology 24 5.1 GARCHM(1,1) 25 5.2 GJR-GARCHM(1,1) 26 5.3 NA-GARCHM(1,1) 27 Chapter 6 Empirical Results 29 6.1 Model Robustness and Parameter Estimates 29 6.2 Forward Test under Different GARCH models 32 6.3 Comparison Between Initial Model and Update Model 36 Chapter 7 Conclusion 38 References 40 Figure Figure1: Distribution of Daily P&L of QQQQ 44 Figure 2: P&L Return and VaR in GARCHM(1,1) 45 Figure 3: P&L Return and VaR in AR(1)-GARCHM(1,1) 46 Figure 4: P&L Return and VaR in MA(1)-GARCHM(1,1) 47 Figure 5: P&L Return and VaR in ARMA(1,1)-GARCHM(1,1) 48 Figure 6: P&L Return and VaR in GJR-GARCHM(1,1) 49 Figure 7: P&L Return and VaR in AR(1)- GJR-GARCHM(1,1) 50 Figure 8: P&L Return and VaR in MA(1)-GJR-GARCHM(1,1) 51 Figure 9: P&L Return and VaR in ARMA(1,1)-GJR-GARCHM(1,1) 52 Figure 10: P&L Return and VaR in NA-GARCHM(1,1) 53 Figure 11: P&L Return and VaR in AR(1)- NA-GARCHM(1,1) 54 Figure 12: P&L Return and VaR in MA(1) NA-GARCHM(1,1) 55 Figure 13: P&L Return and VaR in ARMA(1,1)- NA-GARCHM(1,1) 56 Figure 14: Comparison of GARCH models under 95% confidence level 57 Figure 15: Comparison of AR(1)-GARCH models under 95% confidence level 58 Figure 16: Comparison of MA(1)-GARCH models under 95% confidence level 59 Figure 17: Comparison of ARMA(1,1)-GARCH models under 95% confidence level 60 Figure 18: Comparison of GARCH models under 99% confidence level 61 Figure 19: Comparison of AR(1)-GARCH models under 99% confidence level 62 Figure 20: Comparison of MA(1)-GARCH models under 99% confidence level 63 Figure 21: Comparison of ARMA(1,1)-GARCH models under 99% confidence level 64 Tables Table 1: Components of QQQQ as of January 20, 2009 65 Table 2:Returns Statistics Summary for QQQQ (2002/01/14 to 2009/02/27) 67 Table 3: LR Test 67 Table 4: Parameters estimated in GARCHM(1,1) 68 Table 5: Parameters estimated in AR(1)-GARCHM(1,1) 69 Table 6: Parameters estimated in MA(1)-GARCHM(1,1) 70 Table 7: Parameters estimated in ARMA(1,1)-GARCHM(1,1) 71 Table 8: Parameters estimated in GJR-GARCHM(1,1) 72 Table 9: Parameters estimated in AR(1)-GJR-GARCHM(1,1) 73 Table 10: Parameters estimated in MA(1)-GJR-GARCHM(1,1) 74 Table 11: Parameters estimated in ARMA(1,1)-GJR-GARCHM(1,1) 75 Table 12: Parameters estimated in NA-GARCHM(1,1) 76 Table 13: Parameters estimated in AR(1)-NAGARCHM(1,1) 77 Table 14: Parameters estimated in MA(1)-NA-GARCHM(1,1) 78 Table 15: Parameters estimated in ARMA(1,1)-NA GARCHM(1,1) 79 Table 16: Violation number allowed in Basel Accord 80 Table 17: Violation Number and Mean VaR in GARCH models at 95% confidence level 80 Table 18: Violation Number and Mean VaR in GARCH models at 99% confidence level 81 Table 19: Model Comparison under 95% Confidence Level 82 Table 20: Model Comparison under 99% Confidence Level 83 Table 21: Initial Model and Update Model Comparison under 95% Confidence Level (I) 84 Table 22: Initial Model and Update Model Comparison under 95% Confidence Level (II) 85 Table 23: Initial Model and Update Model Comparison under 95% Confidence Level (III) 86 Table 24: Initial Model and Update Model Comparison under 95% Confidence Level (IV) 87 Table 25: C Initial Model and Update Model Comparison under 99% Confidence Level (I) 88 Table 26: C Initial Model and Update Model Comparison under 99% Confidence Level (II) 89 Table 27: Initial Model and Update Model Comparison under 99% Confidence Level (III) 90 Table 28: Initial Model and Update Model Comparison under 99% Confidence Level (IV) 91 | |
| dc.language.iso | en | |
| dc.subject | 市場風險 | zh_TW |
| dc.subject | NA GARCH | zh_TW |
| dc.subject | GJR GARCH | zh_TW |
| dc.subject | GARCH | zh_TW |
| dc.subject | QQQQ | zh_TW |
| dc.subject | 風險值 | zh_TW |
| dc.subject | Market risk | en |
| dc.subject | NA GARCH | en |
| dc.subject | GJR GARCH | en |
| dc.subject | GARCH | en |
| dc.subject | QQQQ | en |
| dc.subject | VaR | en |
| dc.title | QQQQ之不對稱GARCH市場風險值之研究 | zh_TW |
| dc.title | Asymmetric GARCH Value at Risk of QQQQ | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 王耀輝(Yaw-Huei Jeffery Wang) | |
| dc.contributor.oralexamcommittee | 胡星陽,黃漢青 | |
| dc.subject.keyword | 市場風險,風險值,QQQQ,GARCH,GJR GARCH,NA GARCH, | zh_TW |
| dc.subject.keyword | Market risk,VaR,QQQQ,GARCH,GJR GARCH,NA GARCH, | en |
| dc.relation.page | 92 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-02-05 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| Appears in Collections: | 財務金融學系 | |
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