請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16807
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇永成(YONG-CHENG SU) | |
dc.contributor.author | Yi-Ting Chen | en |
dc.contributor.author | 陳翊庭 | zh_TW |
dc.date.accessioned | 2021-06-07T23:46:49Z | - |
dc.date.copyright | 2014-07-16 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2014-06-13 | |
dc.identifier.citation | 1. Andersen, T.G., T. Bollerslev, P.F. Christoffersen, and F.X. Diebold (2006), “Practical Volatility and Correlation Modeling for Financial Market Risk Management,” NBER Working Paper Series 11069.
2. Angelidis, T., A. Benos and S. Degiannakis (2004), “The Use of GARCH Models in VaR Estimation,” Statistical Methodology, Vol. 1, pp. 105-128. 3. Basak, S. and A. Shapiro (2001), “Value-at-risk-based risk management: Optimal policies and asset prices,” Review of Financial Studies, Vol.14, pp. 371–405. 4. Berkowitz, J., and J. O’Brien (2002), “How Accurate Are Value-at-Risk Models at Commercial Banks?” Journal of Finance, Vol.57, pp. 1093-1111. 5. Bender,T.S. (1995),VAR: Seductive but Dangerous, Financial Analyst Journal, Sep-Oct, pp.12-24. 6. BIS, Basle Committee on Banking Supervision, 1988, “International Convergence of Capital Measurement and Capital Standards.” 7. BIS, Basle Committee on Banking Supervision, 1996, “Supervisory Framework for the Use of Back-testing in Conjunction with Internal Models Approach to Market Risk Capital Requirements.” 8. BIS, Basle Committee on Banking Supervision, 1996 updated to 1998, “Amendment to the Capital Accord to Incorporate Market Risks.” 9. BIS, Basle Committee on Banking Supervision (2004), “Basel II International Convergence of Capital Measurement and Capital Standards”. 10. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, Vol.31, pp. 307–327. 11. Bollerslev, T. (1987), “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return,” The Review of Economics and Statistics, Vol. 69, pp.542-547. 12. Burns, P. (2002), “The Quality of Value at Risk via Univariate GARCH,” Working Paper, Burns Statistic. 13. Cassidy, C., and M. Gizycki (1997), “Measuring Traded Market Risk :Value at Risk and Backtesting Techniques,” Research Discussion Paper 9708, Reserve Bank of Australia. 14. He, C., and T. Terasvirta (1999), “Properties of Moments of a Family of GARCH Processes,” Journal of Econometrics, Vol.92, pp.173-192. 15. Chiang (2004), “Modeling Value-at-Risk of Financial Companies---A Comparison of Symmetric and Asymmetric Models,” Master Thesis of Graduate Institute of Business Administration in National Taiwan University. 16. Engle, R. F. (1982). “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation,” Econometrica, Vol.50, pp. 987-1008. 17. Engel, R. F., D. M. Lilien, and R. P. Robins (1987), “Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model,” Econometrica, Vol.55, pp. 391-407. 18. Engle, R. F., and V. K. Ng (1993). “Measuring and Testing the Impact of News on Volatility,” Journal of Finance, Vol.48, pp. 1749-1778. 19. Guermat, C. and R. D. F. Harris (2002), “Robust Conditional Variance Estimation and Value-at-Risk,” Journal of Risk, Vol.4, pp.25-41. 20. Gupta, A., and B. Liang (2005), Do hedge funds have enough capital ? A value-at-risk approach, Journal of Financial Economics, Vol.77, pp.219-253. 21. Glosten, L. R., R. Jagannathan, and D. E. Runkle (1993), “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, Vol.48, pp. 1179-1801. 22. Hentschel, L. (1995), “All in the Family Nesting Symmetric and Asymmetric GARCH Models,” Journal of Financial Economics, Vol.39, pp. 71-104. 23. Jorion, P. (2001), Value-at-Risk: The New Benchmark for Controlling Market Risk, 2nd Edition. 24. Dowd, K. (1999), “A value at risk approach to risk-return analysis,” Journal of portfolio management, Vol.25, pp.60-67. 25. Inui, K., M. Kijima, and A. Kitano(2005), “VaR Is Subject to Significant Positive Bias,” Statistics & Probability Letters, Vol.72, pp.299-311. 26. Lin, Chu-Hsing and Shan-Shan Shen (2006), “Can the Student-t Distribution Provide Accurate Value at Risk?” The Journal of Risk Finance, Vol.7, pp. 292-300. 27. Mandelbrot, B. (1963), “The Variation of Certain Speculative Prices,” The Journal of Business, Vol.36, pp. 394-419. 28. Nelson, D. B. (1991). “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, Vol.59, pp. 347-370. 29. Kupiec, P. H. (1998), “Stress Testing in a Value at Risk Framework,” Journal of derivatives, Vol.6, pp.7-24. 30. Abken, P. A. (2000), “An empirical evaluation of value at risk by scenario simulation,” Journal of derivatives, Vol.7, pp.12-29. 31. Schwert, G. W. (1989), “Why Does Stock Market Volatility Change Over Time,” Journal of Finance, Vol.44, pp. 1115-1153. 32. Wang, (2003), “Market Risk VaR Models for Financial Holding Company,” Master Thesis Graduate Institute of Business Administration in National Taiwan University. 33. So, M. K. P. and P. L. H. Yu (2006), “Empirical Analysis of GARCH Models in Value at Risk Estimation”, Journal of International Financial Markets, Institutions & Money, Vol.16, pp.180-197. 34. Kuo, S. (2012), “Asymmetic GARCH Value-at-Risk of EUD/USD during the European Debt Crisis,” Master Thesis Graduate Institute of Business Administration in National Taiwan University. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16807 | - |
dc.description.abstract | 市場風險值VaR 已是受到廣泛應用的風險控制工具。在近年VaR模型效果估計比較的多篇研究證實了GARCH 模型在估計市場風險值的有效性及精確性後,本研究檢驗包含旋轉效果的GJR-GARCH及平移效果的NA-GARCH兩種不對稱GARCH模型與對稱的GARCHM模型比較,在不同的報酬結構之下,找出對於黃金價格具有較佳VaR值預測表現的模型。我們利用523筆日報酬率的資料,並將其分為兩個群組進行模型配置及市場風險值估算之用,依據不同信賴區間下估算出的市場風險值與實際報酬表現做比較,並另外運用其他穿透測試等項目檢驗模型的精確性。
本研究主要發現包含以下部分: 1. 從穿透次數而言,對稱的GARCHM模型和GJR-GARCH模型的表現優於NA-GARCH模型。表示不對稱的GARCH模型表現並不總是較對稱的GARCH模型好。 2. 雖然平均而言NA-GARCH表現不如對稱的GARCH模型和GJR-GARCH模型,但在所有模型中,ARMA(1,1)-NA-GARCHM(1,1)在各項穿透測試項目的表現最好。 3. ARMA(1,1)-NA-GARCHM(1,1)表現最好,我們認為是由於NA-GARCH模型不對稱的效果較小,較符合黃金價格波動小的特性。 | zh_TW |
dc.description.abstract | VaR is more applicable as a financial management tool to control risk. Since the GARCH model is proved to be the useful and more accurate model in estimating VaR, in this paper, we employ the asymmetric GARCH models including the innovation-rotated GJR GARCH and the innovation-shifted NA GARCH models with different mean equations in comparison with symmetric GARCHM model to find out a more appropriate GARCH method in estimating VaR of gold price. We gathered the latest 523 daily return of gold and divided into two groups to fit the models and get the VaR estimates under each confidence level we chose.
Our major findings are described as follows: (1) In term of violation number, symmetric GARCH model and GJR-GARCH models outperform NA-GARCH models. The result implies that asymmetric GARCH models do not outperform symmetric GARCH models (GARCHM model) all the time. (2) We evidently find out ARMA(1,1)-NA-GARCHM(1,1) is the best fitted model in estimating VaR of gold price through forward test among GARCH models with four types of mean equations. (3) The relatively smaller asymmetric effect of NA-GARCH models (ARMA(1,1)-NA-GARCHM(1,1)) fits better with the relatively stable character of gold price compared to GJR-GARCH models. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:46:49Z (GMT). No. of bitstreams: 1 ntu-102-R00723014-1.pdf: 1714136 bytes, checksum: 44405699c781757fc71f0fda852cf4e5 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Purposes and Motivation 1 1.2 Framework 4 Chapter 2 Basel Accord and Market Risk 5 2.1 The Basel Committee 5 2.2 1988 Basel I Accord 6 2.3 1996 Amendment 7 2.4 Basel II Accord 8 2.5 Basel III Accord 10 Chapter 3 Literature Review 12 3.1 Value-at-Risk 12 3.2 Volatility Modeling with GARCH Effect 13 3.3 Related Literature 14 Chapter 4 Data 19 4. 1 Introduction to Gold Spot Price 19 4. 2 Holding Period and Daily P&L 19 Chapter 5 Methodology 21 5.1 GARCHM(1,1) 22 5.2 GJR-GARCHM(1,1) 23 5.3 NA-GARCHM(1,1) 24 Chapter 6 Empirical Results 26 6.1 Model Robustness and Parameter Estimates 26 6.2 Parameter Estimates 26 6.3 Forward Test under Different GARCH models 30 Chapter 7 Conclusion 35 References 37 Figure1: Distribution of Daily P&L of Gold 42 Figure 2: P&L Return and VaR in GARCHM(1,1) 43 Figure 3: P&L Return and VaR in AR(1)-GARCHM(1,1) 44 Figure 4: P&L Return and VaR in MA(1)-GARCHM(1,1) 45 Figure 5: P&L Return and VaR in ARMA(1,1)-GARCHM(1,1) 46 Figure 6: P&L Return and VaR in GJR-GARCHM(1,1) 47 Figure 7: P&L Return and VaR in AR(1)- GJR-GARCHM(1,1) 48 Figure 8: P&L Return and VaR in MA(1)-GJR-GARCHM(1,1) 49 Figure 9: P&L Return and VaR in ARMA(1,1)-GJR-GARCHM(1,1) 50 Figure 10: P&L Return and VaR in NA-GARCHM(1,1) 51 Figure 11: P&L Return and VaR in AR(1)- NA-GARCHM(1,1) 52 Figure 12: P&L Return and VaR in MA(1) NA-GARCHM(1,1) 53 Figure 13: P&L Return and VaR in ARMA(1,1)- NA-GARCHM(1,1) 54 Table 1:Returns Statistics Summary for Gold (2009/11/3to 2011/11/3) 55 Table 2: Likelihood Ratio Test 55 Table 3: Parameters estimated in GARCHM(1,1) 56 Table 4: Parameters estimated in AR(1)-GARCHM(1,1) 57 Table 5: Parameters estimated in MA(1)-GARCHM(1,1) 58 Table 6: Parameters estimated in ARMA(1,1)-GARCHM(1,1) 59 Table 7: Parameters estimated in GJR-GARCHM(1,1) 60 Table 8: Parameters estimated in AR(1)-GJR-GARCHM(1,1) 61 Table 9: Parameters estimated in MA(1)-GJR-GARCHM(1,1) 62 Table 10: Parameters estimated in ARMA(1,1)-GJR-GARCHM(1,1) 63 Table 11: Parameters estimated in NA-GARCHM(1,1) 64 Table 12: Parameters estimated in AR(1)-NA-GARCHM(1,1) 65 Table 13: Parameters estimated in MA(1)-NA-GARCHM(1,1) 66 Table 14: Parameters estimated in ARMA(1,1)-NA GARCHM(1,1) 67 Table 15: Violation number allowed in Basel Accord 68 Table 16: Violation Number and Mean VaR in GARCH models at 95% confidence level 69 Table 17: Violation Number and Mean VaR in GARCH models at 99% confidence level 70 Table 18: Model Comparison under 95% Confidence Level 71 Table 19: Model Comparison under 99% Confidence Level 72 | |
dc.language.iso | en | |
dc.title | 黃金之不對稱GARCH市場風險值之研究 | zh_TW |
dc.title | Asymmetric GARCH Value at Risk of GOLD | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 胡星陽(XING-YANG HU),黃漢青(HAN-JING HUANG) | |
dc.subject.keyword | 市場風險值,GARCH模型,黃金, | zh_TW |
dc.subject.keyword | VaR,GARCH,GOLD, | en |
dc.relation.page | 72 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2014-06-13 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
顯示於系所單位: | 財務金融學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-102-1.pdf 目前未授權公開取用 | 1.67 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。