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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇永成 | |
| dc.contributor.author | Hai-Lan Chen | en |
| dc.contributor.author | 陳海蘭 | zh_TW |
| dc.date.accessioned | 2021-06-08T05:14:32Z | - |
| dc.date.copyright | 2006-07-13 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-03 | |
| dc.identifier.citation | References
1. Jeremy Berkowitz and James O’Brien (2002), How Accurate are Value-at-Risk Models at Commercial Banks? Journal of Finance 57, 1093-1112 2. Patricia Jackson, David J. Maude, and William Perraudin (1998), Bank capital and Value at Risk. Bank of England 1998 ISSN 1368-5562 3. Kao (2005), NA-GARCH Model in Value-at-Risk of Financial Holdings, working paper of NTU, 2005. 4. Chiang (2004), Modeling Value at Risk of Financial Companies ― A Comparison of Symmetric and Asymmetric Models, working paper of NTU, 2004. 5. Wang (2003), Market Risk VaR Models for Financial Holding Company, working paper of NTU, 2003. 6. Ching-Fan Chung (2003), The Conditional Variance Model, Chapter 25. http://gate.sinica.edu.tw/~metrics/index.html 7. Christopher J. Neely, Target Zones and Conditional Volatility: The Role of Realignments. Journal of Empirical Finance, April 1999. 8. Changli He and Timo Teräsvirta (1997), Properties of Moments of a Family of GARCH Processes. Working Paper Series in Economics and Finance No 198 9. Financial Risk Manager Handbook, by Philippe Jorion, 2005, Wiley, Chapter 31, 32. 10. Value-at-Risk: The New Benchmark for Managing Financial Risk, by Philippe Jorion, 2000, McGraw-Hill. 11. Gupta, Anurag and Liang, Bing (2005), 'Do Hedge Funds Have Enough Capital? A Value-at-Risk Approach '. Journal of Financial Economics 77, July 2005, 219-253. 12. Hull, J. and A. White (1998). Incorporating volatility updating into the historical simulation method for value-at-risk. The Journal of Risk 1, 5-19. 13. Bauer C (2000). Journal of Economics and Business 52, September 2000, 455-467. 14. Prause (1999). The Generalized Hyperbolic Model: Estimation, Financial Derivatives, and Risk Measures. Unpublished PhD thesis, University of Freiburg, Germany. 15. Embrechts, P., C. Kluppelberg, and T. Mikosch (1997) Modelling extremal events. For insurance and finance. Applications of Mathematics 33, Springer-Verlag, Berlin. 16. Benoit Mandelbrot (1963), New methods in statistical economics, J. Polit. Economy 71, 421-440 17. E.F. Fama (1965). 'The behaviour of stock market prices'. Journal of Business, January, 34-105. 18. Blattberg, R. C. and N. J. Gonedes (1974). A comparison of the stable and student distributions as statistical models for stock prices. Journal of Business 47, 244-280. 19. French, Kenneth R. and Richard Roll (1986). 'Stock Return Variances. The Arrival of Information and the Reaction of Traders.' Journal of Financial Economics 17, 5-26. 20. Bollerslev T. (1986). A generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307-327. 21. Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50, 987-1008. 22. Black, F. (1976), ”Studies of Stock Market Volatility Changes”, Proceedings of the American Statistical Association, Business and Economic Statistics Section, 177-181. 23. Engle, R. F. and V. K. Ng (1993), “Measuring and Testing the Impact of News on Volatility,' Journal of Finance 48, 1749-1778. 24. Nelson, D., (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach.” Econometrica, 59, 2347-2370. 25. Glosten, L. R., R. Jagannathan and D. Runkle (1993), ”On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance 48, 1779-1801. 26. Engle, R.F., D.M. Lilien and R.P. Robins (1987), “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M model”, Econometrica, 55, 391-407. 27. Zakoian, J., (1994), 'Threshold Heteroskedastic Model, ' Journal of Economic Dynamics and Control, 18, 931-955. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24034 | - |
| dc.description.abstract | 本文採用AV-GARCHM模型伴隨不同的報酬結構－ARMA(1,1)、AR(1)、MA(1)、In-Mean－來檢測其於金融控股公司VaR預測值上的表現。我們沿用2003年王所模擬的兩個投資組合，以逐日計價原則計算每日P&L值. 並據此分別估算99%與95%信賴水準下的領先一天VaR預測值。在違約次數的合格性與資本提列有效性的考量下，本研究有以下發現：
1. 在99%信賴水準下，四個VaR預測模型都只發生一次違約，小於 Basel規定的2次；在95%信賴水準下，四個VaR預測模型在投資組合A都產生2個違約次數而投資組合B僅產生1個違約次數；以損失的超出次數為基準，此四個VaR預測模型均可視為合格的內部VaR市場風險模型。 2. AV-GARCHM模型同時考慮了訊息的平移與旋轉效果，理論上，我們假設其應當優於僅考慮旋轉效果的EGARCHM模型以及僅考慮平移效果的NA-GARCHM模型。然而在既定的模擬組合與資料期間下，除了ARMA(1,1)模型，我們並無法明確判斷其他三個模型相對於EGARCHM以及NA-GARCHM模型優越。 | zh_TW |
| dc.description.abstract | In this paper, we employ the AV-GARCHM model with various mean equations to evaluate their performance as VaR forecast models. We form two simulated portfolios, and calculate their daily profit and loss based on marking to market rule. Forward testing of one-day-head VaR models under 99% and 95% confidence level is evaluated with realized P&L of two simulated portfolios. Based on the consideration of violation number and capital charge efficiency, we have the following findings:
1. All of the four models generate only 1 violation number under 99% confidence level and 2 violations in portfolio A and 1 violations in portfolio B under 95% confidence level. 2. AV-GARCHM model considers both shift and rotation effect to news shock. Theoretically, we assume it should be better than EGARCHM model and NA-GARCHM model. However, except ARMA (1, 1) model, all rest models perform equally better in terms of violation number in both portfolio A and portfolio B. Thus, we cannot say that AV-GARCHM model is absolutely better than EGARCHM model or NA-GARCHM model. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T05:14:32Z (GMT). No. of bitstreams: 1 ntu-95-R93723005-1.pdf: 699278 bytes, checksum: d6ead073ca7efb38f208330469c2533d (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | Chapter 1 Introduction...............................1
1.1 Motivation…………………………………………………1 1.2 Purposes……………………………………………………2 1.3 Framework………………………………………………… 3 Chapter 2 Market Risk Management of Basel…………… 4 2.1 Market Risk Valuation Methods ………………… ...4 2.2 The Basel Committee…………………………………….4 2.3 The Basel Market Risk Charges ………………………6 Chapter 3 Literature Review ………………………………9 3.1 Value at Risk ……………………………………………9 3.2 Modeling Volatility…………………………………… 12 3.3 Related Literatures………………………………… …15 Chapter 4 Data and Assumptions ………………………… 20 4.1 Assumptions ………………………………………………20 4.2 Components of Portfolios………………………………21 4.3 Data Period……………………………………………… 23 Chapter 5 Methodology …………………………………… 24 5.1 Value at Risk Models………………………………… 24 5.2 Establishing Forecasting Model………………………28 Chapter 6 Empirical Results……………………………… 29 6.1 Time Series Pattern of Daily P&L…………………… 29 6.2 Testing Results of VaR Models…………………………30 Chapter 7 Conclusions…………………………………… …33 Reference………………………………………………………… 35 Figure 1 Daily P&L Distribution of Portfolio A…… …38 Figure 2 Daily P&L Distribution of Portfolio B…………38 Figure 3 ARMA(1,1)-AVGARCHM(1,1) VaR in Portfolio A under 99% Confidence Level…………………………… ………………39 Figure 4 ARMA(1,1)-AVGARCHM(1,1) VaR in Portfolio A under 95% Confidence Level………………………….……...………..39 Figure 5 AR(1)-AVGARCHM(1,1) VaR in Portfolio A under 99% Confidence Level ………………………………………………… 40 Figure 6 AR(1)-AVGARCHM(1,1) VaR in Portfolio A under 95% Confidence Level ………………………………………………… 40 Figure 7 MA(1)-AVGARCHM(1,1) VaR in Portfolio A under 99% Confidence Level ………………………………………………… 41 Figure 8 MA(1)-AVGARCHM(1,1) VaR in Portfolio A under 95% Confidence Level ………………………………………………… 41 Figure 9 In Mean-AVGARCHM(1,1) VaR in Portfolio A under 99% Confidence Level………………………….………………… 42 Figure 10 In Mean-AVGARCHM(1,1) VaR in Portfolio A under 95% Confidence Level………………………………………………42 Figure 11 ARMA(1,1)-AVGARCHM(1,1) VaR in Portfolio B under 99% Confidence Level………………………………… …43 Figure 12 ARMA(1,1)-AVGARCHM(1,1) VaR in Portfolio B under 95% Confidence Level …………………………………….43 Figure 13 AR(1)-AVGARCHM(1,1) VaR in Portfolio B under 99% Confidence Level ………………………………………….. 44 Figure 14 AR(1)-AVGARCHM(1,1) VaR in Portfolio B under 95% Confidence Level ………………………………………….. 44 Figure 15 MA(1)-AVGARCHM(1,1) VaR in Portfolio B under 99% Confidence Level…….... ………………………………….45 Figure 16 MA(1)-AVGARCHM(1,1) VaR in Portfolio B under 95% Confidence Level…………………………………………… 45 Figure 17 In Mean-AVGARCHM(1,1) VaR in Portfolio B under 99% Confidence Level………………………………………………46 Figure 18 In Mean-AVGARCHM(1,1) VaR in Portfolio B under 95% Confidence Level…………..………………………………. 46 Figure 19 AVGARCHM VaRs in Portfolio A under 99% Confidence Level……………………………………………………47 Figure 20 AVGARCHM VaRs in Portfolio A under 95% Confidence Level…... ……………………………………………47 Figure 21 AVGARCHM VaRs in Portfolio B under 99% Confidence Level……………………………………………………48 Figure 22 AVGARCHM VaRs in Portfolio B under 95% Confidence Level……………………………………………………48 Table 1 Summary for operational income and net profit-and-loss for subsidiaries in Portfolio A ………………………49 Table 2 Summary for operational income and net profit-and-loss for subsidiaries in Portfolio B ………………………49 Table 3 Size and allocation of portfolio A among categories of investment asset……………………………… 50 Table 4 Size and allocation of portfolio A among categories of investment asset ………………………………50 Table 5 The percentage of asset allocation for portfolio A and B………………………………………………………………50 Table 6 Position Details for Portfolio A …………… 51 Table 7 Position Details for Portfolio B……………… 54 Table 8 Summary statistics of actual daily profit and loss for the two simulated portfolios from November 28th 2000 to April 15th 2003…………………………………………57 Table 9 Statistics summary of VaR in ARMA (1, 1)-AVGARCHM (1, 1)……………………………………………………………… 58 Table 10 Parameters estimated in ARMA (1, 1)-AVGARCHM (1, 1) ……………………………………………………………………58 Table 11 Statistics summary of VaR in In-Mean + AVGARCHM (1, 1)…………………………………………………………… 59 Table 12 Parameters estimated in In-Mean + AVGARCHM (1, 1)…………………………………………………………………… 59 Table 13 Statistics summary of VaR in AR (1)-AVGARCHM (1, 1)…………………………………………………………………… 60 Table 14 Parameters estimated in AR (1)-AVGARCHM (1, 1)…………………………………………………………………… 60 Table 15 Statistics summary of VaR in MA (1)-AVGARCHM (1, 1)…………………………………………………………………… 61 Table 16 Parameters estimated in MA (1)-AVGARCHM (1, 1)……………………………………………………………………… 61 Table 17 All VaR models in Portfolio A……………………62 Table 18 All VaR models in Portfolio B……………………62 | |
| dc.language.iso | en | |
| dc.subject | 市場風險值 | zh_TW |
| dc.subject | 金融控股公司VaR | zh_TW |
| dc.subject | 巴塞爾協定 | zh_TW |
| dc.subject | Basel | en |
| dc.subject | AVGARCH | en |
| dc.subject | VaR | en |
| dc.subject | Violation Number | en |
| dc.title | AV-GARCHM模型於金融控股公司市場風險值之研究 | zh_TW |
| dc.title | AV-GARCHM Model in Value-at-Risk of Financial Holdings | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 胡星陽,王耀輝 | |
| dc.subject.keyword | 市場風險值,金融控股公司VaR,巴塞爾協定, | zh_TW |
| dc.subject.keyword | AVGARCH,VaR,Violation Number,Basel, | en |
| dc.relation.page | 64 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2006-07-04 | |
| 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 | |
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