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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇永成 | |
| dc.contributor.author | Yun-Ju Lin | en |
| dc.contributor.author | 林韻茹 | zh_TW |
| dc.date.accessioned | 2021-06-13T16:54:51Z | - |
| dc.date.available | 2008-07-04 | |
| dc.date.copyright | 2005-07-04 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-06-12 | |
| dc.identifier.citation | 1. Berkowitz, Jeremy, and O’Brien, James, 2002, “How Acurate Are Value-at-Risk
Models at Commercial Banks？, ” Journal of Finance, 57, 1093-1112. 2. BIS, Basle Committee on Banking Supervision, 1988, “Internanational Convergence of Capital Measurement and Capital Standards.” 3. BIS, Basle Committee on Banking Supervision, 1996, “Supervisiory Frameworkl for the Use of Back-testing in Conjunction with Internal Models Approach to Market Risk Capital Requirements.” 4. BIS, Basle Committee on Banking Supervision, 1996 updated to 1998, “Amendment to the Capital Accord to Incorporate Market Risks.” 5. Bollerslev, Tim, 1987, “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return,” The Review of Economics and Statistics, Vol. 69, No. 3., 542-547. 6. Chiang, 2004, “Modeling Value-at-Risk of Financial Companies---A Comparison of Symmetric and Asymmetrci Models,” Master Thesis of Graduate Institute of Business Administration in National Taiwan University. 7. Engel, Robert F., Lilien, David M., and Robins, Russell P., 1987, “Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model,” Econometrica, 55, 391-407. 8. Glosten, Lawrence R., Jagannathan, Ravi, and Runkle, David E., 1993, “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48, 1179-1801. 9. Hendricks, Darryl, 1996, “Evaluation of Value-at-Risk Models Using Historical Data,” Economic Policy Review, April, 36-69. 10. Hentschel, Ludger, 1995, “All in the Family Nesting Symmetric and Asymmetric GARCH Models,” Journal of Financial Economics, 39, 71-104. 11. Jorion, Philippe, 2001,”Value-at-Risk：The New Benchmark for Controlling Market Risk, ” 2nd edition. 12. Kupiec, Paul, 1995, “Techniques for Verifying the Accuracy of Risk Measurement Models,” Journal of Derivatives, 3, 73-84. 13. Marshall, Chris, and Siegal, Michael, 1997, “Value-at-Risk：Implementing a Risk Measurement Standard,” Journal of Derivatives, 4, 91-111. 14. Pritsker, Matthew, 1997, “Evaluating Value-at-Risk Methodologies：Accuracy versus Computational Time,” Journal of Financial Services Research, 12, 201-242. 15. Saunders, Anthony, 2000, “Financial Institutions Management ：A Modern Perspective,” 3rd edition, 180-201. 16. Wang, 2003, “Market Risk VaR Models for Financial Holding Company,” Master Thesis Graduate Institute of Business Administration in National Taiwan University. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38966 | - |
| dc.description.abstract | 近年來金融環境變化迅速，由於新金融商品的開放及金融機構間激烈的競爭，金融機構無不增加其交易活動以改善其獲利，隨之也帶來顯著的市場風險。市場風險值（Value-at-Risk，VaR）目前已經成為衡量金融機構市場風險的標準方法。
本研究導入一不對稱GARCH模型－GJR-GARCH模型，來計算市場風險值，探討使用GJR-GARCH模型是否可以更精確地衡量金融機構的市場風險。由於缺乏實際的每日交易損益資料，我們模擬兩個投資組合A和B，分別代表富邦和國泰世華金融控股公司。模擬資料持有期間從2000年11月28日 至 2003年4月15日，其中400筆資料點當作樣本用來估計參數，而剩下的資料點則用來比較模型估計出之市場風險值，看其預測能力如何。 我們發現使用GJR-GARCH模型可有效預測市場風險值，然而用作比較之對稱GARCHM模型也同樣具有良好的預測能力，和先前研究相比，似乎並沒有存在槓桿效果。我們更進一步每隔五日更新所估計之參數，在不同的模型比較下，我們發現更新參數可使模型衡量更為精確。但是不可諱言的，每日交易損益超出市場風險值的次數和資本計提負擔之間必須互相取捨，不可兼得。 | zh_TW |
| dc.description.abstract | Financial environment has changed more rapidly in recent years. Due to the opening of innovative financial products and vigorous competition, the trading activities are increasing and produce significant market risk. Value-at-Risk（VaR）now has become a standard measure of financial market risk.
In this study, we introduce an asymmetric GARCH model, GJR-GARCH, in Value-at-Risk. We want to see if GJR-GARCH is a better method to evaluate the market risk of financial holdings. Because of lacking the actual daily P＆L data, we simulated portfolio A and B, representing FuBon and Cathay financial holdings. The holding period stretches from 2000/11/28 to 2003/4/15. We take 400 observations as Sample Group to do the backward test and use the rest observations to forecast the change of VaR We find GJR-GARCH works very well in VaR forecasting. However, it also performs very well under the symmetric GARCHM model. There seems no leverage effect as the previous study. Further, we open a 5-day moving window to update parameter estimates. Comparing the results under different models, we find that the model is more accurate by updating parameter estimates. It is a trade-off between violations and capital charges | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T16:54:51Z (GMT). No. of bitstreams: 1 ntu-94-R92723010-1.pdf: 1059508 bytes, checksum: be5ae168fbbe0df6861d96a565c06e9d (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | Contents
Chapter 1 Introduction...........................................................................................1 1.1 Motivations and Purposes ................................................................................1 1.2 Framework .......................................................................................................4 Chapter 2 Basle Ⅱ and Market Risk ..................................................................6 2.1 Basle Ⅱ...........................................................................................................6 2.2 Market Risk Amendment .................................................................................8 2.3 Trading Book Issues in Basle Ⅱ.....................................................................9 Chapter 3 Literature Review ...............................................................................11 Chapter 4 Data ......................................................................................................14 4.1 Assumptions by Wang（2003）....................................................................15 4.2 Portfolio Formation........................................................................................16 4.3 Holding Period and Daily P&L......................................................................17 Chapter 5 Methodology........................................................................................19 5.1 GJR-GARCHM (1,1).....................................................................................20 5.2 GARCHM (1,1) .............................................................................................21 Chapter 6 Empirical Results................................................................................23 6.1 Backward Test................................................................................................23 6.2 Value-at-Risk in GJR-GARCHM...................................................................25 6.3 Value-at-Risk under GARCHM.....................................................................27 6.4 Model Comparison.........................................................................................27 Chapter 7 Conclusion ...........................................................................................28 References...................................................................................................................30 Figures Figure 1...................................................................................................................32 Structure of Basle Ⅱ Figure 2...................................................................................................................33 Distribution of Daily P&L of Portfolio A（2000/11/28－2003/4/15） Figure 3...................................................................................................................33 Distribution of Daily P&L of Portfolio A（2000/11/28－2003/4/15） Figure 4...................................................................................................................34 P＆L Returns and VaR of Portfolio A under ARMA(1,1)-GJR GARCHM(1,1) Figure 5...................................................................................................................35 P＆ L Returns and VaR of Portfolio B under ARMA(1,1)-GJR GARCHM(1,1) Figure 6...................................................................................................................36 P＆L Returns and VaR of Portfolio A under AR(1)-GJR GARCHM(1,1) Figure 7...................................................................................................................37 P＆L Returns and VaR of Portfolio B under AR(1)-GJR GARCHM(1,1) Figure 8...................................................................................................................38 P＆L Returns and VaR of Portfolio A under MA(1)-GJR GARCHM(1,1) Figure 9...................................................................................................................39 P＆L Returns and VaR of Portfolio B under MA(1)-GJR GARCHM(1,1) Figure 10..................................................................................................................40 P＆L Returns and VaR of Portfolio A under Different Modles in ARMA(1,1) at 99% confidence level Figure 11..................................................................................................................41 P＆L Returns and VaR of Portfolio A under Different Modles in ARMA(1,1) at 95% confidence level Figure 12..................................................................................................................42 P＆L Returns and VaR of Portfolio B under Different Modles in ARMA(1,1) at 99% confidence level Figure 13..................................................................................................................43 P＆L Returns and VaR of Portfolio B under Different Modles in ARMA(1,1) at 95% confidence level Figure 14..................................................................................................................44 P＆L Returns and VaR of Portfolio A under Different Modles in AR(1) at 99% confidence level Figure 15..................................................................................................................45 P＆L Returns and VaR of Portfolio A under Different Modles in AR(1) at 95% confidence level Figure 16..................................................................................................................46 P＆L Returns and VaR of Portfolio B under Different Modles in AR(1) at 99% confidence level Figure 17..................................................................................................................47 P＆L Returns and VaR of Portfolio B under Different Modles in AR(1) at 95% confidence level Figure 18..................................................................................................................48 P＆L Returns and VaR of Portfolio A under Different Modles in MA(1) at 99% confidence level Figure 19..................................................................................................................49 P＆L Returns and VaR of Portfolio A under Different Modles in MA(1) at 95% confidence level Figure 20..................................................................................................................50 P＆L Returns and VaR of Portfolio B under Different Modles in MA(1) at 99% confidence level Figure 21..................................................................................................................51 P＆L Returns and VaR of Portfolio B under Different Modles in MA(1) at 95% confidence level Tables Table 1...................................................................................................................52 Summary for Operational Income and Net Profit and Loss（P&L）for Subsidiaries in Fu Bon Financial Holding Company Table 2...................................................................................................................52 Summary for Operational Income and Net Profit and Loss（P&L）for Subsidiaries in Cathay Financial Holding Company Table 3...................................................................................................................53 Size and Allocation of Portfolio A Categorized by Investment Asset Classes Table 4...................................................................................................................53 Size and Allocation of Portfolio B Categorized by Investment Asset Classes Table 5...................................................................................................................53 Percentage of Investment Asset Allocation for Portfolio A and B Table 6...................................................................................................................54 Details of Positions and Investing Instruments for Portfolio A Table 7...................................................................................................................57 Details of Positions and Investing Instruments for Portfolio B Table 8...................................................................................................................59 P&L Returns Statistics Summary for Portfolio A and B （2000/11/28－2003/4/15） Table 9...................................................................................................................59 P&L Returns Statistics Summary for Sample Groups（2000/11/28－2003/3/1） Table 10..................................................................................................................60 Likelihood Ratio Test for Portfolio A and B Table 11..................................................................................................................61 Summery of Parameters for under GJR-GARCHM Table 12..................................................................................................................62 Summery of Parameters under GARCHM Table 13..................................................................................................................63 VaR Comparison under mean equation of ARMA(1,1) Table 14..................................................................................................................64 VaR Comparison under mean equation of AR(1) Table 15..................................................................................................................65 VaR Comparison under mean equation of MA(1) | |
| dc.language.iso | en | |
| dc.subject | 風險值 | zh_TW |
| dc.subject | 市場風險 | zh_TW |
| dc.subject | 金融控股公司 | zh_TW |
| dc.subject | GJR GARCH | zh_TW |
| dc.subject | GARCH | zh_TW |
| dc.subject | market risk | en |
| dc.subject | GARCH | en |
| dc.subject | GJR GARCH | en |
| dc.subject | value-at-risk | en |
| dc.subject | financial holdings | en |
| dc.title | GJR-GARCH模型於金融控股公司市場風險值之研究 | zh_TW |
| dc.title | GJR-GARCH Model in Value-at-Risk of Financial Holdings | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 胡星陽,何耕宇 | |
| dc.subject.keyword | 市場風險,金融控股公司,GJR GARCH,GARCH,風險值, | zh_TW |
| dc.subject.keyword | financial holdings,market risk,value-at-risk,GJR GARCH,GARCH, | en |
| dc.relation.page | 65 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-06-13 | |
| 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 | |
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