跳到主要內容

臺灣博碩士論文加值系統

(3.233.217.106) 您好!臺灣時間:2022/08/17 12:28
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:林帛靜
研究生(外文):Bor-Jing Lin
論文名稱:期貨市場報酬分配之厚尾型態與風險值衡量模式之探討:臺灣臺指期貨與新加坡摩根臺指期貨
論文名稱(外文):Fat Tails and Value-at-Risk Analysis of Taiwan Stock Index Futures Markets: TAIFEX V.S. SGX-DT
指導教授:黃玉娟黃玉娟引用關係
指導教授(外文):Yu-Chuan Huang
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:財務管理所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:66
中文關鍵詞:風險值APARCH模型Conditional VaR-x 法尾部指數
外文關鍵詞:Value at RiskTail indexAPARCH ModelConditional VaR-x Approach
相關次數:
  • 被引用被引用:5
  • 點閱點閱:509
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:4
中文提要
雖然風險值模型不斷地引領更新,然而目前並無一最適的風險值估測模型可應用於不同的金融市場與金融資產上。而使用風險值估測市場風險之準確度,關鍵在於是否能夠有效捕捉資產報酬分配之厚尾型態。本文採用Huisman et al.(1997)所提出之Modified Hill-estimator來估計尾部指數以量測期貨市場報酬分配的厚尾特性,並進一步觀察可能存在之極端負報酬的情形。
由於臺灣期交所臺指期貨市場(TAIFEX)及新加坡摩根臺股指數期貨市場(SGX-DT)確實具有厚尾高狹的特性,因此本文嚐試利用以極值理論來量測報酬分配厚尾程度並適用於小樣本資料特性的Conditional VaR-x法與可避免扭曲資料特性並能納入報酬波動性之槓桿效果的APARCH模型,針對TAIFEX與SGX-DT臺指期貨風險值模型之適用性進行探討,並與目前較為廣泛採用之歷史模擬法與RiskMetrics模型作為比較的基礎,最後並針對模型的保守性、正確性與效率性等多元化的指標來評估各個模型之預測績效。
本文之實證結果顯示結合Student-t分配之APARCH-t模型有較佳的預測績效,其於0.1%信賴水準條件下的TAIFEX與SGX-DT臺指期貨市場皆有相當不錯的表現。Conditional VaR-x法則是於信賴水準愈趨嚴格的條件下,愈能捕捉報酬分配的厚尾特性,整體而言,Conditional VaR-x法之估計結果最為穩健,較適用於小樣本資料及報酬分配具有厚尾特性之金融市場。
ABSTRACT
The facts that the distribution of TAIFEX and SGX-DT Taiwan stock index futures returns exhibit fat tails. This paper applies the Modified Hill estimator to obtain tail index estimates and to examine the fat-tails of these stock index futures returns. Various VaR techniques are investigated to capture the nature of downside risk and provide a more suitable methodology for risk management.
We consider four classes of VaR model: Historical simulation approach, RiskMetrics Model, APARCH Model and Conditional VaR-x approach. The VaR models’ performance assessments are based on a range of measures that address the conservatism, accuracy and efficiency.
We find that the APARCH Model combined with Student-t distribution provides the better forecasting performances under 0.1% confidence level. The Conditional VaR-x Approach offers more robust VaR forecasting estimates and capable to capture the fat tails of these returns’ distributions at higher confidence level in relatively small samples.
目 錄

中文提要i
英文提要ii
誌謝iii
目錄iv
表目錄v
圖目錄vi
附錄-表目錄vii
附錄-圖目錄viii
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3
第三節 研究流程與架構4
第二章 文獻回顧7
第三章 研究方法13
第一節 尾部指數之估計13
第二節 風險值之估計方法15
第三節 風險值模型之評比24
第四章 實證結果與分析32
第一節 資料來源與分析32
第二節 APARCH模型參數之估計結果35
第三節 風險值模型之實證結果38
第五章 結論與建議44
第一節 研究結論44
第二節 研究建議45
參考文獻48
附錄53
參考文獻一、中文1.王君文,2001,極值理論風險值評估模式之探討,國立中正大學財務金融研究所,碩士論文。2.李存修,陳若鈺,2000,“台灣股匯市風險值(VaR)模型之估計、比較與測試”,金融財務,5卷,頁51-75。3.沈大白,柯瓊鳳,鄒武哲,1998,“風險值衡量模式之探討─以台灣上市公司權益證券為例”,東吳經濟商學學報,22卷,頁57-76。4.紀舒文,2000,VaR風險管理之保守性、精確度與效率性研究,國立台灣大學商學研究所,碩士論文。5.康倫年,1999,Value at Risk與無母數方法,國立台灣大學財務金融學研究所,碩士論文。6.陳文華,王佳真,吳壽山,1999,“風現值方法之比較”,證券市場發展季刊,11卷,1期,頁139-162。7.陳宜玫,2000,風險值估測模型之研究:以台灣股票市場為例,義守大學管理研究所,碩士論文。8.盧陽正,2000,“考量厚尾分配誤差修正之涉險值拔靴複製估計─以亞洲新興股市投資組合為實證”,證券市場展季刊,12卷,2期,頁1-28。9.謝家和,1999,風險值之衡量-多元變數GARCH模型之應用,國立暨南大學國際企業學研究所,碩士論文。二、英文1.Barone-Adesi, G. and Kostas, G., 2000, “Non-parametric VaR techniques. Myths and Realities.”, Working paper.2.Beder, T. S., 1995, “VAR: Seductive but Dangerous”, Financial Analysts Journal, September-October, pp. 12-24.3.Beirlant, J., P. Vynckier and J. Teugels, 1996, “Tail index Estimation, Pareto Quantile Plots and Regression Diagnostics”, Journal of the American Statistical Association, 91, pp. 1659-1667.4.Black, F., 1976, “Studied in Stock Price Volatility Changes”, Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177-181.5.Bollerslev, T., 1986, “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31, pp. 307-327.6.Christoffersen, P., 1998, “Evaluating Interval Forecasts”, International Economic Review, 39, pp. 841-862.7.Dacorogna, M., Miller, U., Pictet, O., and de Vries, C., 1995, “The Distribution of Extremal Foreign Exchange Rate Returns in Extremely Large Data Sets”, Tinbergen Institute Discussion Paper #95-70.8.Danielsson, J. and C.G. de Vries, 1997, “Value-at-Risk and Extreme Returns”, Working Paper, London School of Economics.9.Danielsson, J. and C.G. de Vries, 1998, “Beyond the Sample: Extreme Quantile and Probability Estimation”, Tinbergen Institute Discussion Paper TI 98-016/2.10.Ding, Z., C.W.J. G. and R.F. Engle, 1993, “A Long Memory Property of Stock Market Returns and A New Model”, Journal of Empirical Finance, 1, pp. 83-106.11.Duffie, D. and Pan, J., 1997, “An Overview of Value at Risk,” Journal of Derivatives, 4, pp. 7-49.12.Engle, R., 1982, “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation”, Econometrica, 50, pp. 987-1008.13.Engel, J. and M. Gizycki, 1999, “Conservatism, Accuracy and Efficiency: Comparing Value-at-Risk Models”, Working paper.14.Fisher, R. A. and L. H. C. Tippett, 1928, “Limiting Forms of the Frequency Distribution of the Largest or Smallest Member of a Sample”, Proceeding of the Cambirdge Philosophical Society, 24, pp. 180-190.15.Geweke, J., 1986, “Comment”, Econometric Review, 5, pp. 57-61.16.Giot, P. and S. Laurent, 2001, “Value-at-Risk for Long and Short Trading Positions”, Working paper.17.Glosten, L. R., R. J. 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, 48, pp. 1779-1801.18.Goorbergh, R. V. D. and P. Vlaar, 1999, “Value-at-Risk Analysis of Stock Returns Historical Simulation, Variance Techniques or Tail Index Estimation?”, Working paper.19.Guillaume D. M., M. M. Dacorogna, R. R. Dave, U. A. Muller, R. B. Olsen, O. V. Pictet, 1997, “From the Bird’s Eye to the Microscope: A Survey of New Stylized Facts of the Intra-daily Foreign Exchange Markets”, Finance and Stochastics, 1, pp. 95-129.20.Hendricks, D., 1996, “Evaluation of Value-at-Risk Models Using Historical Data”, Economic Policy Review, Federal Reserve Bank of New York, 2, pp. 39-69.21.Hentschel, L., 1995, “All in the Family: Nesting Symmetric and Asymmetric GARCH Models”, Journal of Financial Economics, 30, pp. 71-104.22.Higgins, M. L. and A. K. Bera, 1992, “A Class of Nonlinear ARCH Models”, International Economic Review, 33, pp. 137-158.23.Hill, B.M., 1975, “A Simple General Approach to Inference About The Tail of A Distribution”, Annals of Statistics, 3, pp. 1163-1174.24.Huisman, R., K. Koedijk, C. Kool, and F. Palm, 1997, “Fat Tail in Small Samples”, Working Paper, Limburg Institute of Financial Economics, Maastricht University.25.Huisman, R., K. Koedijk, C. Kool, and F. Palm, 1998, “The Fat-tailedness of FX Returns”, Working paper, Limburg Institute of Financial Economics, Maastricht University.26.Huisman, R., K.G. Koedijk , and R. A. J. Pownall, 1998, “VaR-x:Fat Tails in Financial Risk Management”, Journal of Risk, pp. 47-61.27.J. P. Morgan, 1996, RiskMetrics Technical Document, 4th edition. New York.28.Jackson, P., D.J. Maude and W. Peerraudin, 1997, “Bank Capital and Value at Risk”, Journal of Derivatives, pp. 73-89, Spring.29.Jansen, D. and C. G. de Vries, 1991, “On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective”, The Review of Economics and Statistics, 73, pp. 18-24.30.Jorion, P., 2000, Value at Risk: The New Benchmark for Controlling Market Risk,2nd edition, McGraw-Hill.31.Kearns, P. and A. Pagan, 1997, “Estimating the Density Tail Index for Financial Time Series”, The Review of Economics and Statistics, 79, pp. 171-175.32.Kupiec, P., 1995, “Techniques for Verifying the Accuracy of Risk Measurement Models”, Journal of Derivatives, 2, pp. 73-84.33.Lopez, J., 1998, “Methods for Evaluating Value-at-Risk Estimates”, FRBNY Economic Policy Review, pp. 119-124.34.Loretan, M. and P. Phillips, 1994, “Testing the Covariance Structure of Heavy-Tailed Time Series”, Journal of Empirical Finance, 1, pp. 211-248.35.McKenzie, M. D., H. Mitchell, R. D., Brooks and R. W. Faff, 1998, “Power ARCH Modeling of Commodity Futures Data on the London Metal Exchange”, Working paper.36.Mittnik, S. and M. S. Paolella, 2000, “Conditional Density and Value-at-Risk Prediction of Asian Currency Exchange Rates”, Journal of Forecasting, 19, pp. 313-333.37.Nelson, D. B., 1991, ”Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, 59, pp. 347-370.38.Pakfk, S. and K. Imre, 2001, “Evaluating the RiskMetrics Methodology in Measuring Volatility and Value-at-Risk in Financial Markets.”, Physica A, 299, pp. 305-310.39.Pantula, S. G., 1986, “Comment”, Econometric Review, 5, pp. 71-73.40.Pictet, O., Dacorogna, M. and Miller, U., 1996, “Hill, Bootstrap and Jackknife Estimators for Heavy Tails”, Olsen & Associates Working Paper.41.Pownall, R.A. and Koedijk K.G., 1999, “Capturing Downside Risk in Financial Markets: The Case of The Asian Crisis”, Journal of International Money and Finance, 18, pp. 853-870.42.Schwert, G.W., 1989, “Why Does Market Volatility Change over Time”, Journal of Finance, 5, pp. 1115-1153.43.Taylor, S., 1986, Modeling Financial Time Series, John Wiley and Sons, New York.44.Terasvirta, T., 1996, “Two Stylized Facts and the GARCH(1,1) Model”, Working paper 96, Stockholm School of Economics.45.Tse, Y.K. and A. K.C. Tsui, 1997, “Conditional Volatility in Foreign Exchange Rates: Evidence from the Malaysian Ringgit and Singapore Dollar”, Pacific-Basin Finance Journal, 5, pp. 345-356.46.Zakoian, J.-M., 1991, “Threshold Heteroskedastic Models”, Unpublished paper, Institut National de la Statistique et des Etudes Economiques, Paris.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 吳昭原、張友珊(民88)。牙醫門診總額試辦計畫各分區委員意見訪談結果。醫院,32(5)''44-55。
2. 宋文娟、藍忠孚、洪錦墩(民90)。內科專科醫師人力問題之剖析一美國V.S台灣。醫務管理期刊,2(1),21-31。
3. 李玉春(民87b)。全民健保支付制度實施現況檢討與改革方向建議。政策月刊,35,14-16
4. 季麟揚(民80)。台灣地區民國78年至80年牙醫師人力之分布與變遷。衛生報導,7(8),12-16。
5. 黃彥聖(民88)。診所在總額預算下如何調整經營。中華牙醫學會訊,144,47-49。
6. 楊全斌(民86)。醫療費用的抑制與總額預算制。牙醫界,16(5),67-73。
7. 馬培卿(民87)。德國健康保險制度及其對我國的啟示。醫院,31(1),21-28。
8. 呂文峰(民85)。加拿大健康保險財務收支分析。社區發展季刊,76,265-276。
9. 洪碧蘭、楊志良(民87)。健保支付與醫界生態關係之初探。醫院, 37(6),41-60。
10. 高森永(民88)。我國醫師人力的現況與展望。國防醫學,28(2)'' 100-104o
11. 張友珊(民88a)。牙醫門診總額預算第二年費用之協商與省思。醫院, 52(3)''1-9。
12. 張友珊(民88b)。荷蘭總額預算醫療費用協定制度之探討。醫院, 32(1),1-6。
13. 張慈桂、李燕鳴、蕭正光(民87)。全民健康保險實施後花蓮偏遠地區民眾醫療可近性探討。慈濟醫學,10(3),201-209。
14. 楊志良、蕭慶倫、盧瑞芬(民79)。從全民健康保險看我國醫療保健體系。公共衛生,16(4),341-357。
15. 詹啟賢(民87)。健保多元改革、體制永續發展。政策月刊,35,2-3。