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研究生:翁雅蕾
研究生(外文):Ya-lei Weng
論文名稱:考慮波動時變性的風險值模型─應用一般化誤差分配
論文名稱(外文):Value at Risk (VaR) MODELS WITH TIME--VARYING VOLATILITY: THE APPLICATION OF GENERALIZED ERROR DISTRIBUTION
指導教授:張簡彰程張簡彰程引用關係
指導教授(外文):Chang-cheng Chang- chien
學位類別:碩士
校院名稱:南華大學
系所名稱:財務金融學系財務管理碩士班
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:2009
畢業學年度:97
語文別:中文
論文頁數:75
中文關鍵詞:風險值一般化誤差分配波動性模型
外文關鍵詞:Value-at-Risk (VaR)Volatility modelGeneralized Error Distribution (GED)
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  近幾年許多估計風險值(Value at Risk,VaR)的方法,實證研究發現多數皆有高狹峰與厚尾的現象,常態分配並不能確切地捕捉到實證資料的統計特徵和分佈情況,因此造成估計VaR產生偏誤的問題,而產生不當的資本計提。本研究採用具有厚尾分配特徵的一般化誤差分配以解釋財金資料之非對稱與高峰厚尾之現象,且在此厚尾分配下進行風險值之估計。最後並將厚尾分配下的風險值與Cornish Fisher 展開式及常態分配估計之風險值作模型績效之檢定與比較。本研究實證結果發現在99%與99.5%的信賴水準下,一般化誤差分配的風險值相對較常態分配的風險值績效表現為佳。一般化誤差分配下相對較常態分配來得保守並更能表達其尾端風險。
  Recently plentiful methodologies have been proposed in VaR modelling. The phenomena of leptokurtosis and skewness in high frequency financial series are evidently verified in empirical research, hence the using of normal distribution might lead to potential biases, capital adequacy for banks, for instance. Given the characteristics of generalized error distribution (GED) are heavy-tailed, we employ the general error distribution to estimate VaR therefore. Then, we compare the performance of VaR estimation under the heavy-tailed generalized error distribution, Cornish Fisher expansions and normal distribution.The empirical results reveal that under 99% and 99.5% confidence level, the performance of VaR estimation under GED distributions are better than that of normal distribution relatively. The empirical results also indicate the GED distributions are relatively more conservative than that of normal distribution.
論文口試委員審定書 ii
中文摘要 iv
英文摘要 v
目錄 vi
表目錄 vii
圖目錄 viii
  
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 論文架構 4
  
第二章 文獻回顧 7
第一節 風險值定義 7
第二節 波動性估計模型文獻探討 9
第三節 風險值估計模型文獻探討 12
第四節 文獻總結 14
  
第三章 研究方法 15
第一節 研究樣本 15
第二節 波動預測模型 15
第三節 風險值估計模型 18
第四節 模型績效評估方法 20
  
第四章 實證結果與分析 23
第一節 資料選取與說 23
第二節 樣本資料之敘述統計分析 23
第三節 風險值估計分析 25
第四節 風險值模型績效分析 44
  
第五章 結論與建議 69
第一節 結論 69
第二節 研究建議 71
  
參考文獻 72
中文部份
 
田瀅嫆(2006),「厚尾分配下風險值與ETL 探討 ─穩定分配與一般化誤差分配之應用」,銘傳大學財務金融學系碩士班碩士論文。
 
林明威(2005),「以SGT 模型探討選擇權之隱含機率分配:Using the Skewed Generalized t Model to Analyze the Implied Probability Distribution of Options」,台灣科技大學企業管理研究所碩士論文。
 
廖哲宏(2004),「條件與非條件分配型態設定對期貨避險績效影響之研究」,高雄第一科技大學財務管理學系碩士論文。
 
謝景成(2003),「應用結合一般化誤差分配與歷史模擬法之風險值估計模型以增進匯率風險管理之績效」,高雄第一科技大學財務管理學系碩士論文。
 
蒲建亨(2001),「整合VaR 法之衡量與驗證~以台灣金融市場投資組合為例」,國立政治大學國際貿易學系未出版碩士論文。
 
英文部份
 
Akgiray, V. (1989),”Conditional Heteroscedasticity in the Series of Stock Return Evidence and Forecasts,” Journal of Business, Vol.62, pp.55-80.
 
Alexander, C. and C. Leigh (1997),”On the Covariance Matrices Used in Value at Risk Models,” Journal of Derivatives, Vol.4,pp.50-62.
 
Barone-Adesi, G., F. Bourgoin and K. Giannopoulos (1998),”Don’t Look Back,” Risk, Vol.11, pp.100-104, August.
 
Bali T.G., S. Gokcan and B. Liang (2007),”Value at Risk and the Cross-Section of Hedge Fund Returns,” Journal of Banking & Finance, Vol.31, pp.1135-1166.
 
Beder, T.S. (1995),”VAR: Seductive but Dangerous,” Financial Analysts Journal, Vol.5,pp.12-24.
 
Billio, M. and L. Pelizzon (2000),”Value-at-Risk: A Multivariate Switching Regime Approach,” Journal of Empirical Finance, Vol.7, pp.531-554.
 
Bollerslev, T. (1986),”Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, Vol.31, pp.307-327.
 
Day, T. E. and C. M. Lewis (1992),”Stock Market Volatility and the Information Content of Stock Index Options,” Journal of Econometrics, Vol.52, pp.267-287.
 
Duarte, J. (1997),”Model Risk and Risk Management,” Derivatives Quarterly,pp.60-72, spring.
 
Engel, J. and M. Gizycki (1999),”Conservatism, Accuracy and Efficiency: Comparing Value-at-Risk Models,” Working Paper 2, March.
 
Engle, R. F. (1982),”Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of the United Kingdom Inflation,” Econometrica, Vol.50, pp.987-1007.
 
Fama, E. (1965a),”Behavior of Stock Market Prices,” Journal of Business, Vol.38, pp.34-105.
 

 
Florent, P. and T. Jerome (2007),”Empirical investigation of the VaR of hedge funds using daily data,” Derivatives Use, Trading & Regulation, Vol.12, pp.314–329.
 
Goorbergh, R.V.D. and P. Vlaar (1999),”Value-at-Risk Analysis of Stock Returns Historical Simulation, Variance Tchniques or Tail Index Estimation?,” Econometric Research and Special Studies Dept. De Nederlandsche Bank.
 
Guermat C. and R.D.F. Harris (2000),”Robust Conditional Variance Estimation and Value-at-Risk,” Journal of Risk, Vol.4, pp25-41.
 
Guermat C. and R.D.F. Harris (2002),”Forecasting Value at Risk allowing for Time Variation in the Variance and Kurtosit of portfolio Returns,” International Journal of Forecasting, Vol.18, pp409-419.
 
Hendricks, D. (1996),”Evaluation of Value-at-Risk Models Using Historical Data,” Economic Policy Review, Federal Reserve Bank of New York, pp.39-69, April.
 
Hsieh, D. (1989),”Modeling Heteroskedasticity in Daily Foreign Exchange Rates,” Journal of Business and Economics Statistics, Vol.7, pp.307-317.
 
Hull, J. and A. White, 1998,”Incorporating Volatility Updating into the Historical Simulation Method for Value at Risk,” Journal of Risk, Vol.1, pp.5-19.
 
Jaschke, S. R. (2002),”The Cornish-Fisher-Expansion in the Context of Delta-Gamma-Normal Approximations,” Journal of Risk, Vol.4, pp.33-52
 
J.P. Morgan(1995),”Risk MetricsTM,” Technical Document, fourth edition, New York.
 
Koutmos, G. (1999),”Asymmetric Price and Volatility Adjustments in Emerging Asian Stock Markets,” Journal of Business Finance and Accounting, Vol.26(1), pp.83-101.
 
Kupiec, P.H. (1995),”Techniques for Verifying the Accuracy of Risk Measurement Models,” The Journal of Derivatives, Vol.3, pp.73-84.
 
Mandelbrot, B. (1963),”The Variation of Certain Speculative Prices,” Journal of Business, Vol.36, pp.394-419.
 
Nelson, D. (1991),”Conditional Heteroscedasticity in Asset Returns: A New Approach,” Econometrica ,Vol.59, pp.347-370.
 
Vianelli, S. (1963),”La Misura della Variabilit`a Condizionata in uno Schema Generale delle Curve Normali di Frequenza,” Statistica, 23,pp.447–474.
 
Zangari, P., (1996),”An improved methodology for measuring VaR,” RiskMetrics Monitor, Reuters/JP Morgan.
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