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https://ah.lib.nccu.edu.tw/handle/140.119/49959
題名: | 不對稱分配於風險值之應用 - 以台灣股市為例 An application of asymmetric distribution in value at risk - taking Taiwan stock market as an example |
作者: | 沈之元 Shen,Chih-Yuan |
貢獻者: | 毛維凌 Mao,Wei-Ling 沈之元 Shen,Chih-Yuan |
關鍵詞: | 風險值 極值理論 skew-t 分配 回溯測試 Value at Risk Extreme Value Theory asymmetric exponential power distribution Back-testing |
日期: | 2008 | 上傳時間: | 9-Dec-2010 | 摘要: | 本文以台灣股價加權指數,使用 AR(3)-GJR-GRACH(1,1) 模型,白噪音假設為 Normal 、 Skew-Normal 、 Student t 、 skew-t 、 EPD 、 SEPD 、與 AEPD 等七種分配。著重於兩個部份,(一) Student t 分配一族與 EPD 分配一族在模型配適與風險值估計的比較;(二) 預測風險值區分為低震盪與高震盪兩個區間,比較不同分配在兩區間預測風險值的差異。\n\n實證分析顯示, t 分配一族與 EPD 分配一族配適的結果,無論是只考慮峰態 ( t 分配與 EPD 分配) ,或者加入影響偏態的參數 ( skew-t 分配與 SEPD 分配) , t 分配一族的配適程度都較 EPD 分配一族為佳。更進一步考慮分配兩尾厚度不同的 AEPD 分配,配適結果為七種分配中最佳。\n\n風險值的估計在低震盪的區間,常態分配與其他厚尾分配皆能通過回溯測試,採用厚尾分配效果不大;在高震盪的區間,左尾風險值回溯測試結果,常態分配與其他厚尾分配皆無法全數通過,但仍以 AEPD 分配為最佳。最後比較損失函數,左尾風險值估計以 AEPD 分配為最佳,右尾風險值則無一致的結果。因此我們認為 AEPD 分配可作為風險管理有用的工具。 | 參考文獻: | Angelidis, T. , A. Benos and S. Degiannakis (2007), ”A robust VaR model under different time periods and weighting schemes.” Review of Quantitative Finance and Accounting, Springer, 28(2), 187-201. Blanco, C. and G. Ihle (1999), ”How good is Your VaR? Using Backtesting to Assess System Performance.” Financial Engineering News , August , 11 , 1-4. Bollerslev, T. (1986), ”Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics, 31 , 307-327. Christoffersen, P. (1998), ”Evaluating Interval Forecasts.” International Economic Review, 39, 841-862. Engle, R. F. (1982), ”Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation.” Econometrica, 50 , 987-1008. Ghorbel, A. and A. Trabelsi (2007), ”Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation.” MPRA Working Paper. Gilli, M. and E. Kellezi (2006), ”An application of extreme value theory for measuring financial risk.”, Computational Economics, 27, 1-23. 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, 48, No.5, 1779-801. Hansen, B. E. (1994), ”Autoregressive Conditional Density Estimation.” International Economic Review, 35 , 705-730. Hendricks, D. (1996), ”Evaluation of value-at-risk models using historical data.” Economic Police Review, 2, 39-70. Hull, J. and A. White (1998), ”Incorporating volatility updating for value-at-risk.” Journal of Risk, 1 , 5-19. Jondeau, E. , S. H. Poon and M. Rockinger (2007), ”Financial Modeling Under Non-Gaussian Distributions.” , Springer Finance Kupiec, P. (1995), ”Techniques for Verifying the Accuracy of Risk Management Models.” Journal of Derivatives, 3, 73-84. Lopez, J.A. (1999), ”Methods for Evaluating Value-at-Risk Estimates.” , Federal Reserve Bank of San Francisco Economic Review, 2, 3-17. Marimoutou, V. ,B. Raggad, and A. Trabelsi (2006), ”Extreme value theory and value at risk: application to oil market.” GREQAM Working Paper 2006-38 McNeil, A.J, and R. Frey (2000), ”Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach.” Journal of Empirical Finance , 7 , 271-300. Theodossiou, P. (2000), ”Skewed Generalized Error Distribution of Financial Assets and Option Pricing.” SSRN Working Paper Tsay, R. S. (2005), ”Analysis of Financial Time Series.”, 2nd ,Wiley. Zhu, D. and V. Zinde-Walsh (2009), ”Properties and estimation of asymmetric exponential power distribution.” Journal of Econometrics, 148 , 86-99. |
描述: | 碩士 國立政治大學 經濟研究所 96258009 97 |
資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0096258009 | 資料類型: | thesis |
Appears in Collections: | 學位論文 |
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