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研究生:游雲翔
研究生(外文):Yo Yun-Shyang
論文名稱:金融資產分佈高階動差性質對風險值估計的影響-APARCH模型之應用及延伸
論文名稱(外文):The Effect of Distribution of Financial Assets at Higher Order Moments to VaR –Application of APARCH model
指導教授:王凱立王凱立引用關係
指導教授(外文):Wang Kai-Li
學位類別:碩士
校院名稱:東海大學
系所名稱:財務金融學系
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:2007
畢業學年度:95
語文別:中文
論文頁數:62
中文關鍵詞:風險值波動性非常態性質厚尾偏態高階動差
外文關鍵詞:GJR-GARCHAPARCHnon-normalityfat tailskewness
相關次數:
  • 被引用被引用:1
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本研究以亞洲四小龍(香港、南韓、新加坡與台灣)為研究對象,從金融資
產報酬的波動性與非常態性質兩個角度出發,依序探討這些現象對於風險值評估
績效的影響。就波動性而言,為觀察風險值評估績效是否與條件變異數模型對波
動性質的包容能力成正比,本研究比較GJR-GARCH 模型以及APARCH 模型的
風險值估計效能;就非常態性質而言,爲觀察金融資產報酬分佈潛在厚尾(fat tail)
與偏態(skewness)性質對風險值評估績效的影響,本研究將前述GARCH 族模
型分別架構在t 與偏斜t 分佈下,比較其與架構在常態分佈下的模型之風險值評
估績效。實證結果發現,常態分佈下的風險值模型雖能通過大部分顯著水準下的
失敗率檢測,但無法通過0.5%(1%)顯著水準下多部位風險值的失敗率檢測,
此現象說明了常態分佈無法切適描述厚尾分佈尾端特性之不足,不論是透過條件
變異數方程式設定上的改良,或是使模型背後分佈設定上的一般化,皆可緩和此
缺失。唯架構在t 分佈下的風險值模型於空部位風險值之評估上易引發過於保守
的問題,使實際失敗率往往低於理論失敗率甚多,造成其無法通過失敗率檢定的
測試。若採用架構在偏態t 分佈下的風險值模型,則可改善前述t 分佈假設潛在
的過於保守問題,說明分佈配適的重要性。
This research discusses how the behavior of the volatility and non-normality affects the performance of VaR by using the equity index data of East Asian Tigers(Hong Kong, Taiwan, Singapore and South Korea). I employ GJR-GARCH model and APARCH model to investigate whether the long memory characteristics play the significant role on VaR. Second, I replace the normal distribution assumption of GARCH family models with Student t and skewed Student t distribution to analyze the properties of higher order moments, specifically, fat tail and skewness, to VaR performance. Empirical result shows that the GARCH family models based on normal distribution has poor performance in the description of extreme left tail, which can be alleviated by considering a long-memory based conditional variance model or taking a more flexible distribution instead. Of interesting, the finding also indicates the potential conservative problem caused by the symmetrical distribution, such as fat-tail Student t distribution, could be improved by adopting an asymmetrical general distribution, such as skewed Student t distribution.
目 錄
第壹章、緒論............................................1
第1.1 節、研究動機......................................1
第1.2 節、研究目地......................................8
第1.3 節、研究架構......................................9
第1.4 節、研究流程......................................10
第貳章、理論基礎與文獻探討...............................11
第2.1 節、風險值的源起與發展.............................11
第2.2 節、風險值的定義..................................13
第2.3 節、各種風險值計算方法.............................16
(a). 簡單加權平均法....................................16
(b). 指數加權平均法....................................17
(c). GARCH 族模型.....................................17
(d). 歷史模擬法.......................................17
(e). 蒙地卡羅模擬法....................................17
第2.4 節、各種條件變異數方程式設定.......................18
(a). GARCH (p, q)模型.................................18
(b). IGARCH (p, q)模型與其退化之模型....................18
(c). EGARCH 模型.......................................20
(d). APARCH (p, q)模型與其退化之模型.....................21
第2.5 節、條件變異數方程式設定與風險值估計效能之關聯性.......23
第2.6 節、探討分佈厚尾性質與風險值估計效能之關聯性..........24
第2.7 節、探討分佈偏態性質與風險值估計效能之關聯性..........25
第2.8 節、風險值績效評估方法.............................27
IV
(a). 保守性............................................27
(b). 準確性............................................28
(c). 效率性............................................30
第參章、研究方法與實證模型...............................32
第3.1 節、研究方法......................................32
第3.2 節、實證模型......................................33
(a). 架構在常態分佈下的GJR-ARCH 及APARCH 模型............33
(b). 架構在t 分佈下的GJR-ARCH 及AP-ARCH 模型............34
(c). 架構在偏態t 分佈下的GJR-ARCH 及AP-ARCH 模型.........34
第3.3 節、模型適合度檢測................................36
第3.4 節、風險值績效評估................................37
第肆章、實證結果與分析..................................38
第4.1 節、資料來源與處理................................38
第4.2 節、實證結果與分析................................39
(a). 模型的參數估計結果.................................39
(b). 風險值之績效評估...................................41
第伍章、結論...........................................46
附表..................................................48
參考文獻...............................................59
1. 王凱立和林嘉慧, (2003), “條件高階動差於財務金融市場上之應用,”財務金融學刊, 11:2,1-41.
2. 沈大白、柯瓊鳳和鄒武哲, (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, 57-76.
3. 林楚雄和陳宜玫, (2002), “台灣股票市場風險值估測模型之實證研究,”管理學報, 19:4,737-758.
4. Alexander, C. O. and Leigh, C. T., (1997), ”On the Covariance Metrices Used inValue-at-Risk model,” The Journal of Derivatives, 50-62.
5. Ané, Thierry, (2006), “An Analysis of the Flexibility of Asymmetric Power GARCH Models,”Computational Statistics and Data Analysis, 51, 1293-1311.
6. Baillie, R. T., T. Bollerslev and H. O. Mikkelsen,(1996), “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity,”Journal of Econometrics, 74, 3-30.
7. Beder, T. S., (1995), “VaR:Seductive but Dangerous,” Financial Analysts Journal, 51, 12-24.
8. Bekaert, G. and G. Wu, 2000, “”Asymmetric Volatility and Risk in Equity Markets,” Review ofFinancial Studies, 13, 1-42.
9. Black, F., (1976), ”Studies of Stock Price Volatility Changes,” Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economical Section, 177-181.
10. Bollerslev, T., (1986), ”Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307-327.
11. Bollerslev, T., (1987), ”A Conditional Heteroskedastic Time Series Model for Speculative Price and Rate of Return,” Review of Economics and Statistics, 9, 542-547
12. Campbell, J.Y. and L. Hentschel, (1992), ”No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns,”Journal of Financial Economics, 31, 281-318.
13. Chou, R. Y, (1988), “Volatility Persistence and Stock Valuation: Some Empirical Evidence Using GARCH,”Journal of Applied Econometric, 3, 279-294.
14. Chou, C. H., (2000), ”The Performance of VaR Measurements- The Empirical Studies of Currency Exchange Rates,” Graduate Institute of Finance, Fu Jen Catholic University.
15. Christie, A.A., (1982), ”The Stochastic Behavior of Common Stock Variances –Value, Leverage and Interest Rate Effects,” Journal of Financial Economics, 10, 407-432.
16. Ding, Z. and C.W.J. Granger, (1996), “Modeling Volatility Persistence of Speculative Returns: A New Approach,” Journal of Econometrics, 73, 185-215.
17. Ding, Z., C. W. J. Granger and R. F. Engle, (1993), “A Long Memory Property of Stock Market Returns and a New Model,”Journal of Empirical Finance, 1, 83-106.
18. Duffee, G. R., (1995), “Stock Returnsand Volatility: A Firm-Level Analysis,” Journal of Financial Economics, 37, 399-420.
19. Engel, J. and M. Gizycki, (1999), “Conservatism, Accuracy and Efficiency: Comparing Value-at-Risk Models,” Working Paper 2, Australian Prudential RegulationAuthority.
20. Engle, R. F., (1982), ”Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, 50, 987-1007.
21. Engle, R. F. and G. Gonzalez-Rivera, (1991), “Semiparametric ARCH Models,” Journal of Business and Economic Statistics, 9, 345-359.
22. Engle, R. F. and T. Bollerslev, (1986), “Modeling the Persistence of Conditional Variance,”Econometric Review, 5, 1-50.
23. Fama, E. F., (1965), “The Behivior of Stock Market Prices,”Journal of Business, 38, 34-105.
24. Fernandez, V., (2003), “Extreme Value Theory and Value at Risk,”Revista de Analisis Economico,18:1, 57-85.
25. Foster, R. R. and Viswanathan, (1993), “ariations in Trading Volume, Return, Volatility, and Trading Cost: Evidence on Recent Price Formation Models,” Journal of Finance, 48, 187-211.
26. French, K.R., Schwert, G.W. and Stambaugh, R.F.(1987), ”Expected Stock Returns and Volatility,” Journal of Financial Economics, 19,3-29.
27. Gencay, R., F. Selcuk and A Ulugulyagci, (2003), “Hight Volatility, Thick Tail and Extreme Value Theory in Value-at-Risk Estimation, Mathematics and Economics, 33:2, 337-356.
28. Giot, P. and S. Laurnet, (2003), “Value-at-Risk for Long and Short Trading Positions,” Journal of Applied Econometrics, 18, 641-664.
29. Glosten, L., R. Jagannathan and D. Runkle, (1993), “On the Relation Between Expected Value and the Volatility of the Nominal Excess Return on Stocks,”Journal of Finance, 48, 1779-1801.
30. Goorbergh, R. V. D. and P. Vlaar, (1999), “Value-at-Risk Analysis of Stock Returns Historical Simulation, Variance Techniques or Tail Index Estimation,”http:// www.gloriamundi.org.
31. Hendricks, D., (1996), “Evaluation of Value-at-Risk Models Using Historical Data,”Economic Policy Review, 2:1, 39-70.
32. Higgins, M. L. and A. K. Bera, (1992), ”A class of nonlinear ARCH models,” International Economic Review, 33, 137-158.
33. Hong, H. and J.C. Stein, (2003), ”Differences of opinion, short-sales constraints and market crashes,”Review of Financial Studies, 16, 487-525.
34. Huang, Y.C., B. J. Lin, (2004), “Value-at-Risk Analysis for Taiwan Stock Index Futures: Fat Tails and Conditional Asymmetries in Return Innovations,”Review of Quantitative Finance and Accounting, 22, 79-95.
35. Hueng, C. J. and R.Yau, (2002), ”Investor’s Preference and Portfolio Selection: Is Diversification an Appropriate Strategy?”The Conference on Analysis of High-Frequency Financial Data and Market.
36. Inui, K., M. Kijima and A. Kitano, (2003), “VaR is subject to significant positive bias,”workingpaper.
37. Jackson, P., D. J. Maude and W. Perraudin, (1997), “Bank Capital and Value atRisk,” Journal of Derivatives, 4:3, 73-89.
38. Jorion, (1996), “Risk: Measuring the Risk in Value at Risk,”Financial Analysis Journal, 52,47-56.
39. Kupiec, P. H., (1998), “Stress Testing in a Value at Risk Framework,” The Journal of Derivatives,3:2, 7-24.
40. Kurt, B. and N. Nordman, (2003), “Conditional Skewness Modelling for Stock Returns,” Applied Economics Letters, 10, 725-728.
41. Lambert P., S. Laurent, (2000), “Modeling Skewness Dynamics in Series of Financial Data,”Discussion Paper, Institute de Statistique, Louvain-la-Neuve.
42. Lambert P., S. Laurent, (2001), “Modeling Financial Time Series Using GARCH-type Models and A Skewed Student Density,”Mimeo, Université de Liège.
43. Mandlebrot, B., (1963). “The Variance of Certain Speculative Prices.” Journal of Business, 36,94-419.
44. 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, 7:3-4,271-300.
45. Nelson, D., (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,”Econometrica, 59, 349-370.
46. Ñíguez, T. M., (2003), “Volatility And VaR Forecasting For The Ibex-35 Stock-Return Index Using Figarch-Type Processes And Different Evaluation Criteria," Working Papers.
47. Pindyck, R.S., (1984), “Risk, Inflation, and The Stock Market,” American Economic Review, 74,334-351.
48. Tang, T.-L. and S.-J. Shieh, (2006), “Long Memory in Stock Index Futures Markets: A Value at Risk Estimation,” Physica A: Statistical Mechanics and its Applications, 366, 437-448.
49. Richard D. F. Harris, C. Coskun Küçüközmen and Fatih Yilmaz, (2004), “Skewness in the Conditional Distribution of Daily Equity Returns,” Applied Financial Economics, 14:3, 195-202.
50. RiskMetrics Group, (1996), “RiskMetrics ─ Technical Document,”Morgan J.P.
51. So, M.K.P. and P.L.H. Yu, (2006), “Empirical Analysis of GARCH Models in Value at Risk Estimation,” Journal of International Financial Markets, Institutions and Money, 16:2, 180-197.
52. Schwert, W., (1990), “Stock Volatility and the Crash of 1987,”Review of Financial Studies, 3:1,77-102.
53. Tayer, S.,(1986), “Modeling financial time series,“(Wiley, Chichester).
54. Wang, K. L., C. Fawson, C. B. Barrett and J. McDonald, (2001), “A Flexible Parametric GARCH Model with an Application to Exchange Rates,” Journal of Applied Econometrics, 16:4, 521-536.
55. Wang, K. L. and C. Fawson, (2001), “Modeling Asian Stock Returns with a More General Parametric GARCH Specification,” Journal of Financial Studies, 9:3, 21-52.
56. Wang, K. L., C. Fawson, and C. B. Barrett, (2002), “An Assessment of Empirical Model Performance When Financial Market Transactions are Observed at Different Data Frequencies: An Application to East Asian Exchange Rates,” Review of Quantitative Finance and Accounting Journal, 19:2, 111-129.
57. Wu, G., (2001), “The Determinants of Asymmetric Volatility,” Review of Financial Studies, 14:3,837-860.
58. Wu, P.-T. and S.-J. Shieh, (2006), “Value-at-Risk Analysis for Long-Term Interest Rate Futures:Fat-Tail and Long Memory in Return Innovations,”Journal of Empirical Finance, In Press,Corrected Proof, Available online 24 August 2006.
59. Wung, S. B., (1999), “The Market Risk Measurement of the Warrants of the Issuer,” The Department of Economics, Soochow University, Taiwan.
60. Zakoian, J. M., (1994), “Threshold Heteroskedasticity Models,”Journal of Economic Dynamics and Control, 15, 931-955.
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