結合PowerEWMA與歷史模擬法之風險值估測模型__臺灣人文及社會科學引文索引資料庫

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題名：	結合PowerEWMA與歷史模擬法之風險值估測模型
作者：	張簡彰程
作者(外文)：	Chang-Cheng Changchien
校院名稱：	國立高雄第一科技大學
系所名稱：	管理研究所
指導教授：	林楚雄
學位類別：	博士
出版日期：	2006
主題關鍵詞：	指數加權移動平均法；歷史模擬法；風險值；一般化條件異質變異數模型；EWMA；GARCH Model；Historical Simulation；Value at Risk
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:0 共同引用:0 點閱:39

摘要
歷史模擬法為一個具有捕捉非常態分配與不須考慮資產間相關係數的估計資
產組合風險值的簡單模型。然而，應用歷史模擬法估計風險值時，潛藏無法捕捉
與時改變波動的行為而導致風險預測能力降低的問題。因此本研究首先結合power
EWMA 於Hull 與 White (1998)所提出之結合近期波動資訊的歷史模擬法。再者，
本研究亦提出一個結合power EWMA 與歷史模擬法的二階段估計風險值模型，以
提昇歷史模擬法估計風險值之準確性。此外，本研究並提出利用峰態係數法來推
估隨時間改變的波動行為之Power EWMA 估計式，可避免運用GARCH 模型時必
須事先設定條件分配以求解高度非線性參數估計的問題，而保有歷史模擬法容易
估計的特性。本研究經保守性、正確性與效率性統計檢定結果，證實本研究所建
構的結合power EWMA於Hull 與 White (1998)之修正方式與本研究所提出的結合
power EWMA 與歷史模擬法的二階段估計風險值模型除了保有歷史模擬法容易估
計的特性外，確實也能夠提昇風險值的預測能力。再者，當以Kupiec (1995)檢定
法比較Hull 與White 修正方式與兩階段修正方式的設定方式時，本研究建議採用
兩階段之修正方式的風險值模型更可以獲得較準確的風險值預測。此外，本研究
亦建議採用本研究所提出利用峰態係數來推估條件分配的型態，而避免靜態分配
型態的參數設定為何值的困擾。

以文找文

ABSTRACT
Although the historical simulation possesses privileged advantages in estimating
portfolio VaR, it still has to deal with inherent dilemma problems caused by using
massive amounts of historical data. These problems are mainly attributed to the fact that
it usually misses situations with temporarily elevated volatility. This phenomenon of
time-varying volatility has been recognized in practice and literature. Consequently, the
negative impact caused on the estimative accuracy of portfolio VaR could be so
significant that the practicability of historical simulation may become nothing at all.
To solve the above problems concerned, this study proposes a two-stage approach
combining the Power EWMA estimator with the historical simulation when estimating
Value-at-Risk. Our method can avoid estimating parameters to forecast the variance
when using GARCH model and retains the easy usages characteristic of the historical
simulation approach. In addition, we also use the kurtosis coefficients to estimate the
distribution form for capturing the time-varying volatility. In the light of results of the
conservative, accuracy and efficiency, the empirical result shows that the proposed
method can considerably enhance the estimation accuracy of Value-at-Risk.
Moreover, from the results of the Kupiec (1995) test, our two-stage approach
indeed provides relatively promising performances on the forecasting accuracy of VaR,
in contrast with the Hull and White’s (1998) method. We particularly highlight that this
capturing capability comes from its own dynamic availability of tail-fatness through
kurtosis calculated.

以文找文

參考文獻
1. 林士貴、傅承德、柯子介，2004，“重點抽樣下拔靴法計算風險值”，財務金融
學刊，12 卷，1 期，頁81~116。
2. 林楚雄、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 new window

報，19 卷，4 期，頁737~758。
3. 林楚雄、張簡彰程，2002，“增進歷史模擬法估計風險值準確性之研究”，中山 new window

管理評論，10 卷，3 期，頁497~520。
4. 林楚雄、張簡彰程、謝景成，2005，“三種修正歷史模擬法估計風險值模型之 new window

比較”，風險管理學報，7 卷，2 期，頁183~201。
5. 劉美纓，2005，“銀行投資組合風險值估計－保守性、準確性及效率性”，財務
金融學刊，13 卷，2 期，頁97~128。
6. Alexander, C.O., and C.T. Leigh, 1997, “On the Covariance Matrices Used in
Value at Risk Models”, Journal of Derivatives, pp.50-62, spring.
7. Baillie, R., and R. DeGennaro, 1990, “Stock returns and volatility”, Journal of
Financial and Quantitative Analysis, vol.25, pp.203-214.
8. Barone-Adesi, G., K. Giannopoulos, and L. Vosper, 1999, “VaR without
Correlations for Portfolios of Derivative Securities”, The Journal of Futures
Markets, vol.19(5), pp.583-602.
9. Barone-Adesi, G., K. Giannopoulos, and L. Vosper, 2002, “Backtesting Derivative
Portfolios with FHS”, European Financial Management, pp.31-58.
10. Beder, T.S., 1995, “VAR: seductive but Dangerous”, Financial Analysts Journal,
pp.12-24, September.
11. Billio, M., and L. Pelizzon, 2000, “Value-at-Risk: A Multivariate Switching
91
Regime Approach”, Journal of Empirical Finance, vol.7, pp.531-554.
12. Bollerslev, T., 1986, “Generalized Autoregressive Condition Heteroskedasticity”,
Journal of Econometrics, vol.31, pp.307-327.
13. Bollerslev, T., R. Chou, and K. Kroner, 1992, “ARCH Modeling in Finance”,
Journal of Econometrics, vol.52, pp.5-59.
14. Boudoukh, J., M. Richardson, and R. Whitelaw, 1997, “Investigation of a Class of
Volatility Estimators”, Journal of Derivatives, vol.4, pp.63-71.
15. Boudoukh, J., M. Richardson, and R. Whitelaw, 1998, “The Best of Both Worlds:
A Hybrid Approach to Calculating Value at Risk”, Risk, vol.11, pp.64-67.
16. Butler, J.S., and B. Schachter, 1998, “Estimating Value at Risk by Combining
Kernel Estimation with Historical Simulation”, Review of Derivatives Research,
vol.1, pp.371-390.
17. Danielsson, J., and C.G. de Vries, 1997, “Value at Risk and extreme returns”,
Working Paper, London School of Economics.
18. Danielsson, J., and C.G. de Vries, 1997, “Beyond the sample: Extreme quantile and
probability estimation”, Working Paper.
19. Danielsson, J., and C.G. de Vries, 1997, “Tail index and quantile estimation with
high frequency data”, Journal of Empirical Finance, pp.241-257.
20. Danielsson, J., L. de Haan, L. Peng, and C.G. de Vries, 1997, “Using bootstrap
method to choose the sample fraction in tail index estimation”, Working Paper.
21. Dowd, K., 1998, Beyond Value-at-Risk: The New Science of Risk Management,
John Wiley & Sons, Chichester.
92
22. Duarte, J., 1997, “Model Risk and Risk Management”, Derivatives Quarterly,
pp.60-72, spring.
23. Duffie, D., and J. Pan, 1997, “An Overview of Value at Risk”, Journal of
Derivatives, pp.7-49.
24. Engel, J., and M. Gizycki, 1999, “Conservatism, Accuracy and Efficiency:
Comparing Value-at-Risk Models”, Working Paper 2, March.
25. Fama, E.F., 1965, “The behavior stock market prices”, Journal of Business, vol.38,
pp.34-105.
26. Goorbergh, R.V.D., and P. Vlaar, 1999, “Value-at-Risk Analysis of Stock Returns
Historical Simulation, Variance Techniques or Tail Index Estimation?”,
Econometric Research and Special Studies Dept. De Nederlandsche Bank.
27. Guermat, C., and Richerd D.F. Harris, 2002, “Robust Conditional Variance
Estimation and Value-at-Risk”, The Journal of Risk, vol.4(2), pp.25-41.
28. Harris, Richard D. F., and J. Shen, 2003, “Robust Estimation of The Optimal
Hedge Ratio”, The Journal of Futures Markets, vol.23(8), pp.799-816.
29. Hendricks, D., 1996, “Evaluation of Value-at-Risk Models Using Historical Data”,
Economic Policy Review, Federal Reserve Bank of New York 2(April), pp.39-69.
30. Hendricks, D., and B. Hirtle, 1997, “Bank Capital Requirements for Market Risk:
The Internal Models Approach”, Economic Policy Review, Federal Reserve Bank
of New York 2(December), pp.1-12.
31. Heynen, R.C., and G.M. Kat, 1994, “Volatility prediction: A comparison of the
stochastic volatility, GARCH (1,1) and EGARCH (1,1) models”, Journal of
Derivatives, pp.50-65.
93
32. Hopper, G., 1996, “Value at Risk：A new Methodology for Measuring Portfolio
Risk”, Business Review, Federal Reserve Bank of Philadelphia, pp.19-30.
33. Huang, Y.C., C.H. Lin, C.C. Changchien, and B.J. Lin, 2004, “The Tail Fatness and
Value-at-Risk Analysis of TAIFEX and SGX-DT Taiwan Stock Index Futures”,
Asia Pacific Management Review, vol.9(4), pp.729-750.
34. Hull, J., and A. White, 1998, “Incorporating volatility updating into the historical
Simulation method for value-at-risk”, Journal of Risk, pp.5-19.
35. Hull, J., and A. White, 1998, “Value at Risk when daily changes in market
variables are not normally distribution”, Journal of Derivatives, pp.9-19, spring.
36. Jackson, P., D. Maude, and W. Perraudin, 1997, “Bank Capital and Value-at-Risk”,
Journal of Derivatives, pp.73-90.
37. Jorion, P., 1997, Value at Risk: The New Benchmark for Controlling Market Risk,
McGraw-Hill.
38. Jorion, P., 1997, “Risk2: Measuring the Risk in Value at Risk”, Financial Analysis
Journal, pp.47-56, November/December.
39. Jorion, P., 2000, Value-at-Risk, McGraw-Hill.
40. J.P. Morgan, 1996, RiskMetrics Technical Document, 4th edition.
41. Kupiec, P., 1995, “Technique for Verifying the Accuracy of Risk Measurement
Models”, Journal of Derivatives, vol.3, pp.73-84.
42. Lin, C.H., C.C. Changchien, and S.W. Chen, 2005, “A General Revised Historical
Simulation Method for Portfolio Value-at-Risk”, Journal of Alternative
Investments, vol.8(2), pp.87-103.
43. Lin, C.H., C.C. Changchien, and S.W. Chen, 2006, “Incorporating the
94
Time-varying tail-fatness into the Historical Simulation Method for Portfolio
Value-at-Risk”, Review of Pacific Basin Financial Markets and Policies, vol.9(2),
forthcoming.
44. Linsmeier, T., and Neil D. Pearson, 2000, “Value at Risk”, Financial Analysts
Journal, vol. 56(2), pp.47-67.
45. Marshall, C., and M. Siegel, 1997, “Value at Risk ： Implementing a Risk
Measurement Standard”, Journal of Derivatives, vol. 4(3), pp.91-111.
46. McNeil, A.J., and R. Frey, 2000, “Estimation of Tail-Related Risk Measure for
Heteroscedastic Financial Time Series: An Extreme Value Approach”, Journal of
Empirical Finance, vol.7, pp.271-300.
47. Pritsker, M., 1997, “Evaluating Value at Risk Methodologies: Accuracy versus
Computational Time”, Journal of Financial Services Research, vol.12(3),
pp.201-243.
48. Schwert, G.W., 1989, “Why does stock market volatility change over time?”
Journal of Finance, vol.44, pp.1115-1153.
49. Simons, K., 1996, “Value at Risk-New Approaches to Risk Management”, New
England Economic Review, pp.3-13, Sep/Oct.
50. Vlaar, P., 2000, “Value at Risk Models for Dutch Bond Portfolios”, Journal of
Banking and Finance, vol.24(7), pp.1131-1154.

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