Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23739
標題: The Measurements of Value at Risk on TAIEX Futures
台灣加權股價指數期貨之風險值衡量
作者: 張瑞竹
Chang, Jui-Chu
關鍵字: GARCH;GARCH;Extreme Value Theory;VaR;Back-Testing;極值理論;風險值;回溯測試
出版社: 財務金融系所
引用: 一、中文部分 台灣期貨交易所,統計資料專區, http://www.taifex.com.tw/chinese/home.asp 陳怡君(2002),極端值理論在風險值上的應用,政治大學統計研究所碩士論文。 二、西文部分 1.Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and Diebold, F.X. (2006), “Practical Volatility and Correlation Modeling for Financial Market Risk Management,” Risks of Financial Institutions, University of Chicago Press for NBER, pp.513-548. 2.Basel Committee on banking Supervision, (2004), International Convergence of Capital Measurement and Capital standards, Basle, Switzerland. 3.Bali, T.G. (2003), “An Extreme Value Approach to Estimating Volatility and Value at Risk,” Journal of Business, 76, pp.83-108. 4.Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, 31, pp.307–327. 5.Bystrom, H. (2001), “Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory,” Working Paper, Department of Econometrics, Lund University. 6.Danielsson, J. and C. de Vries (1997), “Tail Index and Quantile Estimation with Vary High Frequency Data,” Journal of Empirical Finance, Vol. 4, pp.241-257. 7.Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK,” Econometrica, 50, pp.987 -1008. 8.Engle, R.F. (2001), “GARCH 101: The Use of ARCH/GARCH Model in Applied Economics,” Journal of Economic Perspectives, 15(4), pp.157-168. 9.Fisher, R. and L. Tippett (1928), “Limiting Forms of the Frequency Distribution of the Largest or Smallest Member of a Sample,” Proceedings of the Cambirdge Philosophical Society, pp.180-190. 10.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. 11.Jorion, P. (1997), The New Benchmark for Controlling Market Risk, Irwin. 12.Jorion, P. (2001), Value at Risk : The New Benchmark for Managing Financial Risk, 2nd edition, McGraw-Hill, Inc. 13.J. P. Morgan (1995), Riskmetrics Technical Manual, J. P. Morgan, New York. 14.Kevin Dowd , Measuring Market Risk, 2nd edition, John Wiley & Sons, Ltd. 15.Kupiec, P. H. (1995), “Techniques for Verifying the Accuracy of Risk Measurement Models, ” Journal of Derivatives, Vol.3, No.2, pp.73-84. 16.Longin, F.M. (1996), “The Asymptotic Distribution of Extreme Stock Market Returns,” Journal of Business, 69(1), pp. 383-408. 17.Longin F. (2000), “From Value at Risk to Stress Testing: the Extreme Value Approach,” Journal of Banking & Finance, Vol. 24, pp.1097-1130. 18.Mandelbrot, B. B. (1963). “The Variation of Certain Speculative Prices,” Journal of Business 36, pp. 394-419. 19.McNeil, A. (1999), “Extreme Value Theory for Risk Managers,” In Internal Modeling and CAD Ⅱ, RISK Books, pp. 93-113. 20.McNeil, A. and R. Frey (2000), “Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach,” Journal of Empirical Finance, Autumn, pp. 271-300. 21.Moosa, A. and B. Bollen (2002), “A Benchmark for Measuring Bias in Estimated Daily Value at Risk,” International Review of Financial Analysis, 11, pp.85-100. 22.Pafka, 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. 23.Smith, R. (1985), “Maximum Likelihood Estimation in a Class of Non-regular Cases,” Biometrika, Vol. 72, pp.67-90. 24.Stelios D.B. and Dimitris A.G. (2005), “Estimation of Value-at-Risk by Extreme Value and Conventional Methods: A comparative evaluation of their predictive performance,” Journal of International Finacial Markets, Institutions and Money, 15, pp.209-228.
摘要: 
極值理論常被用來研究金融資產尾部極值的行為,本研究考慮台股期貨報酬具有厚尾的特性,在避免低估其風險值下,以極值理論進行風險值的估計,台股期貨報酬還有波動群聚的特性,以AR(1)-GARCH(1,1) 模型來配適其報酬資料,捕捉報酬的條件異質變異,再利用極值理論估計風險值,另外還使用歷史模擬法及RiskMetrics的指數加權移動平均法來估計風險值。
以2006年1月2日至2009年12月31日進行回溯測試來評估模型的正確性,回溯測試期間分為金融風暴發生前及金融風暴期間,結果顯示在金融風暴發生前,三個模型估計的風險值皆可通過驗證;在金融風暴期間,考慮GARCH效果下,再以極值理論模型估計出來的風險值最準確,而歷史模擬法在99% 信賴水準下及較長的移動窗口下的風險值估計較為準確,在95%信賴水準下容易低估風險值,EWMA模型的表現最差,不適合用來估計台股期貨的風險值。

Taking into consideration that the return distribution of TAIEX Futures is fat-tail, this paper adapts Extreme Value Theory to calculate Value-at-Risk. Extreme value theory is powerful to study the behavior of the tail distribution. That the volatility of returns is clustering implies that returns are conditionally heteroskedastic. This study uses AR(1)-GARCH (1,1) model to measure the standardized innovations of returns on application of Extreme Value Theory. This study also implements historical simulation approach and EWMA model of RiskMetrics to measure VaR.
This study exams the accuracy of these VaR models by back-testing and the back-testing sample period is from January 2, 2006 to December 31, 2009, before financial crisis period and during financial tsunami period. Before financial crisis period, the empirical results prove that VaR can be calculated right by these models. During financial tsunami period, VaR can be calculated right by AR(1)-GARCH(1,1)-GEV model. Historical simulation approach may underestimate VaR in 0.95 and 0.99 quantiles. Using RiskMetrics model to evaluate VaR of TAIEX Futures no matter in which confidence interval is not appropriate.
URI: http://hdl.handle.net/11455/23739
其他識別: U0005-2807201016301600
Appears in Collections:財務金融學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.