Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23801
標題: 分量迴歸在風險值估計與回測檢定的應用
Applications of Quantile Regressions on the Estimations and Backfit Tests for VaR
作者: 邱建豪
Chiu, Chien-Hao
關鍵字: 風險值;Value at Risk;分量迴歸;穩健性;回溯測試;Quantile Regression;robust;Backfit test
出版社: 財務金融系所
引用: 參考文獻 陳旭昇(2007), 《時間序列分析-總體經濟與財務金融之應用》, 東華書局. 黃達業(2005), 《風險值-金融風險管理的新基準》, 麥格羅希爾. Artzner, Philippe, Delbaen, Freddy, Eber, Jean-Marc, and Heath, David (1999), “Coherent measures of risk”, Mathematical Finance, 9, 203–228. Baillie, Richard T. and Tim, Bollerslev (1989), “Common stochastic trends in a system of exchange rates”, Journal of Finance, 44, 167–181. Balkema, A. and de Haan, L. (1974), “Residual life time at great age”, Annals of Probability, 2, 792–804. Bollerslev, Tim (1986), “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 31, 307–327. Boudoukh Jacob, Richardson Matthew and Whitelaw, Robert F. (1998), “The best of both worlds”, Risk, 64–67. Chen, M.-Y. (2009), “Quantile regression: Estimation, statistical inferences and application”, Department of Finance National Chung Hsing University, Taiwan. Chen, Mei-Yuan (2010), “Application of quantile regression to estimation of value at risk”, Department of Finance National Chung Hsing University, Taiwan. Christoffersen, Peter F. (1998), “Evaluating interval forecasts”, International Eco-nomic Review, 39, 841–862. Danielsson, Jon and de Vries, Casper G. (1997), “Value at risk and extreme returns”, Technical report, London School of Economics. Ding Z., Granger C.W.J. and Engle, Robert. F. (1993), “A long memory property of stock market returns and a new model”, Journal of Empirical Finance, 1, 83–106. Duffie, D. and Pan, J. (1997), “An overview of value at risk”, Journal of Derivatives. Engle, Robert F. (1982), “Autoregressive conditional heteroscedasticity with esti-mates of the variance of united kingdom inflation”, Econometric, 50, 987–1007. Fernandez, Carmen and Steel, Mark F. J. (1998), “On bayesian modeling of fat tails and skewness”, Journal of the American Statistical Association, 93, 359–371. Gaglianone Wagner P., Oliver Linton, Luiz Renato Lima and Smith., Daniel R. (2011), “Evaluating value-at-risk models via quantile regressions”, Journal of Business and Economic Stastistics, 29, 150–160. Gnedenko, B. V. (1943), “Sur la distribution limite du terme maximum dune seire aleatoire”, Annals of Mathematics, 44, 423–453. Hull, John and Alan, White (1998), “Value at risk when daily changes in market variables are not normally distributed”, Journal of Derivatives, 5, 9–19. Jenkinson, A.F. (1955), “The frequency distribution of the annual maximum (or minimum) values of meteorological elements”, Meteo. Soc, 81, 158–171. Koenker, R. and Basset, G. (1978), “Regression quantiles”, Econometrica, 46, 33–50. Koenker, R. and Xiao, Z. (2004), “Quantile autoregression”, Technical report, Uni-versity of Illinois, working paper. Koenker, R. and Zhao, Q. (1996), “Conditional quantile estimation and inference for arch models”, Econometric, 12, 793–813. Kupiec, P. (1995), “Techniques for verifying the accuracy of risk management mod-els”, Journal of Derivatives, 3, 73–84. Lawrence R. Glosten, RAVI Jagannathan and Runkle, David E. (1993), “Relation between the expected value and the volatility of the nominal excess return on stocks”, Journal of Finance, 31, 1779–1801. Lima, Luiz Renato and Neri, Breno Pinheiro (2007), “Comparing value at risk methodologies”, Brazilian Review of Econometrics, 27(1), 1–25. Manganelli, Simone and Engle, Robert F. (2001), “Value at risk models in finance”, Technical report, European Central Bank. Pickands, J. (1975), “Statistical inference using extreme order statistics”, Annals of Statist, 3, 119–131.
摘要: 
風險值是一個被廣泛利用於財務上的風險測度,至1999年開始即被巴塞爾公約列為衡量財務風險的重要工具。文獻上已發展出許多計算風險值的方法,而這些方法可以歸納為參數法、半參數法和無母數方法。在本文中將分別介紹不同方法的風險值模型及如何求算出風險值。
而在文獻上提到半參數法-分量迴歸法是一種具有穩健性的方法。在本文中,將透過一個實證研究來比較五種風險值模型之表現,其中五種模型中含有兩個分量迴歸方法的模型。在實證中我們計算台灣加權指數和上海A股指數的報酬率並對其作風險值的估算與比較,進而再用回溯測試來比較這些風險值的準確性。本論文實證研究結果顯示分量迴歸方法確實在風險值估算上具有較優的表現。

Value at Risk(VaR) is a widely used measure of the risk of loss on a specific protfolio of financial assets. The Basel Capital
Accord beginning in 1999, gave further push to the use of VaR. Now, there are many kinds of methods to caculate the VaR, such as
parametric methods, semi-parametric methods and non-parametric methods. This paper introduces some useful methods to
calculate VaR.
In literature, the semi-parametric method - Quantile regression is a robust method. In this thesis, we compare
five different Value-at-Risk methods, which contain two quantile regression methods, through an empirical exercise. In the empirical exercis, we estimate VaR for returns of TSEC weighted index and returns of SHASHR index. Then we will use Backfit test to check accuracy of these VaR models. The empirical results show that the Quantile regression methods are better to estiamte VaR.
URI: http://hdl.handle.net/11455/23801
其他識別: U0005-0702201315571300
Appears in Collections:財務金融學系所

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