全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于非线性分位数回归模型的多期VaR风险测度

, PP. 56-65

Keywords: 分位数回归,多期VaR,非线性,神经网络,支持向量机

Full-Text   Cite this paper   Add to My Lib

Abstract:

?多期VaR主要受到持有期及波动率两个变量的影响,并且其影响模式(线性或非线性)的确定对于准确地进行VaR风险测度至关重要。非线性分位数回归模型,能够克服线性分位数回归模型只能揭示多期VaR及其影响因素之间线性依赖关系的局限,从而提高多期VaR风险测度的准确性。结合波动模型与两个非线性分位数回归方法:QRNN和SVQR,给出了多期VaR风险测度的三类方案:波动模型法、QRNN+波动模型法、SVQR+波动模型法。选取3个股票价格指数作为研究对象,考虑了6种不同形式的波动模型,得到了18个多期VaR风险测度方法进行实证比较,结果表明:波动模型选择影响到多期VaR风险测度效果;SVQR+波动模型法略优于QRNN+波动模型法,并且两者显著优于波动模型法。

References

[1]  Jorion P. Value at risk: The new benchmark for managing financial risk[M]. New York: McGraw-Hill. 2007.
[2]  Morgan J, Riskmetrics: Technical document. Working Paper, Morgan Guaranty Trust Company of New York, 1996.
[3]  Koenker R, Bassett G W. Regression quantiles[J]. Econometrica, 1978, 46(1): 33-50.
[4]  Taylor J W. Using exponentially weighted quantile regression to estimate value at risk and expected shortfall[J]. Journal of Financial Econometrics, 2008, 6(3): 382-406.
[5]  张瑞锋, 张世英, 唐勇. 金融市场波动溢出分析及实证研究[J]. 中国管理科学, 2006, 14(5): 14-22.
[6]  史金凤, 刘维奇, 杨威. 基于分位数回归的金融市场稳定性检验[J]. 中国管理科学, 2011, 19(2): 24-29. 浏览
[7]  许启发, 蒋翠侠. 分位数局部调整模型及应用[J]. 数量经济技术经济研究, 2011, 28(8): 115-133.
[8]  陈磊, 曾勇, 杜化宇. 石油期货收益率的分位数建模及其影响因素分析[J]. 中国管理科学, 2012, 20(3): 35-40. 浏览
[9]  Taylor J W. A quantile regression approach to estimating the distribution of multiperiod returns[J]. Journal of Derivatives, 1999, 7(1): 64-78.
[10]  Taylor J W. A quantile regression neural network approach to estimating the conditional density of multiperiod returns[J]. Journal of Forecasting, 2000, 19(4): 299-311. 3.0.CO;2-V target="_blank">
[11]  White H. Nonparametric estimation of conditional quantiles using neural networks[M]. New York: Computing Science and Statistics, 1992.
[12]  Feng Yijia, Li Runze, Sudjianto A, et al. Robust neural network with applications to credit portfolio data analysis[J]. Statistics and its interface, 2010, 3(4): 437-444.
[13]  Cannon A J. Quantile regression neural networks: Implementation in R and application to precipitation downscaling[J]. Computers & Geosciences, 2011, 37(9): 1277-1284.
[14]  Cannon A J. Neural networks for probabilistic environmental prediction: Conditional density estimation network creation and evaluation (CaDENCE) in R[J]. Computers & Geosciences, 2012, 41(4): 126-135.
[15]  Takeuchi I, Furuhashi T. Non-crossing quantile regressions by SVM. Proceedings of 2004 IEEE International Joint Conference on Neural Networks, Budapest,Hungary,July 25-29,2004.
[16]  Li Youjuan, Liu Yufeng, Zhu Ji. Quantile regression in reproducing kernel Hilbert spaces[J]. Journal of the American Statistical Association, 2007, 102(477): 255-268.
[17]  Shim J, Kim Y, Lee J, et al. Estimating value at risk with semiparametric support vector quantile regression[J]. Computational Statistics, 2012, 27(4): 685-700.
[18]  Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3): 307-327.
[19]  Vapnik V N. The nature of statistical learning theory[M]. New York: Springer, 1995.
[20]  Huber P J. Robust estimation of a location parameter[J]. Annals of Mathematical Statistics, 1964, 35(1): 73-101.
[21]  Yuan Ming. GACV for quantile smoothing splines[J]. Computational Statistics and Data Analysis, 2006, 50(3): 813-829.
[22]  Chen Meiyuan, Chen J E. Application of quantile regression to estimation of value at risk. Working Paper, National Chung-Cheng University, 2002.
[23]  王鹏, 魏宇. 中国燃油期货市场的 VaR 与 ES 风险度量[J]. 中国管理科学, 2012, 20(6): 1-8. 浏览
[24]  Kupiec P. Techniques for verifying the accuracy of risk measurement models[J]. Journal of Derivatives, 1995, 3(2): 73-84.
[25]  Christoffersen P F. Evaluating interval forecasts[J]. International Economic Review, 1998, 39(4): 841-862.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133