风险价值的多尺度估值模型及其均方误差收敛性分析被引量：1

Multi-resolution estimating model of the value-at-risk and its mean square error convergence analysis

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摘要在金融市场风险管理研究中,利用参数及半参数模型度量风险价值并不足以体现投资行为的异质性及资产价格波动的多尺度特征.引入概率密度的小波非线性阈值估计方法,建立了风险价值的多尺度估值模型,并分析了估值误差的收敛性,发现密度函数空间的光滑度和样本容量同时决定均方误差的收敛速度.最后以正态密度函数为算例,通过不同容量的仿真样本检验了该理论方法的可行性. It was insufficient to reflect the heterogeneity of the investment behavior and multi-scale features of the asset price fluctuation via using the parametric or semi-parametric models to measure the market risk in financial risk management research.This paper introduced the probability density of nonlinear wavelet threshold estimator in order to establish the multi-scale valuation model of the Value-at-Risk and analyze the mean square error（MSE） convergence of the model.The result shows that both the smoothness of the density function space and sample size determines the MSE convergence rate.Finally,by means of different sample sizes of simulation experiment,we choose the normal density family as an example to prove the feasibility of the proposed approach.

作者彭选华傅强

机构地区重庆大学经济与工商管理学院

出处《系统工程理论与实践》 EI CSSCI CSCD 北大核心 2011年第10期1837-1846,共10页 Systems Engineering-Theory & Practice

基金高等学校博士学科点专项科研基金(20100191110033) 国家自然科学基金(70501015)

关键词小波分析多尺度分析风险价值风险管理 wavelet analysis multi-resolution analysis value-at-risk risk management

分类号 F224.0 [经济管理—国民经济]

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参考文献22

1Jorion P. Value at Risk: The New Benchmark for Controlling Market Risk[M]. New York: McGraw-Hill, 1997.
2Tsay R S. Analysis of Financial Time Series[M]. New York: Wiley, 2005.
3Manganelli S, Engle R F. Value at risk models in finance[EB/OL]. Working Paper Series 075, European Central Bank, 2001.
4Glasserman P, Heidelberger P, Shahabuddin P. Portfolio value-at-risk with heavy-tailed risk factors [J]. Mathe- matical Finance, 2002, 12(3): 239-270.
5Rockafellar R, Uryasev S. Conditional value-at-risk for general loss distributions[J]. Journal of Banking and Finance, 2002, 26(7): 1443-1471.
6Angelidis T, Benos A, Degiannakis S. The use of GARCH models in VaR estimation[J]. Statistical Methodology, 2004, 1(2): 105-128.
7Christoph H, Stefan M, Paolelta M. Accurate value-at-risk forecasting based on the normal-GARCH model[J]. Computational Statistics & Data Analysis, 2006, 51(4): 2295-2312.
8Bhattacharyya M, Chaudhary A, Yadav G. Conditional VaR estimation using Pearson's type IV distribution[J]. European Journal of Operational Research, 2008, 191(2): 386-397.
9Sadefo J. A - VaR and A -TVaR for portfolios with mixture of elliptic distributions risk factors and DCC[J]. Insurance: Mathematics and Economics, 2009, 44(3): 325-336.
10Ramazan G, Faruk S. Extreme value theory and value-at-risk: Relative performance in emerging markets[J]. International Journal of Forecasting, 2004, 20(2): 287-303.

二级参考文献6

1余璟明,何希琼,程冬爱.基于离散小波变换的时间序列数据挖掘[J].计算机应用,2005,25(3):652-653. 被引量：3
2李水银，吴纪桃．分形与小波[M]．北京：科学出版社．2002．
3KIM S,CHO S,LEE S.On the CUSUM test for parameter changes in GARCH (1,1) models[J].Commun in Statist,2000,29:445-462.
4INCLAN C,TIAO G C.Use of cumulative sums of squares for retrospective detection of changes of variances[J].Amer Statist Assoc,1994,89:913-923.
5MALLAT S,HANG W L.Singularity detection and processing with wavel et[J].IEEE Transon Information Theory,1992,38(22):617-643.
6POPIVANOVI,MILLER R J.Similarity search over timeseries data using wavelets[C]//Proceedings of the 18th International Conference on Data Engineering.Washington,DC:IEEE Computer Society,2002:212-221.