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...

基于多分形理论的动态VaR预测模型研究

, PP. 7-15

Keywords: 多分形,波动率,样本外动态风险价值,预测,Backtesting

Full-Text   Cite this paper   Add to My Lib

Abstract:

?经济物理学(econophysics)的大量研究表明,金融市场的波动具有复杂的多分形(multifractal)特征,因此准确地测度和预测市场波动,对金融风险管理工作的意义重大。在已有多分形波动率(multifractalvolatility)测度及其模型应用基础上,以上证综指10年的高频数据为对象,提出了基于多分形波动率的样本外动态风险价值(out-of-sampledynamicVaR)预测法。通过两种规范的后验分析(backtesting)结果表明,与8种主流的线性和非线性GARCH族模型相比,在高风险水平上,基于多分形波动率测度的VaR模型明显具有更高的样本外动态风险预测精度。

References

[1]  Matteo T D. Multi-scaling in finance [J]. Quantitative Finance, 2007, 7(1): 21-36.
[2]  Mandelbrot B B. A multifractal walk down wall street [J]. Scientific American, 1999, 280(2): 70-73.
[3]  Stanley H E, Amaral L A N, et al. Similarities and differences between physics and economics [J]. Physica A, 2001, 299(1-2): 1-15.
[4]  Bacry E, Delour J, et al. Modeling financial time series using multifractal random walks [J]. Physica A, 2001, 299(1-2): 84-92.
[5]  Calvet L, Fisher A. Multifractality in asset returns: theory and evidence [J]. Review of Economics and Statistics, 2002, 84(3): 381-406.
[6]  Xu Zhaoxia, Gencay R. Scaling, self-similarity and multifractality in FX markets [J]. Physica A, 2003, 323: 578-590.
[7]  何建敏,常松.中国股票市场多重分形游走及其预测[J].中国管理科学,2002,10(3):11-17.
[8]  张永东,毕香秋.中国股票市场多标度行为的实证分析[J].预测,2002,21(4):56-59.
[9]  魏宇,黄登仕.金融市场多标度分形现象及与风险管理的关系[J].管理科学学报,2003,6(1):87-91.
[10]  魏宇,黄登仕.基于多标度分形理论的金融风险测度指标研究[J].管理科学学报,2005,8(4):50-59.
[11]  周孝华,宋坤,等.股票价格持续大幅度波动前后多重分形谱的异常及分析[J].管理工程学报,2006,20(2):92-96.
[12]  苑莹,庄新田.股票市场多重分形性的统计描述[J].管理评论,2007,19(12):3-8.
[13]  Faruk S, Ramazan G. Intraday dynamics of stock market returns and volatility [J]. Physica A, 2006, 367: 375-387.
[14]  魏宇.金融市场的多分形波动率测度、模型及其SPA检验[J].管理科学学报,2009,12(5):88-99.
[15]  魏宇.多分形波动率测度的VaR计算模型[J].系统工程理论与实践,2009,29(9):7-15.
[16]  Swanson N R, Elliott G, et al. Predictive methodology and application in economics and finance: volume in honor of the accomplishments of Clive W.J. Granger [J]. Journal of Econometrics, 2006, 135(1-2): 1-9.
[17]  Cont R. Empirical properties of asset returns: stylized facts and statistical issues [J]. Quantitative Finance, 2001, 1(2): 223-236.
[18]  Hansen P R, Lunde A. A forecast comparison of volatility models: does anything beat a GARCH(1,1)?[J]. Journal of Applied Econometrics, 2005, 20(7): 873-889.
[19]  Kupiec P H. Techniques for verifying the accuracy of risk measurement models [J]. Journal of Derivatives, 1995,3(2): 73-84.
[20]  Engle R, Manganelli S. CAViaR: conditional autoregressive value at risk by regression quantiles [J]. Journal of Business and Economic Statistics, 2004, 22(4): 367-381.
[21]  Kang S H, Kang S M, et al. Forecasting volatility of crude oil markets [J]. Energy Economics, 2009, 31(1): 119-125.
[22]  Angelidis T, Benos A, et al. The use of GARCH models in VaR estimation [J]. Statistical Methodology, 2004, 1(1-2): 105-128.
[23]  So M K P, Yu P L H. Empirical analysis of GARCH models in value at risk estimation [J]. Journal of International Financial Markets, Institutions and Money, 2006, 16(2): 180-197.
[24]  周炜星.金融物理学导论[M].上海:上海财经大学出版社,2007.
[25]  Jiang Zhiqiang, Zhou Weixing. Multifractality in stock indexes: fact or fiction? [J]. Physica A, 2008, 387(14): 3605-3614.
[26]  Ashely R, Granger C W J, et al. Advertising and aggregate consumption: an analysis of causality [J]. Econometrica, 1980, 48(5): 1149-1167.
[27]  Lo A W, MacKinlay A C. Data-snooping biases in tests of financial asset pricing models [J]. Review of Financial Studies, 1990, 3(3): 431-467.
[28]  Foster F D, Smith T, et al. Assessing goodness-of-fit of asset pricing models: the distribution of the maximal R2 [J]. Journal of Finance, 1997, 52(2): 591-607.
[29]  Koopman S J, Jungbacker B, et al. Forecasting daily variability of the S&P100 stock index using historical, realized and implied volatility measurements [J]. Journal of Empirical Finance, 2005, 12(3): 445-475.
[30]  Andersen T G, Bollerslev T, et al. The distribution of realized stock return volatility [J]. Journal of Financial Economics, 2001, 61(1): 43-76.
[31]  Wei Yu, Wang Yudong, Huang Dengshi. Forecasting crude oil market volatility: further evidence using GARCH-class models [J]. Energy Economics, 2010, 32(6): 1477-1484.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133