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Composite quantile regression estimation for P-GARCH processes
Zhao Biao1; Chen Zhao2; Tao GuiPing3; Chen Min4
AbstractWe consider the periodic generalized autoregressive conditional heteroskedasticity (P-GARCH) process and propose a robust estimator by composite quantile regression. We study some useful properties about the P-GARCH model. Under some mild conditions, we establish the asymptotic results of proposed estimator. The Monte Carlo simulation is presented to assess the performance of proposed estimator. Numerical study results show that our proposed estimation outperforms other existing methods for heavy tailed distributions. The proposed methodology is also illustrated by VaR on stock price data.
Keywordcomposite quantile regression periodic GARCH process strictly periodic stationarity strong consistency asymptotic normality
Funding ProjectNational Natural Science Foundation of China[11371354] ; Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences[2008DP173182] ; National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences
WOS Research AreaMathematics
WOS SubjectMathematics, Applied ; Mathematics
WOS IDWOS:000374329000012
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Document Type期刊论文
Corresponding AuthorChen Zhao
Affiliation1.Univ Sci & Technol China, Dept Stat & Finance, Hefei 230026, Peoples R China
2.Penn State Univ, Dept Stat, University Pk, PA 16802 USA
3.Capital Univ Econ & Business, Sch Stat, Beijing 100070, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhao Biao,Chen Zhao,Tao GuiPing,et al. Composite quantile regression estimation for P-GARCH processes[J]. SCIENCE CHINA-MATHEMATICS,2016,59(5):977-998.
APA Zhao Biao,Chen Zhao,Tao GuiPing,&Chen Min.(2016).Composite quantile regression estimation for P-GARCH processes.SCIENCE CHINA-MATHEMATICS,59(5),977-998.
MLA Zhao Biao,et al."Composite quantile regression estimation for P-GARCH processes".SCIENCE CHINA-MATHEMATICS 59.5(2016):977-998.
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