Composite quantile regression estimation for P-GARCH processes

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• 作者：Biao Zhao ; Zhao Chen ; GuiPing Tao ; Min Chen
• 关键词：composite quantile regression ; periodic GARCH process ; strictly periodic stationarity ; strong consistency ; asymptotic normality
• 刊名：SCIENCE CHINA Mathematics
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：59
• 期：5
• 页码：977-998
• 全文大小：308 KB
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• 作者单位：Biao Zhao (1)
  Zhao Chen (2)
  GuiPing Tao (3)
  Min Chen (4)
  
  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, 230026, China
  2. Department of Statistics, Pennsylvnia State University, Pennsylvnia, 16802, USA
  3. School of Statistics, Capital University of Economics and Business, Beijing, 100070, China
  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Chinese Library of Science
  Applications of Mathematics
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1862

文摘

We 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.