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compositequantileregressionestimationforpgarchprocesses
Zhao Biao1; Chen Zhao2; Tao Guiping3; Chen Min4
2016
Source Publicationsciencechinamathematics
ISSN1674-7283
Volume59Issue:5Pages:977
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.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/37319
Collection应用数学研究所
Affiliation1.中国科学技术大学
2.Department of Statistics, Pennsylvnia State University
3.首都经济贸易大学
4.中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Zhao Biao,Chen Zhao,Tao Guiping,et al. compositequantileregressionestimationforpgarchprocesses[J]. sciencechinamathematics,2016,59(5):977.
APA Zhao Biao,Chen Zhao,Tao Guiping,&Chen Min.(2016).compositequantileregressionestimationforpgarchprocesses.sciencechinamathematics,59(5),977.
MLA Zhao Biao,et al."compositequantileregressionestimationforpgarchprocesses".sciencechinamathematics 59.5(2016):977.
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