compositequantileregressionestimationforpgarchprocesses

CSpace > 应用数学研究所

	compositequantileregressionestimationforpgarchprocesses
	Zhao Biao 1; Chen Zhao 2; Tao Guiping 3; Chen Min4
	2016
发表期刊	sciencechinamathematics
ISSN	1674-7283
卷号	59 期号:5 页码:977
摘要	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.
语种	英语
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/37319
专题	应用数学研究所
作者单位	1.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
推荐引用方式 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.