compositequantileregressionestimationforpgarchprocesses
文献类型：期刊论文
| 作者 | Zhao Biao4; Chen Zhao1; Tao Guiping3; Chen Min2
|
| 刊名 | sciencechinamathematics
 |
| 出版日期 | 2016 |
| 卷号 | 59期号:5页码:977 |
| ISSN号 | 1674-7283 |
| 英文摘要 | 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 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/37318]  |
| 专题 | 应用数学研究所 |
| 作者单位 | 1.Department of Statistics, Pennsylvnia State University 2.中国科学院数学与系统科学研究院 3.首都经济贸易大学 4.中国科学技术大学 |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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