Robust M-estimate of GJR Model with High Frequency Data

In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient when high-frequency data is taken into the model. However, high-frequency data brings...

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Bibliographic Details
Published inActa Mathematicae Applicatae Sinica Vol. 31; no. 3; pp. 591 - 606
Main Authors Huang, Jin-shan, Wu, Wu-qing, Chen, Zhao, Zhou, Jian-jun
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2015
Subjects
Applications of Mathematics
Math Applications in Computer Science
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
M估计
Theoretical
估计性能
数据带
稳健估计
评估模型
金融时间序列
频数
90B22
Robust
scaling model
GJR model
volatility proxy
GARCH model
60K25
estimates
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Summary:In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient when high-frequency data is taken into the model. However, high-frequency data brings noises and outliers which may lead to big bias of the estimators. Therefore, robust estimates should be taken into consideration. Asymptotic results are derived from the robust M-estimates without the finite fourth moment of the innovations. A simulation study is carried out to assess the performance of the model and its estimates.Robust M-estimate of GJR model is also applied in predicting Va R for real financial time series.
Bibliography:GJR model GARCH model Robust M-estimates scaling model volatility proxy
In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient when high-frequency data is taken into the model. However, high-frequency data brings noises and outliers which may lead to big bias of the estimators. Therefore, robust estimates should be taken into consideration. Asymptotic results are derived from the robust M-estimates without the finite fourth moment of the innovations. A simulation study is carried out to assess the performance of the model and its estimates.Robust M-estimate of GJR model is also applied in predicting Va R for real financial time series.
11-2041/O1
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-015-0488-y