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Robust M-estimate of GJR Model with High Frequency Data 被引量：3

Robust M-estimate of GJR Model with High Frequency Data
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摘要 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. 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.
作者 Jin-shan HUANG Wu-qing WU Zhao CHEN Jian-jun ZHOU
机构地区 Department of Statistics and Finance School of Business Princeton University Department of Statistics
出处 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2015年第3期591-606,共16页 应用数学学报（英文版）
基金 Supported by National Natural Science Foundation of China(Grant No.71003100) the Research Funds of Renmin University of China(No.11XNK027)
关键词 GJR model GARCH model Robust M-estimates scaling model volatility proxy GJR model GARCH model Robust M-estimates scaling model volatility proxy
分类号 O212.1 [理学—概率论与数理统计]
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同被引文献15

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Acta Mathematicae Applicatae Sinica
2015年 第3期

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