Abstract
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 VaR for real financial time series.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 591-606 |
| Number of pages | 16 |
| Journal | Acta Mathematicae Applicatae Sinica |
| Volume | 31 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 23 2015 |
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All Science Journal Classification (ASJC) codes
- Applied Mathematics
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Robust M-estimate of GJR model with high frequency data. / Huang, Jin shan; Wu, Wu qing; Chen, Zhao; Zhou, Jian jun.
In: Acta Mathematicae Applicatae Sinica, Vol. 31, No. 3, 23.07.2015, p. 591-606.Research output: Contribution to journal › Article
TY - JOUR
T1 - Robust M-estimate of GJR model with high frequency data
AU - Huang, Jin shan
AU - Wu, Wu qing
AU - Chen, Zhao
AU - Zhou, Jian jun
PY - 2015/7/23
Y1 - 2015/7/23
N2 - 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 VaR for real financial time series.
AB - 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 VaR for real financial time series.
UR - http://www.scopus.com/inward/record.url?scp=84937518964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937518964&partnerID=8YFLogxK
U2 - 10.1007/s10255-015-0488-y
DO - 10.1007/s10255-015-0488-y
M3 - Article
VL - 31
SP - 591
EP - 606
JO - Acta Mathematicae Applicatae Sinica
JF - Acta Mathematicae Applicatae Sinica
SN - 0168-9673
IS - 3
ER -