Robust M-estimate of GJR model with high frequency data

Jin shan Huang, Wu qing Wu, Zhao Chen, Jian jun Zhou

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)591-606
Number of pages16
JournalActa Mathematicae Applicatae Sinica
Volume31
Issue number3
DOIs
StatePublished - Jul 23 2015

Fingerprint

M-estimates
High-frequency Data
Robust Estimate
Model
Financial Time Series
Outlier
Time series
Innovation
Simulation Study
Scaling
Moment
Estimator
Estimate

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

Cite this

Huang, Jin shan ; Wu, Wu qing ; Chen, Zhao ; Zhou, Jian jun. / Robust M-estimate of GJR model with high frequency data. In: Acta Mathematicae Applicatae Sinica. 2015 ; Vol. 31, No. 3. pp. 591-606.
@article{9000a883821949e6a1b0049a92b94327,
title = "Robust M-estimate of GJR model with high frequency data",
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.",
author = "Huang, {Jin shan} and Wu, {Wu qing} and Zhao Chen and Zhou, {Jian jun}",
year = "2015",
month = "7",
day = "23",
doi = "10.1007/s10255-015-0488-y",
language = "English (US)",
volume = "31",
pages = "591--606",
journal = "Acta Mathematicae Applicatae Sinica",
issn = "0168-9673",
publisher = "Springer Verlag",
number = "3",

}

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 journalArticle

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 -