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 journalArticlepeer-review

4 Scopus citations


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
Issue number3
StatePublished - Jul 23 2015

All Science Journal Classification (ASJC) codes

  • Applied Mathematics


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