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) |
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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 |
All Science Journal Classification (ASJC) codes
- Applied Mathematics