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Statistical Inference for Unified Garch–Itô Models with High-Frequency Financial Data. (2016). Kim, Donggyu.
In: Journal of Time Series Analysis.
RePEc:bla:jtsera:v:37:y:2016:i:4:p:513-532.

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Cites: 22

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  1. Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial data. (2023). Hao, Hong-Xia ; Lin, Jin-Guan ; Fu, Jin-Yu.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:4:p:1698-1712.

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  3. Forecasting price of financial market crash via a new nonlinear potential GARCH model. (2021). Long, Chao ; Li, Jiang-Cheng ; Xing, Dun-Zhong.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:566:y:2021:i:c:s037843712030947x.

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  4. Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction. (2019). Fan, Jianqing ; Kim, Donggyu.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:208:y:2019:i:2:p:395-417.

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References

References cited by this document

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  12. Kim D Wang Y 2014 Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data
    Paper not yet in RePEc: Add citation now
  13. Kim D Wang Y Zou J 2015 Asymptotic theory for large volatility matrix estimation based on high-frequency financial data
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  14. Li, 2015. Generalized methods of integrated moments for high-frequency data
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