Factor GARCH-It models for high-frequency data with application to large volatility matrix prediction

Donggyu Kim Jianqing Fan

Statistics Theory and Methods mathscidoc:1912.43424

Journal of econometrics, 208, (2), 395-417, 2019.2
Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these nonparametric estimators do not have a dynamic structure to implement. In this paper, we introduce a novel It diffusion process based on the approximate factor models and call it a factor GARCH-It model. We then investigate its properties and propose a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-It model. We also apply it to estimating conditional expected large volatility matrices and establish their asymptotic properties. Simulation studies are conducted to validate the finite sample performance of the proposed
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@inproceedings{donggyu2019factor,
  title={Factor GARCH-It models for high-frequency data with application to large volatility matrix prediction},
  author={Donggyu Kim, and Jianqing Fan},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114336771115984},
  booktitle={Journal of econometrics},
  volume={208},
  number={2},
  pages={395-417},
  year={2019},
}
Donggyu Kim, and Jianqing Fan. Factor GARCH-It models for high-frequency data with application to large volatility matrix prediction. 2019. Vol. 208. In Journal of econometrics. pp.395-417. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114336771115984.
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