Structured volatility matrix estimation for non-synchronized high-frequency financial data

Jianqing Fan Donggyu Kim

Statistics Theory and Methods mathscidoc:1912.43438

Journal of Econometrics, 209, (1), 61-78, 2019.3
Several large volatility matrix estimation procedures have been recently developed for factor-based It processes whose integrated volatility matrix consists of low-rank and sparse matrices. Their performance depends on the accuracy of input volatility matrix estimators. When estimating co-volatilities based on high-frequency data, one of the crucial challenges is non-synchronization for illiquid assets, which makes their co-volatility estimators inaccurate. In this paper, we study how to estimate the large integrated volatility matrix without using co-volatilities of illiquid assets. Specifically, we pretend that the co-volatilities for illiquid assets are missing, and estimate the low-rank matrix using a matrix completion scheme with a structured missing pattern. To further regularize the sparse volatility matrix, we employ the principal orthogonal complement thresholding method (POET). We also investigate the asymptotic
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@inproceedings{jianqing2019structured,
  title={Structured volatility matrix estimation for non-synchronized high-frequency financial data},
  author={Jianqing Fan, and Donggyu Kim},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114426031451998},
  booktitle={Journal of Econometrics},
  volume={209},
  number={1},
  pages={61-78},
  year={2019},
}
Jianqing Fan, and Donggyu Kim. Structured volatility matrix estimation for non-synchronized high-frequency financial data. 2019. Vol. 209. In Journal of Econometrics. pp.61-78. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114426031451998.
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