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

Donggyu Kim, Jianqing Fan

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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 Itô diffusion process based on the approximate factor models and call it a factor GARCH-Itô model. We then investigate its properties and propose a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-Itô 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 estimation methods. The proposed method is also illustrated by using data from the constituents of the S&P 500 index and an application to constructing the minimum variance portfolio with gross exposure constraints.

Original languageEnglish (US)
Pages (from-to)395-417
Number of pages23
JournalJournal of Econometrics
Issue number2
StatePublished - Feb 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Economics and Econometrics


  • Factor model
  • Low-rank
  • POET
  • Quasi-maximum likelihood estimator
  • Sparsity


Dive into the research topics of 'Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction'. Together they form a unique fingerprint.

Cite this