• Media type: E-Book
  • Title: Factor Overnight GARCH-Itô Models
  • Contributor: Kim, Donggyu [VerfasserIn]; Oh, Minseog [VerfasserIn]; Song, Xinyu [VerfasserIn]; Wang, Yazhen [VerfasserIn]
  • imprint: [S.l.]: SSRN, 2023
  • Published in: KAIST College of Business Working Paper Series
  • Extent: 1 Online-Ressource (51 p)
  • Language: English
  • DOI: 10.2139/ssrn.4342551
  • Identifier:
  • Keywords: Factor Model ; High Dimensionality ; POET ; Quasi-Maximum Likelihood Estimation ; Realized Volatility Matrix Estimator
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 28, 2023 erstellt
  • Description: This paper introduces a unified factor overnight GARCH-Itô Models model for large volatility matrix estimation and prediction. To account for whole-day market dynamics, the proposed model has two different instantaneous factor volatility processes for the open-to-close and close-to-open periods, while each embeds the discrete-time multivariate GARCH model structure. To estimate latent factor volatility, we assume the low rank plus sparse structure and employ non-parametric estimation procedures.Then, based on the connection between the discrete-time model structure and the continuous-time diffusion process, we propose a weighted least squares estimation procedure with the non-parametric factor volatility estimator and establish its asymptotic theorems
  • Access State: Open Access