EconPapers    
Economics at your fingertips  
 

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

Donggyu Kim and Jianqing Fan

Journal of Econometrics, 2019, vol. 208, issue 2, 395-417

Abstract: 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.

Keywords: Factor model; GARCH; Low-rank; POET; Quasi-maximum likelihood estimator; Sparsity (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 C55 C58 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407618301957
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:208:y:2019:i:2:p:395-417

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-07-27
Handle: RePEc:eee:econom:v:208:y:2019:i:2:p:395-417