A factor-GARCH model for high dimensional volatilities

Tools

Li, Xiao-ling and Li, Yuan and Pan, Jia-zhu and Zhang, Xing-fa (2022) A factor-GARCH model for high dimensional volatilities. Acta Mathematicae Applicatae Sinica, 38 (3). 635–663. ISSN 1618-3932 (https://doi.org/10.1007/s10255-022-1104-6)

[thumbnail of Li-etal-AMAS-2021-A-factor-GARCH-model-for-high-dimensional-volatilities]
Preview
 Text. Filename: Li_etal_AMAS_2021_A_factor_GARCH_model_for_high_dimensional_volatilities.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

Abstract

This paper proposes a method for modelling volatilities (conditional covariance matrices) of high dimensional dynamic data. We combine the ideas of approximate factor models for dimension reduction and multivariate GARCH models to establish a model to describe the dynamics of high dimensional volatilities. Sparsity condition and thresholding technique are applied to the estimation of the error covariance matrices, and quasi maximum likelihood estimation (QMLE) method is used to estimate the parameters of the common factor conditional covariance matrix. Asymptotic theories are developed for the proposed estimation. Monte Carlo simulation studies and real data examples are presented to support the methodology.

ORCID iDs

Li, Xiao-ling, Li, Yuan, Pan, Jia-zhu ORCID logoORCID: https://orcid.org/0000-0001-7346-2052 and Zhang, Xing-fa;
  • Item type:Article
    ID code:81383
    Dates:
    Date
    Event
    1 July 2022
    Published
    15 December 2021
    Accepted
    22 July 2021
    Submitted
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:07 Jul 2022 09:49
    Last modified:11 Nov 2024 13:33
    URI:https://strathprints.strath.ac.uk/id/eprint/81383
CORE (COnnecting REpositories)