Work

Estimation of Conditional Covariance Matrices for a Class of High-Dimensional Varying Coefficient Factor-Generalized Autoregressive Conditional Heteroscedasticity Model

Year: 2024

Type: article

Abstract: This paper explores a class of High-Dimensional Varying Coefficient Factor-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, designed to capture dynamic relationships between variables and account for the heterogeneity of time series data. By exploiting the structure of the model, a mixed approach is proposed to estimate conditional covariance matrices. Furthermore, asymptotic properties for the estimators are established, providing a theoretical foundation for their consistency and efficiency. A simulation study is conducted to demonstrate the performance of estimators, and a real data example of portfolio allocation is presented to illustrate the practical application of the approach.

Source: Symmetry

DOI: 10.3390/sym16121635

Authors: Yujiao Liu, Yuan Li, Xingfa Zhang

Institutions: Guangzhou University, Shenzhen Polytechnic University

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: Basic and Applied Basic Research Foundation of Guangdong Province