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Wang, M., Chen, Z., & Wang, C. D. (2018). Composite Quantile Regression for GARCH Models Using High-Frequency Data. Econometrics and Statistics, 7, 115-133.
https://doi.org/10.1016/j.ecosta.2016.11.004

has been cited by the following article:

• TITLE: Estimation and Application of GARCH-X Model Based on High-Frequency Data

  AUTHORS: Zefang Song, Lingjun Chen, Wenlin Huang

  KEYWORDS: High-Frequency Data, Exogenous Variables, GARCH-X Model, QMLE Estimation, VaR Forecasting

  JOURNAL NAME: American Journal of Industrial and Business Management, Vol.15 No.2, February 20, 2025

  ABSTRACT: This paper aims to study the GARCH-X model based on high-frequency data. Building upon the existing research on the selection criteria for optimal volatility representation and parameter estimation methods for high-frequency data GARCH(1,1) models, we extend the model to incorporate exogenous variables within high-frequency data GARCH(1,1) models and conduct both model simulations and empirical validation. The empirical research results show that in the study of the returns of the CSI 300 Index, including the 500 Index return rate as an exogenous variable can better explain the volatility of the return rate, thereby demonstrating the practical applicability of the model in real-world applications.