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.
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