An Event-Driven Analysis of Volatility Forecasting for U.S. and Chinese Technology Indices: Application of GARCH Family Models

DOI: https://doi.org/10.62517/jse.202511605

Author(s)

Lan Huang

Affiliation(s)

City University of Hong Kong, Economics and Finance Department, Hong Kong, China

Abstract

This study investigates the distinct volatility dynamics of the U.S. (NASDAQ-100) and Chinese (ChiNext) technology stock markets in response to major geopolitical and economic shocks. Utilizing daily data from 2017 to 2024, the study employs GARCH, EGARCH, and TGARCH models to analyze market behavior, with a specific focus on four key events including the U.S.-China Trade War and the COVID-19 pandemic. The results reveal a strong, classic leverage effect in the NASDAQ-100, for which the EGARCH model provides the best fit. In contrast, the ChiNext index exhibits a weaker, threshold-based asymmetry best captured by the TGARCH model. While the models effectively captured event-driven volatility through their asymmetric components, diagnostic tests indicate that standard GARCH frameworks, though adequate for the NASDAQ-100, are insufficient to fully model the more complex risk structure of the ChiNext market. The findings underscore that risk characteristics are fundamentally market-specific, necessitating tailored modeling approaches for effective global risk management.

Keywords

Volatility Forecasting; GARCH; Event Study; Technology Indices; Leverage Effect

References

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