Selected Recent Developments in GARCH Models: A Narrative Review

Abstract

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework introduced by Bollerslev remains one of the standard tools for modeling conditional volatility in financial time series, value-at-risk measurement, and related applications. At the same time, recent literature shows that the contemporary development of GARCH-based research has been shaped by several recurring themes, including asymmetric volatility modeling, dynamic dependence structures, estimation under heavy tails, and hybrid forecasting architectures. This review provides a selective narrative survey of recent developments while restricting detailed discussion to studies whose bibliographic information can be checked or substantially corroborated. In addition to recent work, the paper includes an earlier Chinese-language contribution by Yang (2012) as a background reference for distributional assumptions in VaRoriented GARCH applications.

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