A long short-term memory enhanced realized conditional heteroskedasticity model

Chen Liu, Chao Wang, Minh-Ngoc Tran and Robert Kohn

Economic Modelling, 2025, vol. 142, issue C

Abstract: This paper examines the potential of using realized volatility measures for capturing financial markets’ uncertainty. Earlier studies show the usefulness of the high-frequency data based Generalized AutoRegressive Conditional Heteroskedasticity (RealGARCH) model for enhancing volatility forecasting accuracy; however, this model focuses only on linear and short-term dependencies of realized volatility measures on the underlying volatility. Recognizing the critical economic implications of this limitation, the long short-term memory neural network is integrated into RealGARCH, aiming to explore the full impact of realized volatility on volatility modeling and forecasting via capturing the nonlinear and long-term effects. A comprehensive empirical study using 31 indices from 2004 to 2021 is conducted. The results demonstrate that our proposed framework achieves superior in-sample and out-of-sample performance compared to several benchmark models. Importantly, it retains interpretability and effectively adapts to the stylized facts observed in volatility, emphasizing its significant potential for enhancing economic decision-making and risk management.

Keywords: Conditional heteroskedasticity; Long short-term memory; Volatility modeling; Realized volatility measure (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:142:y:2025:i:c:s0264999324002797

DOI: 10.1016/j.econmod.2024.106922

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