A long short-term memory enhanced realized conditional heteroskedasticity model

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. • Long short-term memory neural network is employed to enhance realized GARCH. • Nonlinear and long-term dependence between realized data and volatility is captured. • Bayesian Sequential Monte Carlo is used for inference and sequential forecasting. • A comprehensive empirical study covering 31 global stock indices is conducted. • The proposed model improves the accuracy in capturing the dynamics of volatility.