Semiparametric Estimation and Application of Realized GARCH Model with Time-Varying Leverage Effect

Jinguan Lin, Yizhi Mao (), Hongxia Hao and Guangying Liu
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Jinguan Lin: School of Statistics and Data Science, Nanjing Audit University, No. 86 Yushan Western Road, Nanjing 211815, China
Yizhi Mao: School of Statistics and Data Science, Nanjing Audit University, No. 86 Yushan Western Road, Nanjing 211815, China
Hongxia Hao: School of Statistics and Data Science, Nanjing Audit University, No. 86 Yushan Western Road, Nanjing 211815, China
Guangying Liu: School of Statistics and Data Science, Nanjing Audit University, No. 86 Yushan Western Road, Nanjing 211815, China

Mathematics, 2025, vol. 13, issue 9, 1-26

Abstract: To describe the stylized features of volatility comprehensively, this paper embeds the time-varying leverage effect of volatility into the Realized Generalized AutoRegressive Conditional Heteroskedasticity (RG) model and proposes a new volatility model with a time-varying leverage effect. The Quasi-Maximum Likelihood-Kernel (QML-K) method is proposed to approximate the density function of returns and to estimate the parameters in the new model. Under some mild regularity conditions, the asymptotic properties of the resulting estimators are achieved. Simulation studies demonstrate that the proposed model yields better performances than traditional RG models under different situations. Finally, the empirical analysis shows better finite sample performance of the estimation method and the new model on real data compared with existing methods.

Keywords: volatility model; time-varying leverage effect; realized GARCH model; semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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