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

Author

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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)

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.

Suggested Citation

  • Jinguan Lin & Yizhi Mao & Hongxia Hao & Guangying Liu, 2025. "Semiparametric Estimation and Application of Realized GARCH Model with Time-Varying Leverage Effect," Mathematics, MDPI, vol. 13(9), pages 1-26, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1506-:d:1648583
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    References listed on IDEAS

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