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Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of Volatility

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  • Ding, Y.

Abstract

We propose a model that extends Smetanina's (2017) original RT-GARCH model by allowing conditional heteroskedasticity in the variance of volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a second-order difference equation as opposed to first-order under GARCH(1,1) and RT-GARCH(1,1). Empirical studies confirm the presence of conditional heteroskedasticity in the volatility process and the standardised residuals of return are close to Gaussian under this model. We show we are able to obtain better in-sample nowcast and out-of-sample forecast of volatility.

Suggested Citation

  • Ding, Y., 2021. "Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of Volatility," Cambridge Working Papers in Economics 2112, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2112
    Note: yd274
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    References listed on IDEAS

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    More about this item

    Keywords

    GARCH; diffusion limit; forecasting; volatility of volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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