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Time-Varying Volatility Asymmetry: A Conditioned HAR-RV(CJ) EGARCH-M Model

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  • Ceylan, Ozcan

    (Galatasaray University Economic Research Center)

Abstract

Based on the recent developments in the high-frequency econometrics and asymmetric GARCH modeling literature, I develop a novel model that accounts for the volatility feedback and leverage effects, effectively incorporating signed continuous and jump components of the realized variance in the variance specification through an HAR forecasting model. I then condition the variance specification on the lagged realized variance and the risk aversion (that is proxied by the variance risk premium level) to analyze the eventual state-dependent variations in the volatility asymmetry. I find that the volatility asymmetry is clearly more pronounced in the periods of market stress marked by high levels of volatility and risk aversion. In addition, I reveal a further asymmetry in the asymmetric reaction patterns of the volatility to good and bad news: while the market moves through the periods of higher volatility and risk aversion, the impact of a bad news increases much more heavily than that of good news pointing to the fact that the investors become more sensible to bad news in market downturns.

Suggested Citation

  • Ceylan, Ozcan, 2012. "Time-Varying Volatility Asymmetry: A Conditioned HAR-RV(CJ) EGARCH-M Model," GIAM Working Papers 12-4, Galatasaray University Economic Research Center.
    • Handle: RePEc:ris:giamwp:2012_004
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    2. Nagapetyan, Artur, 2019. "Precondition stock and stock indices volatility modeling based on market diversification potential: Evidence from Russian market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 45-61.
    3. Bahmani, Mohammad & Sheikh Ahmadi, Sayed Amir & Sanginabadi, Bahram, 2013. "Return Volatility and Asymmetric News of Computer Industry stocks in Tehran Stock Exchange (TEX)," MPRA Paper 70793, University Library of Munich, Germany, revised 15 Mar 2014.
    4. Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.

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    More about this item

    Keywords

    Time-varying volatility asymmetry; High-frequency econometrics; EGARCH-M; HAR models; Volatility components; Variance risk premium;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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