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Modelling Intraday Realized Volatility: The Role Of Vix, Oil And Gold

Author

Listed:
  • Avraham Turgeman

    (West University of Timisoara)

  • Claudiu Botoc

    (West University of Timisoara)

  • Marilen Pirtea

    (West University of Timisoara)

  • Octavian Jude

    (West University of Timisoara)

Abstract

The main aim of the paper is to test an autoregressive implied volatility (IV) model that can significantly predict realized volatility (RV) of stock index. Subsequently, we want to test the predictive power of products that are external to the index of interest (S&P), by including certain commodities that are derived from VIX, i.e., crude oil and gold. The results do not reject the memory effect, given the predictive power of several lags for VIX over realized volatility. Furthermore, crude oil volatility is a significant predictor, alternatively in realized volatility and implied volatility. Finally, gold implied volatility (with higher lags) predicts stock returns volatility, which suggests a gap since traders tend to start gaining gold earlier to be on the safe side. Our findings have certain implications for trading and risk estimation.

Suggested Citation

  • Avraham Turgeman & Claudiu Botoc & Marilen Pirtea & Octavian Jude, 0000. "Modelling Intraday Realized Volatility: The Role Of Vix, Oil And Gold," Proceedings of Economics and Finance Conferences 14115804, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iefpro:14115804
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Implied volatility; Realized volatility; AR model; Forecasting;
    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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