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Forecasting option smile dynamics

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  • Le, Van
  • Zurbruegg, Ralf

Abstract

Practitioners have long tried to exploit the predictability of the option implied volatility smile. Motivated by the recent developments in the literature focusing on market-based option pricing arguments, this paper proposes the introduction of trading volume into a vector autoregressive (VAR) model to improve forecasts of the smile dynamics. The augmented VAR-volume model produces quality forecasts of the smile surface and explains its dynamic changes over time relatively well. Our results suggest that the incorporation of trading volume leads to it outperforming other alternative forecast approaches, as well as being robust to a variety of perturbations of the data and offers scope for investors to more accurately predict option implied volatility in the future.

Suggested Citation

  • Le, Van & Zurbruegg, Ralf, 2014. "Forecasting option smile dynamics," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 32-45.
    • Handle: RePEc:eee:finana:v:35:y:2014:i:c:p:32-45
    DOI: 10.1016/j.irfa.2014.07.006
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    Cited by:

    1. Sudarshan Kumar & Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2023. "Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1615-1644, November.
    2. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.

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    More about this item

    Keywords

    Volume; Implied volatility; Option smile; Forecasting; VAR models;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G19 - Financial Economics - - General Financial Markets - - - Other

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