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Predictability in implied volatility surfaces: evidence from the Euro OTC FX market

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  • Georgios Chalamandaris
  • Andrianos E. Tsekrekos

Abstract

Recent general equilibrium models prescribe predictable dynamics in the volatility surfaces that are implied by observed option prices. In this paper, we investigate the predictability of surfaces, using extensive time series of implied volatilities from over-the-counter options on eight different currencies, quoted against the Euro. We examine implied volatility surfaces in the context of predictability through three different models, two that employ parametric specifications to describe the surface and one that decomposes it into latent statistical factors. All examined models are shown to (a) accurately describe the surfaces in-sample, and (b) produce forecasts that are superior to hard-to-beat benchmarks that ignore information about the shape of the surface, in medium- to long-term horizons. We show that these forecasts can support profitable volatility trading strategies in the absence of transaction costs. Comparing across competing models, our results suggest that parametric models, that allow for a more structured description of the surface, are more successful in terms of forecasts' accuracy and significance of trading profits.

Suggested Citation

  • Georgios Chalamandaris & Andrianos E. Tsekrekos, 2014. "Predictability in implied volatility surfaces: evidence from the Euro OTC FX market," The European Journal of Finance, Taylor & Francis Journals, vol. 20(1), pages 33-58, January.
  • Handle: RePEc:taf:eurjfi:v:20:y:2014:i:1:p:33-58
    DOI: 10.1080/1351847X.2012.670123
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    Cited by:

    1. Kearney, Fearghal & Shang, Han Lin & Sheenan, Lisa, 2019. "Implied volatility surface predictability: The case of commodity markets," Journal of Banking & Finance, Elsevier, vol. 108(C).
    2. 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.
    3. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    4. Connor J.A. Stuart & Sebastian A. Gehricke & Jin E. Zhang & Xinfeng Ruan, 2021. "Implied volatility smirk in the Australian dollar market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4573-4599, September.

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