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Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?

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  • Alejandro Bernales
  • Massimo Guidolin

Abstract

We develop a general equilibrium asset pricing model under incomplete information and rational learning in order to understand the unexplained predictability of option prices. In our model, the fundamental dividend growth rate is unknown and subject to breaks. Immediately after a break, there is insufficient information to price option contracts accurately. However, as new information arrives, a representative Bayesian agent recursively learns about the parameters of the process followed by fundamentals. We show that learning makes beliefs time-varying and generates predictability patterns across option contracts with different strike prices and maturities; as a result, the implied movements in the implied volatility surface resemble those observed empirically. Keywords: option pricing, rational learning, Bayesian updating, implied volatility, predictability. JEL classification: G12, D83.

Suggested Citation

  • Alejandro Bernales & Massimo Guidolin, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Working Papers 565, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Handle: RePEc:igi:igierp:565
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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. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
    3. Guidolin, Massimo & Wang, Kai, 2023. "The empirical performance of option implied volatility surface-driven optimal portfolios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Tanha, Hassan & Dempsey, Michael, 2016. "The evolving dynamics of the Australian SPI 200 implied volatility surface," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 44-57.
    5. Du, Kai, 2019. "Investor expectations, earnings management, and asset prices," Journal of Economic Dynamics and Control, Elsevier, vol. 105(C), pages 134-157.
    6. Bernales, Alejandro & Cañón, Carlos & Verousis, Thanos, 2018. "Bid–ask spread and liquidity searching behaviour of informed investors in option markets," Finance Research Letters, Elsevier, vol. 25(C), pages 96-102.
    7. Bernales, Alejandro & Verousis, Thanos & Voukelatos, Nikolaos, 2020. "Do investors follow the herd in option markets?," Journal of Banking & Finance, Elsevier, vol. 119(C).
    8. Martin Magris & Perttu Barholm & Juho Kanniainen, 2017. "Implied volatility smile dynamics in the presence of jumps," Papers 1711.02925, arXiv.org, revised May 2020.
    9. Alejandro Bernales & Thanos Verousis & Nikolaos Voukelatos & Mengyu Zhang, 2020. "What do we know about individual equity options?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 67-91, January.
    10. Chen, Ying & Grith, Maria & Lai, Hannah L. H., 2023. "Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach," MPRA Paper 119022, University Library of Munich, Germany.

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    More about this item

    Keywords

    option pricing; rational learning; bayesian updating; implied volatility; predictability. jel classification: g12; d83.;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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