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Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities

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  • Allan Timmermann

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  • Massimo Guidolin

Abstract

This paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning effects. In the context of an equilibrium model where dividend news evolve on a binomial lattice with unknown but recursively updated probabilities we derive closed-form pricing formulas for European options. Learning is found to generate asymmetric skews in the implied volatility surface and systematic patterns in the term structure of option prices. Data on S&P 500 index option prices is used to back out the parameters of the underlying learning process and to predict the evolution in the cross-section of option prices. The proposed model leads to lower out-of-sample forecast errors and smaller hedging errors than a variety of alternative option pricing models, including Black-Scholes and a GARCH model.

Suggested Citation

  • Allan Timmermann & Massimo Guidolin, 2001. "Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities," FMG Discussion Papers dp397, Financial Markets Group.
  • Handle: RePEc:fmg:fmgdps:dp397
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    More about this item

    JEL classification:

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

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