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

58 Pages Posted: 24 Mar 2001

See all articles by Massimo Guidolin

Massimo Guidolin

Bocconi University, Dept. of Finance; Bocconi University - CAREFIN - Centre for Applied Research in Finance

Allan Timmermann

UCSD ; Centre for Economic Policy Research (CEPR)

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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.

JEL Classification: G12, D83

Suggested Citation

Guidolin, Massimo and Timmermann, Allan, Option Prices Under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities. EFMA 2001 Lugano Meetings, Available at SSRN: https://ssrn.com/abstract=264655 or http://dx.doi.org/10.2139/ssrn.264655

Massimo Guidolin (Contact Author)

Bocconi University, Dept. of Finance ( email )

Via Roentgen, 1
2nd floor
Milan, MI 20136
Italy

Bocconi University - CAREFIN - Centre for Applied Research in Finance

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Milan, 20136
Italy

Allan Timmermann

UCSD ( email )

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United States
858-534-0894 (Phone)

HOME PAGE: http://rady.ucsd.edu/people/faculty/timmermann/

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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