Option prices under Bayesian learning: implied volatility dynamics and predictive densities

✍ Scribed by Massimo Guidolin; Allan Timmermann

• Book ID: 104293260
• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 563 KB
• Volume: 27
• Category: Article
• ISSN: 0165-1889
• DOI: 10.1016/s0165-1889(01)00069-0

✦ Synopsis

This paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning e ects. 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.