Working Paper: CEPR ID: DP3005
Authors: Massimo Guidolin; Allan Timmermann
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 evolves 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.
Keywords: Bayesian Learning; Black-Scholes; Option Pricing Model; Option Prices
JEL Codes: D83; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Bayesian learning (C11) | option pricing (G13) |
| Bayesian learning (C11) | implied volatility (C69) |
| implied volatility (C69) | option pricing (G13) |
| Bayesian learning (C11) | asymmetric skews in implied volatility (C46) |
| Bayesian learning (C11) | shifts in the term structure of option prices (G13) |
| Bayesian learning (C11) | perceived state price densities (P22) |
| perceived state price densities (P22) | option pricing (G13) |
| Bayesian learning (C11) | biases in Black-Scholes model (G13) |
| Bayesian learning (C11) | lower out-of-sample forecast errors (C53) |
| Bayesian learning (C11) | smaller hedging errors (G41) |