Option Pricing Model Biases: Bayesian and Markov Chain Monte Carlo Regression Analysis

We investigate systematic and unsystematic option pricing biases in (a) pure jump Levy, (b) jump-diffusion, (c) stochastic volatility, and (d) GARCH models applied to the Black-Scholes-Merton model. We use options data for trades on the S&P500 index from the CBOE. In addition to standard ordinary least square regression, we employ Bayesian regression and Markov Chain Monte Carlo regression to investigate the moneyness and maturity biases of the models. We demonstrate the usefulness of these advanced methodologies as compared to the benchmark techniques.

Automated summary

The study investigates systematic and unsystematic option pricing biases in various models applied to the Black-Scholes-Merton model, including pure jump Levy, jump-diffusion, stochastic volatility, and GARCH models. Options data for S&P500 trades from the CBOE were used, and advanced methodologies such as Bayesian regression and Markov Chain Monte Carlo regression were compared with standard techniques to show their usefulness.