Abstract: We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function which is functionally affine in the model’s state vector. Exploiting the model’s corresponding linear state space representation allows us to use suitably extended collapsed Kalman-type filtering techniques and brings important computational advantages. We establish the asymptotic properties of our procedure and analyze its finite-sample behavior in Monte Carlo simulations. We illustrate the applicability of our procedure in two case studies that analyze S&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction.
Work in process