Boswijk, H., Laeven, R. and Vladimirov, E. (2024). Estimating option pricing models using a characteristic function-based linear state space representation Journal of Econometrics, 244(1).

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{\textquoteright}s state vector. We formally derive an associated linear state space representation and the asymptotic properties of the corresponding measurement errors. The state space representation allows us to use a suitably modified Kalman filtering technique to learn about the latent state vector and a quasi-maximum likelihood estimator of the model parameters, for which we establish asymptotic inference results. Accordingly, the filtering and estimation procedure brings important computational advantages. We analyze the finite-sample behavior of our procedure in Monte Carlo simulations. The applicability of our procedure is illustrated in two case studies that analyze S\&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction and economic policy uncertainty.