  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. We formally derive an associated linear state space representation and
establish 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, which 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.
