CEBA Talk: Estimating Option Pricing Models Using a Characteristic Function Based Linear State Space Representation

Speaker: Evgenii Vladimirov (PhD candidate at the University of Amsterdam and Tinbergen Institute) Abstract: We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jumpdiffusions. Our procedure is based on the comparison between an optionimplied, modelfree representation of the conditional logcharacteristic function and the modelimplied conditional logcharacteristic function which is functionally affine in the models state vector. Exploiting the models corresponding linear state space representation allows us to use suitably extended collapsed Kalmantype filtering techniques and brings important computational advantages. We establish the asymptotic properties of our procedure and analyze its finitesample 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 Covid19 reproduction.