Paper by Peter Boswijk, Roger Laeven and Evgenii Vladimirov has appeared in the Journal of Econometrics
The paper 'Estimating option pricing models using a characteristic function-based linear state space representation' by research fellow Peter Boswijk (University of Amsterdam), candidate fellow Evgenii Vladimirov (Erasmus University Rotterdam) and alumnus and faculty member Roger Laeven (University of Amsterdam) has
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. 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.
Article citation
H. Peter Boswijk, Roger J.A. Laeven and Evgenii Vladimirov. August 2024. "Estimating option pricing models using a characteristic function-based linear state space representation" Journal of Econometrics, Vol.