2022/10/12 by H. Peter Boswijk, Roger J. A. Laeven, Boswijk, H. Peter +3
Economics, Econometrics and Finance · #60G35 #62M20 #Climate Change Policy and Economics #Econometrics (econ.EM) #FOS: Economics and business #Monetary Policy and Economic Impact #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2210.06217
openalex publication_date 2022/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.