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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. TI 2022-075/III

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Financial Econometrics ; Contingent Pricing; Futures Pricing; option pricing ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Financial Crises
• Subject: Options ; Characteristic Function ; Affine Jump-Diffusion ; State Space Representation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Boswijk, H. Peter ; Laeven, Roger J. A. ; Vladimirov, Evgenii
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/273788
• Last update: 10.03.2025, 11:42 AM CET