Estimating option pricing models using a characteristic function-based linear state space representation

H. Peter Boswijk, Roger Laeven and Evgenii Vladimirov

Journal of Econometrics, 2024, vol. 244, issue 1

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.

Keywords: Options; Characteristic function; Affine jump-diffusion; State space representation (search for similar items in EconPapers)
JEL-codes: C13 C32 C58 G01 G13 (search for similar items in EconPapers)
Date: 2024
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Related works:
Working Paper: Estimating Option Pricing Models Using a Characteristic Function-Based Linear State Space Representation (2022)
Working Paper: Estimating Option Pricing Models Using a Characteristic Function Based Linear State Space Representation (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:244:y:2024:i:1:s0304407624002094

DOI: 10.1016/j.jeconom.2024.105864

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