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

H. Peter Boswijk, Roger Laeven and Evgenii Vladimirov
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H. Peter Boswijk: University of Amsterdam
Evgenii Vladimirov: University of Amsterdam

No 22-000/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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 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.

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: 2022-11-13
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Related works:
Journal Article: Estimating option pricing models using a characteristic function-based linear state space representation (2024)
Working Paper: Estimating Option Pricing Models Using a Characteristic Function-Based Linear State Space Representation (2022)
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