Arbeitspapier

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

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.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. TI 2022-075/III

Klassifikation
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
Thema
Options
Characteristic Function
Affine Jump-Diffusion
State Space Representation

Ereignis
Geistige Schöpfung
(wer)
Boswijk, H. Peter
Laeven, Roger J. A.
Vladimirov, Evgenii
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Boswijk, H. Peter
  • Laeven, Roger J. A.
  • Vladimirov, Evgenii
  • Tinbergen Institute

Entstanden

  • 2022

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