A Non-Gaussian, Structure-Preserving Stochastic Volatility and Option Pricing Model in Discrete Time.
We propose a novel stochastic volatility model based on the autoregressive gamma process that accommodates a structure-preserving change to the risk-neutral measure while relying on a non-Gaussian distribution for the return innovations. The model employs the Meixner (MXN) distribution, which enrich...
| Publicado en: | Journal of Financial Econometrics Vol. 24; no. 2; pp. 1 - 25 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192849869&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192849869 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: 2026 vid: 24 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192849869 10.1093/jjfinec/nbag004 ppf: 1 ppct: 24 formats: tig: atl: A Non-Gaussian, Structure-Preserving Stochastic Volatility and Option Pricing Model in Discrete Time. aug: au: Feistle, Simon Fengler, Matthias R Melnikov, Alexander affil: School of Economics and Political Science, University of St.Gallen, Rosenbergstrasse 22, St.Gallen, 9000, Switzerland School of Economics and Political Science, University of St.Gallen, Rosenbergstrasse 22, St.Gallen, 9000, SwitzerlandSwiss Finance Institute, Walchestrasse 9, Zürich, 8006, Switzerland su: Discrete-time systems Stochastic processes Distribution (Probability theory) Maximum likelihood statistics Market volatility Price fluctuations Gaussian distribution sug: subj: Discrete-time systems Stochastic processes Distribution (Probability theory) Maximum likelihood statistics Market volatility Price fluctuations Gaussian distribution keyword: approximate maximum likelihood autoregressive gamma process C22 copyrightHolder:Oxford University Press copyrightYear:2026 discrete-time option pricing exponentially affine models G12 G13 inLanguage:en Meixner distribution publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag004 stochastic volatility approximate maximum likelihood autoregressive gamma process C22 copyrightHolder:Oxford University Press copyrightYear:2026 discrete-time option pricing exponentially affine models G12 G13 inLanguage:en Meixner distribution publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag004 stochastic volatility ab: We propose a novel stochastic volatility model based on the autoregressive gamma process that accommodates a structure-preserving change to the risk-neutral measure while relying on a non-Gaussian distribution for the return innovations. The model employs the Meixner (MXN) distribution, which enriches the return dynamics with conditional stochastic skewness and kurtosis. We propose a fast and accurate estimation method by combining the approximate maximum likelihood method of David S. Bates with a numerical integration technique suitable for highly oscillatory functions. We derive a closed-form discrete-time option pricing formula. The MXN model performs particularly well, compared to benchmarks within its class and of the generalized autoregressive conditional heteroskedasticity family, when calibrated directly to option data and when applied to option data with a high level of implied volatility, such as Bitcoin. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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