Fast Bayesian Calibration of Option Pricing Models Based on Sequential Monte Carlo Methods and Deep Learning.
Model calibration is a challenging yet fundamental task in financial engineering. Using sequential Monte Carlo methods, we reformulate the nonconvex optimization problem as a Bayesian estimation task. This allows to compute any statistic of the estimated parameters, mitigating the strong dependence...
| Publicado en: | Journal of Financial Econometrics Vol. 24; no. 3; pp. 1 - 25 |
|---|---|
| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
2026
|
| Materias: | |
| 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=194637004&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 194637004 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: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 194637004 10.1093/jjfinec/nbag011 ppf: 1 ppct: 24 formats: tig: atl: Fast Bayesian Calibration of Option Pricing Models Based on Sequential Monte Carlo Methods and Deep Learning. aug: au: Brignone, Riccardo Gonzato, Luca Knaust, Sven Lütkebohmert, Eva affil: Department of Economics and Management, University of Pavia, Pavia, Italy Department of Statistics and Operations Research, University of Vienna, Vienna, Austria Department of Economics, University of Freiburg, Freiburg i. Br, Germany su: Monte Carlo method Bayes' estimation Standard & Poor's 500 Index Markov chain Monte Carlo Financial engineering Deep learning Artificial neural networks sug: subj: Monte Carlo method Bayes' estimation Standard & Poor's 500 Index Markov chain Monte Carlo Financial engineering Deep learning Artificial neural networks keyword: Bayesian estimation C45 C58 C61 C63 copyrightHolder:Oxford University Press copyrightYear:2026 deep learning finance G13 inLanguage:en option pricing models publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag011 sequential Monte Carlo Bayesian estimation C45 C58 C61 C63 copyrightHolder:Oxford University Press copyrightYear:2026 deep learning finance G13 inLanguage:en option pricing models publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag011 sequential Monte Carlo ab: Model calibration is a challenging yet fundamental task in financial engineering. Using sequential Monte Carlo methods, we reformulate the nonconvex optimization problem as a Bayesian estimation task. This allows to compute any statistic of the estimated parameters, mitigating the strong dependence on starting points and avoiding the troublesome local minima, that plague standard calibration methods. To accelerate computation, we incorporate Markov chain Monte Carlo methods with delayed acceptance and a neural network-based option pricing approach. When applied to S&P 500 index options, our Bayesian algorithms significantly outperform the standard approach in terms of runtime, accuracy, and statistical fit. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|