Bayesian synthetic likelihood for stochastic models with applications in mathematical finance

We present a Bayesian synthetic likelihood method to estimate both the parameters and their uncertainty in systems of stochastic differential equations. Together with novel summary statistics the method provides a generic and model-agnostic estimation procedure and is shown to perform well even for...

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Библиографические подробности

Главные авторы:	Ramona Maraia, Sebastian Springer, Teemu Härkönen, Martin Simon, Heikki Haario
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Frontiers Media S.A. 2023-06-01
Серии:	Frontiers in Applied Mathematics and Statistics
Предметы:	synthetic likelihood SDE systems parameter estimation MCMC sampling goodness of fit financial models
Online-ссылка:	https://www.frontiersin.org/articles/10.3389/fams.2023.1187878/full
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