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A new technique for simulating the likelihood of stochastic differential equations
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Autores
Orientador(es)
Resumo(s)
This article presents a new simulation-based technique for estimating the likelihood of stochastic differential equations. This technique is based on a result of Dacunha-Castelle and Florens-Zmirou. These authors proved that the transition densities of a nonlinear diffusion process with a constant diffusion coefficient can be written in a closed form involving a stochastic integral. We show that this stochastic integral can be easily estimated through simulations and we prove a convergence result. This simulator for the transition density is used to obtain the simulated maximum likelihood (SML) estimator. We show through some Monte Carlo experiments that our technique is highly computationally efficient and the SML estimator converges rapidly to the maximum likelihood estimator
Descrição
Palavras-chave
Simulated Maximum Likelihood Estimator Simulation-based Method Estimation Stochastic Differential Equations Transition Density Estimation Diffusion Processes
Contexto Educativo
Citação
Nicolau, João .(2002). “A new technique for simulating the likelihood of stochastic differential equations”. Econometrics Journal, Volume 5: pp. 91–103. (Search PDF in 2023)
Editora
Royal Economic Society | Blackwell Publishers Ltd.