Add to ORKGWe derive closed-form expansions for the asymptotic distribution of Hansen and Scheinkman [1995. Back to the future: generating moment implications for continuous-time Markov processes. Econometrica 63, 767-804] moment estimators for discretely, and possibly randomly, sampled diffusions. This result makes it possible to select optimal moment conditions as well as to assess the efficiency of the resulting parameter estimators relative to likelihood-based estimators, or to an alternative type of moment conditions.
peer reviewedInspired by the insightful article [4], we revisit the Nualart–Peccati criterion [13] (...
This paper introduces a family of recursively defined estimators of the parameters of a diffusion pr...
For a general class of diffusion processes with multiplicative noise, describing a variety of physic...
Abstract We derive closed form expansions for the asymptotic distribution of Hansen and Scheinkman (...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
Data available on continuos-time diffusions are always sampled discretely in time. In most cases, th...
We introduce a family of generalized-method-of-moments estimators of the pa-rameters of a continuous...
L'estimation des processus de diffusion (affine ou à sauts) est problématique car l'expression de la...
According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estim...
The main problem with the analysis of a Stochastic Differential Equations (SDE) is that the data are...
The objective of the paper is to present a novel methodology for likelihood-based inference for disc...
A review is given of parametric estimation methods for discretely sampled mul- tivariate diffusion p...
AbstractThe maximum likelihood estimation of the unknown parameter of a diffusion process based on a...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The maximum likelihood estimation of the unknown parameter of a diffusion process based on an approx...
peer reviewedInspired by the insightful article [4], we revisit the Nualart–Peccati criterion [13] (...
This paper introduces a family of recursively defined estimators of the parameters of a diffusion pr...
For a general class of diffusion processes with multiplicative noise, describing a variety of physic...
Abstract We derive closed form expansions for the asymptotic distribution of Hansen and Scheinkman (...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
Data available on continuos-time diffusions are always sampled discretely in time. In most cases, th...
We introduce a family of generalized-method-of-moments estimators of the pa-rameters of a continuous...
L'estimation des processus de diffusion (affine ou à sauts) est problématique car l'expression de la...
According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estim...
The main problem with the analysis of a Stochastic Differential Equations (SDE) is that the data are...
The objective of the paper is to present a novel methodology for likelihood-based inference for disc...
A review is given of parametric estimation methods for discretely sampled mul- tivariate diffusion p...
AbstractThe maximum likelihood estimation of the unknown parameter of a diffusion process based on a...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The maximum likelihood estimation of the unknown parameter of a diffusion process based on an approx...
peer reviewedInspired by the insightful article [4], we revisit the Nualart–Peccati criterion [13] (...
This paper introduces a family of recursively defined estimators of the parameters of a diffusion pr...
For a general class of diffusion processes with multiplicative noise, describing a variety of physic...