LAN property for ergodic diffusions with discrete observations
By Emmanuel Gobet. 2002
134 citations recorded by the source corpus.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/750f8c9d22d89200b0ba1841033c78d4cfe715a3
How researchers see this paper
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In Finance
- Michael SØrensen
- Omiros Papaspiliopoulos
- Arnaud Gloter
- Gareth O. Roberts
- Hiroki Masuda
- Paul Fearnhead
- J. Forman
- Alexandros Beskos
In Statistics and Probability
- Omiros Papaspiliopoulos
- Gareth O. Roberts
- Paul Fearnhead
- Michael SØrensen
- Alexandros Beskos
- Hiroki Masuda
- Markus Reiß
- J. Forman
Citations in the corpus
- Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) (2006, methodology): “None-the-less, theoretical properties of such MLEs are now well known in particular under ergodicity assumptions; see for example Kessler (1997) and Gobet (2002).”
- The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes (2008, methodology): “Therefore, we exploit that Gobet (2002) has shown that discretely observed diffusion processes are locally asymptotically normal in the high frequency limit where → 0 and n →∞ and has given an expression for the Fisher information.”
- Convergence of Gaussian quasi-likelihood random fields for ergodic Lévy driven SDE observed at high frequency (2013, background): “On one hand, for the diffusion case (where c ≡ 0), the GQL-estimation issue has been solved under some regularity conditions, especially the GQL, which leads to an asymptotically efficient estimator, where the crucial point is that the optimal rates of convergence for estimating α and β are differen”
- Nonparametric estimation of scalar diffusions based on low frequency data (2002, background): “…but long range observation N∆N →∞ as N →∞, various discretization schemes and estimating procedures had been proposed [Yoshida (1992) and Kessler (1997)] until Gobet (2002) eventually proved the LAN property for ergodic diffusions of the form dXt = bθ1(Xt)dt+ σθ2(Xt)dWt(1.4) in a general setting,…”
- Estimation for stochastic differential equations with a small diffusion coefficient (2009, methodology): “The asymptotic variance of β̂ε,n, Iσ(θ0), is similar to the expression for the Fisher information matrix for estimation of the the diffusion parameter β from hight frequency data with fixed ε found by Gobet (2002) in the same way that Ib(θ0) is similar to Gobet’s expression for the Fisher informatio”