Estimators of diffusions with randomly spaced discrete observations: A general theory
By Yacine Aït-Sahalia, Per Aslak Mykland. 2004
75 citations recorded by the source corpus.
We provide a general method to analyze the asymptotic properties of a variety of estimators of continuous time diffusion processes when the data are not only discretely sampled in time but the time separating successive observations may possibly be random. We introduce a new operator, the generalized infinitesimal generator, to obtain Taylor expansions of the asymptotic moments of the estimators. As a special case, our results apply to the situation where the data are discretely sampled at a fixed nonrandom time interval. We include as specific examples estimators based on maximum-likelihood and discrete approximations such as the Euler scheme.
Subfields: Artificial Intelligence, Finance
View Paper: https://www.semanticscholar.org/paper/37be2b2710dc70d98ece0c0c8baa5bcc540cae32
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Citations in the corpus
- Sharp adaptive estimation of the drift function for ergodic diffusions (2005, methodology): “In the problem of estimation and validation of the model with discretely sampled high frequency observations, which is frequently used in mathematical finance, recent progress has been achieved by Fan and Zhang [15] and Aït-Sahalia and Mykland [2].”
- Maximum-likelihood estimation for diffusion processes via closed-form density expansions (2013, background): “and Mykland [7, 8], Aı̈t-Sahalia and Kimmel [5, 6], Li [45], Egorov, Li and Xu [26], Schaumburg [58], Aı̈t-Sahalia and Yu [9], Yu [72], Filipović, Mayerhofer and Schneider [31], Tang and Chen [61], Xiu [66] and Chang and Chen [22].”