Malliavin calculus, geometric mixing, and expansion of diffusion functionals

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Statistical Methods for Stochastic Differential Equations (2012, methodology): “THE LIKELIHOOD FUNCTION 45 1.4 The likelihood function The likelihood function for a discretely observed diffusion model, (1.24) is a product of transitions densities. Unfortunately, the transition density of a diffusion process is only rarely explicitly known, but several numerical approaches make ”
Inference for Observations of Integrated Diffusion Processes (2004, unknown): “Rather general criteria for geometric a-mixing of diffusion processes expressed in the language of Malliavin calculus were given by Kusuoka & Yoshida (2000).”
CHAPTER 4 – Estimating Functions for Discretely Sampled Diffusion-Type Models (2010, methodology): “Other conditions for geometrical α-mixing were given by Veretennikov (1987), Doukhan (1994), Hansen & Scheinkman (1995), and Kusuoka & Yoshida (2000), see also A¨ıt-Sahalia, Hansen & Scheinkman (2003).”
Polynomial type large deviation inequalities and quasi-likelihood analysis for stochastic differential equations (2011, methodology): “For the mixing properties of diffusion processes, we refer the reader to Meyn and Tweedie (1992, 1993a,b), Veretennikov (1987, 1997) and Kusuoka and Yoshida (2000).”