Filling the gap between Continuous and Discrete Time Dynamics of Autoregressive Processes

Valerie Girardin and Rachid Senoussi

Journal of Time Series Analysis, 2020, vol. 41, issue 4, 590-602

Abstract: Discretization of continuous time autoregressive (AR) processes driven by a Brownian motion and embedding of discrete time AR sequences driven by a Gaussian white noise are classical issues. The article aims at establishing and using such discretization and embedding formulae between extended AR continuous time processes and discrete time sequences. The continuous‐time processes are driven by either Brownian or jump processes, and may have random coefficients depending on time; Lévy‐driven processes are also considered. The innovation of the discrete time processes may be of many types – including Gaussian. In one way, observing the continuous time AR process at discrete times leads the AR dynamics of the discretized process to be characterized. The other way round, AR sequences can be embedded, in the almost sure sense, into continuous time AR processes with the same dynamics. Illustration is provided through many examples and simulation.

Date: 2020
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