Multivariate CARMA processes

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Multivariate CARMA processes, continuous-time state space models and complete regularity of the innovations of the sampled processes (2012, methodology): “All the results we need from [18] are easily seen to hold true also in this more general setting.”
A multivariate Ornstein-Uhlenbeck type stochastic volatility model (2009, methodology): “From the usual formula for the Fourier transform of an integral with a deterministic integrand with respect to a Lévy process (see Marquardt and Stelzer, 2007, Section 2.2, for a brief review) we have that E ( eitr(( R t 0 Cy,z(t−s)dLs) ∗ Id) ) = e R t 0 ψL(C ∗y,z(t−s)Id)ds = e R t 0 ψL(C…”
Estimation for Non-Negative Lévy-Driven CARMA Processes (2011, background): “The process {Yn} is strictly stationary and ergodic by Propositions 3.26 and 3.34 of Marquardt and Stelzer (2007) and EY2n ∞.”
Recent results in the theory and applications of CARMA processes (2014, background): “…further applications, with the proliferation of high-frequency data in so many fields, and further theoretical developments, particularly with respect to multivariate models (Marquardt and Stelzer 2007), nonlinear models, non-causal modelling, sampling and embedding remains broad and challenging.”
Multivariate supOU processes (2010, methodology): “Ornstein–Uhlenbeck-type processes are a special case of the so-called (multivariate) continuous time autoregressive movingaverage (CARMA) processes (see [10, 11, 33]).”