Semimartingale
In probability theory, a real valued X is called a semimartingale if it can be decomposed as the sum of a local martingale and an adapted finite-variation process. Semimartingales are "good integrators", forming the largest class of processes with respect to which the Itō integral and the Stratonovich integral can be defined.
The class of semimartingales is quite large (including, for example, all continuously differentiable processes, Brownian motion and Poisson processes). Submartingales and supermartingales together represent a subset of the semimartingales.
A real valued process X defined on the filtered probability space (Ω,F,(Ft)t ≥ 0,P) is called a semimartingale if it can be decomposed as
where M is a local martingale and A is a càdlàg adapted process of locally bounded variation.
An Rn-valued process X = (X1,…,Xn) is a semimartingale if each of its components Xi is a semimartingale.
First, the simple predictable processes are defined to be linear combinations of processes of the form Ht = A1{t > T} for stopping times T and FT -measurable random variables A. The integral H · X for any such simple predictable process H and real valued process X is
This is extended to all simple predictable processes by the linearity of H · X in H.
A real valued process X is a semimartingale if it is càdlàg, adapted, and for every t ≥ 0,
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