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m2 Space 📂Stochastic Differential Equations

m2 Space

Definition 1 2

Given that there is a probability space (Ω,F,P)( \Omega , \mathcal{F} , P),

  1. A sequence of sub sigma fields {Ft}t0\left\{ \mathcal{F}_{t} \right\}_{t \ge 0} of F\mathcal{F} is called a Filtration if it satisfies the following: s<t,FsFt \forall s < t, \mathcal{F}_{s} \subset \mathcal{F}_{t}
  2. A stochastic process g(t,ω):[0,)×ΩRng(t,\omega) : [0,\infty) \times \Omega \to \mathbb{R}^{n} is said to be Ft\mathcal{F}_{t}-Adapted if for all t0t \ge 0, ωg(t,ω)\omega \mapsto g (t,\omega) is Ft\mathcal{F}_{t}-measurable.
  3. For an interval I:=[a,b]I := [a,b], the set of functions ff that satisfy the following three conditions is denoted as m2=m2[a,b]m^{2} = m^{2} [a,b]. This II, in particular, is called the Natural Domain of the Ito Integral.
    • (i): B\mathcal{B} is B×F\mathcal{B} \times \mathcal{F}-measurable with respect to the Borel sigma field of [0,)[0, \infty).
    • (ii): f(t,ω)f (t,\omega) is Ft\mathcal{F}_{t}-Adapted.
    • (iii): It has the structure of a Hilbert space, i.e., f22([a,b])=E(abf(t,ω)2dt)< \left\| f \right\|_{2}^{2} \left( [a,b] \right) = E \left( \int_{a}^{b} \left| f(t,\omega) \right|^{2} dt \right) < \infty

  • For Ft\mathcal{F}_{t} to be a sub sigma field of F\mathcal{F} means both are sigma fields of Ω\Omega, but FtF\mathcal{F}_{t} \subset \mathcal{F}.
  • A function ff is Ft\mathcal{F}_{t}-measurable means for all Borel set BB([0,))B \in \mathcal{B}([0,\infty)), f1(B)Ftf^{-1} (B) \in \mathcal{F}_{t}.

Explanation

Given a filtration, saying a stochastic process ff is Ft\mathcal{F}_{t}-measurable means it encompasses the history or information up to time tt. Since a filtration is a sequence of increasingly larger sub sigma fields, it aligns with the notion that information grows over time.

Naturally, the naming of m2m^{2} space comes from the L2L^{2} space as seen in condition (iii).


  1. Øksendal. (2003). Stochastic Differential Equations: An Introduction with Applications: p25. ↩︎

  2. Panik. (2017). Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling: p116. ↩︎