Progressively measurable process

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Short description: Property in the mathematical theory of stochastic processes

In mathematics, progressive measurability is a property in the theory of stochastic processes. A progressively measurable process, while defined quite technically, is important because it implies the stopped process is measurable. Being progressively measurable is a strictly stronger property than the notion of being an adapted process.[1] Progressively measurable processes are important in the theory of Itô integrals.

Definition

Let

  • (Ω,ℱ,ℙ) be a probability space;
  • (𝕏,𝒜) be a measurable space, the state space;
  • {ℱt∣t≥0} be a filtration of the sigma algebra ℱ;
  • X:[0,∞)×Ω→𝕏 be a stochastic process (the index set could be [0,T] or ℕ0 instead of [0,∞));
  • Borel([0,t]) be the Borel sigma algebra on [0,t].

The process X is said to be progressively measurable[2] (or simply progressive) if, for every time t, the map [0,t]×Ω→𝕏 defined by (s,ω)↦Xs(ω) is Borel([0,t])⊗ℱt-measurable. This implies that X is ℱt-adapted.[1]

A subset P⊆[0,∞)×Ω is said to be progressively measurable if the process Xs(ω):=χP(s,ω) is progressively measurable in the sense defined above, where χP is the indicator function of P. The set of all such subsets P form a sigma algebra on [0,∞)×Ω, denoted by Prog, and a process X is progressively measurable in the sense of the previous paragraph if, and only if, it is Prog-measurable.

Properties

  • It can be shown[1] that L2(B), the space of stochastic processes X:[0,T]×Ω→ℝn for which the Itô integral
∫0TXtdBt
with respect to Brownian motion B is defined, is the set of equivalence classes of Prog-measurable processes in L2([0,T]×Ω;ℝn).
  • Every adapted process with left- or right-continuous paths is progressively measurable. Consequently, every adapted process with càdlàg paths is progressively measurable.[1]
  • Every measurable and adapted process has a progressively measurable modification.[1]

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 Karatzas, Ioannis; Shreve, Steven (1991). Brownian Motion and Stochastic Calculus (2nd ed.). Springer. pp. 4–5. ISBN 0-387-97655-8. 
  2. ↑ Pascucci, Andrea (2011). "Continuous-time stochastic processes". PDE and Martingale Methods in Option Pricing. Bocconi & Springer Series. Springer. p. 110. doi:10.1007/978-88-470-1781-8. ISBN 978-88-470-1780-1.