Documentation

BrownianMotion.StochasticIntegral.SquareIntegrable

Square integrable martingales #

structure ProbabilityTheory.IsSquareIntegrable {ι : Type u_1} {Ω : Type u_2} {E : Type u_3} [LinearOrder ι] [TopologicalSpace ι] [NormedAddCommGroup E] [NormedSpace E] [CompleteSpace E] { : MeasurableSpace Ω} (X : ιΩE) (𝓕 : MeasureTheory.Filtration ι ) (P : MeasureTheory.Measure Ω) :

A square integrable martingale is a martingale with cadlag paths and uniformly bounded second moments.

Instances For
    theorem ProbabilityTheory.IsSquareIntegrable.integrable_sq {ι : Type u_1} {Ω : Type u_2} {E : Type u_3} [LinearOrder ι] [TopologicalSpace ι] [NormedAddCommGroup E] [NormedSpace E] [CompleteSpace E] { : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : ιΩE} {𝓕 : MeasureTheory.Filtration ι } (hX : IsSquareIntegrable X 𝓕 P) (i : ι) :
    MeasureTheory.Integrable (fun (ω : Ω) => X i ω ^ 2) P
    theorem ProbabilityTheory.IsSquareIntegrable.add {ι : Type u_1} {Ω : Type u_2} {E : Type u_3} [LinearOrder ι] [TopologicalSpace ι] [NormedAddCommGroup E] [NormedSpace E] [CompleteSpace E] { : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X Y : ιΩE} {𝓕 : MeasureTheory.Filtration ι } (hX : IsSquareIntegrable X 𝓕 P) (hY : IsSquareIntegrable Y 𝓕 P) :
    IsSquareIntegrable (fun (i : ι) (ω : Ω) => X i ω + Y i ω) 𝓕 P
    theorem ProbabilityTheory.IsSquareIntegrable.submartingale_sq_norm {ι : Type u_1} {Ω : Type u_2} {E : Type u_3} [LinearOrder ι] [TopologicalSpace ι] [NormedAddCommGroup E] [NormedSpace E] [CompleteSpace E] { : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : ιΩE} {𝓕 : MeasureTheory.Filtration ι } [MeasureTheory.SigmaFinite P] (hX : IsSquareIntegrable X 𝓕 P) :
    MeasureTheory.Submartingale (fun (i : ι) (ω : Ω) => X i ω ^ 2) 𝓕 P