1Stationary Dilations
Definition
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For all \(A \in \mathcal{F}\), \(\phi^{- 1} (A) \in \tilde{\mathcal{F}}\)
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For all \(A \in \mathcal{F}\), \(P (A) = \tilde{P} (\phi^{- 1} (A))\)
In other words, \((\Omega, \mathcal{F}, P)\) can be obtained from \((\tilde{\Omega}, \tilde{\mathcal{F}}, \tilde{P})\) by projecting the larger space onto the smaller one while preserving the probability measure structure.
Remark
Definition
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\((\Omega, \mathcal{F}, P)\) is a factor of \((\tilde{\Omega}, \tilde{\mathcal{F}}, \tilde{P})\)
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There exists a measurable projection operator \(\Pi\) such that:
\(\displaystyle X_t = \Pi Y_t \quad \forall t \in \mathbb{R}_+\)
Theorem
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is uniformly continuous in probability over compact intervals:
\(\displaystyle \lim_{s \to t} P (|X_s - X_t | > \epsilon) = 0 \quad \forall \epsilon > 0, t \in [0, T], T > 0\) -
has finite second moments:
\(\displaystyle \mathbb{E} [|X_t |^2] < \infty \quad \forall t \in \mathbb{R}_+\) -
has an integral representation of the form:
\(\displaystyle X_t = \int_0^t \eta (s) ds\) where \(\eta (t)\) is a measurable random function that is stationary in the wide sense (with \(\int_0^t \mathbb{E} [| \eta (s) |^2] \hspace{0.17em} ds < \infty\) for all \(t\))
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and has a covariance operator
\(\displaystyle R (t, s) =\mathbb{E} [X_t X_s]\) which is symmetric \((R (t, s) = R (s, t))\), positive definite and continuous
Under these conditions, there exists a representation:
\(\displaystyle X_t = M (t) \cdot S_t\) |
where:
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\(M (t)\) is a continuous deterministic modulation function
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\(\{S_t \}_{t \in \mathbb{R}_+}\) is a stationary process
This representation can be obtained through the stationary dilation by choosing:
\(\displaystyle Y_t = \left( \begin{array}{c} M (t)\\ S_t \end{array} \right)\) |
with the projection operator \(\Pi\) defined as:
\(\displaystyle \Pi Y_t = M (t) \cdot S_t\) |
Proposition
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Preservation of moments:
\(\displaystyle \mathbb{E} [|X_t |^p] \leq \mathbb{E} [|Y_t |^p] \quad \forall p \geq 1\) -
Minimal extension: Among all stationary processes that dilate \(X_t\), there exists a minimal one (unique up to isomorphism) in terms of the probability space dimension
Corollary