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Exer Let $\mathbb{P}$ be the uniform (Lebesgue) measure on $\Omega=[0,1]$. Define $$ Z(\omega)=\left\{\begin{array}{l} 0 \text { if } 0 \leq \omega<\frac{1}{2}, \\ 2 \text { if } \frac{1}{2} \leq \omega \leq 1 . \end{array}\right. $$ For $A \in \mathcal{B}[0,1]$, define $$ \widetilde{\mathbb{P}}(A)=\int_A Z(\omega) d \mathbb{P}(\omega) . $$ (i) Show that $\widetilde{\mathbb{P}}$ is a probability measure. (ii) Show that if $\mathbb{P}(A)=0$, then $\widetilde{\mathbb{P}}(A)=0$. We say that $\widetilde{\mathbb{P}}$ is absolutely continuous with respect to $\mathbb{P}$. (iii) Show that there is a set $A$ for which $\widetilde{\mathbb{P}}(A)=0$ but $\mathbb{P}(A)>0$. In other words, $\widetilde{\mathbb{P}}$ and $\mathbb{P}$ are not equivalent.

   Exer  Let $\mathbb{P}$ be the uniform (Lebesgue) measure on $\Omega=[0,1]$. Define
$$
Z(\omega)=\left\{\begin{array}{l}
0 \text { if } 0 \leq \omega<\frac{1}{2}, \\
2 \text { if } \frac{1}{2} \leq \omega \leq 1 .
\end{array}\right.
$$

For $A \in \mathcal{B}[0,1]$, define
$$
\widetilde{\mathbb{P}}(A)=\int_A Z(\omega) d \mathbb{P}(\omega) .
$$
(i) Show that $\widetilde{\mathbb{P}}$ is a probability measure.
(ii) Show that if $\mathbb{P}(A)=0$, then $\widetilde{\mathbb{P}}(A)=0$. We say that $\widetilde{\mathbb{P}}$ is absolutely continuous with respect to $\mathbb{P}$.
(iii) Show that there is a set $A$ for which $\widetilde{\mathbb{P}}(A)=0$ but $\mathbb{P}(A)>0$. In other words, $\widetilde{\mathbb{P}}$ and $\mathbb{P}$ are not equivalent.

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Stochastic Calculus for Finance II : Continuous-Time Models
Stochastic Calculus for Finance II : Continuous-Time Models
Steven E. Shreve 1st Edition
Chapter 1, Problem 10 ↓

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Step 1

- **Non-negativity**: For any measurable set $A \subseteq [0,1]$, since $Z(\omega) \geq 0$ for all $\omega \in [0,1]$, it follows that $\widetilde{\mathbb{P}}(A) = \int_A Z(\omega) d\mathbb{P}(\omega) \geq 0$. - **Normalization**: We need to show that  Show more…

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Exer Let $\mathbb{P}$ be the uniform (Lebesgue) measure on $\Omega=[0,1]$. Define $$ Z(\omega)=\left\{\begin{array}{l} 0 \text { if } 0 \leq \omega<\frac{1}{2}, \\ 2 \text { if } \frac{1}{2} \leq \omega \leq 1 . \end{array}\right. $$ For $A \in \mathcal{B}[0,1]$, define $$ \widetilde{\mathbb{P}}(A)=\int_A Z(\omega) d \mathbb{P}(\omega) . $$ (i) Show that $\widetilde{\mathbb{P}}$ is a probability measure. (ii) Show that if $\mathbb{P}(A)=0$, then $\widetilde{\mathbb{P}}(A)=0$. We say that $\widetilde{\mathbb{P}}$ is absolutely continuous with respect to $\mathbb{P}$. (iii) Show that there is a set $A$ for which $\widetilde{\mathbb{P}}(A)=0$ but $\mathbb{P}(A)>0$. In other words, $\widetilde{\mathbb{P}}$ and $\mathbb{P}$ are not equivalent.
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Key Concepts

-
Radon-Nikodym Derivative
The Radon-Nikodym derivative is a function that acts as the density of one measure with respect to another when the former is absolutely continuous with respect to the latter. This derivative allows one to express the second measure as the integral of the density function against the first measure. In the example provided, the function Z plays the role of a Radon-Nikodym derivative, establishing a concrete link between the two measures.
Equivalence of Measures
Measures are equivalent if they have the same null sets; that is, a set has measure zero under one measure if and only if it has measure zero under the other. The problem highlights that even when one measure is absolutely continuous with respect to another, they may not be equivalent if there exists some set with positive measure under one while being zero under the other.
Absolute Continuity
Absolute continuity between two measures means that if one measure assigns a set the value zero, then the other must also assign it zero. This concept is important for understanding how one measure can be 'dominated' by another. In the context of the problem, showing that if the Lebesgue measure is zero then the measure defined by the integral of the density function is also zero demonstrates absolute continuity.
Probability Measure
A probability measure is a function defined on a sigma-algebra that assigns every measurable set a number between 0 and 1, where the measure of the entire space is exactly 1 and the measure is countably additive over disjoint sets. Establishing a function as a probability measure involves verifying these properties, ensuring that the measure behaves as expected under the axioms of probability theory.

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Consider a measurable space with the sample space ̒ = [0, 1] and the ̑-algebra ℱ ≡ ℤ([0, 1]), where ℤ([0, 1]) is the Borel ̑-algebra of subsets of [0, 1]. Let ℙ be the (uniform) Lebesgue measure on ̒ = [0, 1] discussed in class ℙ[a, b] = b - a, 0 ≤ a ≤ b ≤ 1. Define the following random variable Z(̑) = { 0 0 ≤ ̑ < 1/2 ; 2 1/2 ≤ ̑ ≤ 1. For each A ∈ ℤ([0, 1]), we define ℙ̃(A) := ∫_A Z(̑)dℙ(̑) = ∫_̒ I_A(̑)Z(̑)dℙ(̑), where I_A(̑) = { 1 ̑ ∈ A ; 0 otherwise. a. Show that ℙ̃ is a probability measure. In your solution, clearly specify the measurable space on which ℙ̃ is defined. b. Show that if ℙ(A) = 0, then ℙ̃(A) = 0. That is, ℙ̃ is absolutely continuous with respect to ℙ. c. Show that there is a set A ∈ ℤ([0, 1]) for which ℙ̃(A) = 0 but ℙ(A) > 0. In order words, ℙ and ℙ̃ are not equivalent.

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