2 E-variables
Notation
Let \(\mathcal{X}, \mathcal{Y}\) be measurable spaces. We denote by \(\mathcal{P}(\mathcal{X})\) the set of probability measures on \(\mathcal{X}\) and write \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) to mean that \(\kappa \) is a Markov kernel from \(\mathcal{X}\) to \(\mathcal{Y}\). \(\mathbb {R}_{+,\infty }\) is the set of nonnegative reals extended with \(+\infty \) and \(\overline{\mathbb {R}}\) is the set of reals extended with \(-\infty \) and \(+\infty \).
A measure will always denote a nonnegative measure. We will specify when we mean a signed measure.
The central object of this study are E-variables, which are measurable functions that are bounded in expectation by \(1\) for all probability measures in a set \(\mathcal{Q}\).
An E-variable for a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) is a measurable function \(f : \mathcal{X} \to \mathbb {R}_{+,\infty }\) such that for all \(Q \in \mathcal{Q}\), \(Q[f] \le 1\).
We denote by \(\mathcal{E}_{\mathcal{Q}}\) the set of E-variables for a set of probability measures \(\mathcal{Q}\).
If \(\mathcal{P} \subseteq \mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) then \(\mathcal{E}_{\mathcal{Q}} \subseteq \mathcal{E}_{\mathcal{P}}\).
We introduce randomized E-variables, which are Markov kernels that can be seen as randomized versions of E-variables. The main difference is that they are allowed to be randomized, i.e., they can return a distribution over \(\mathbb {R}_{+,\infty }\) instead of a single value. This generalization is mainly useful thanks to the compositional properties it provides, as we will see in Lemma 2.22.
A randomized E-variable for a set of distributions \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) is a Markov kernel \(\eta : \mathcal{X} \rightsquigarrow \mathbb {R}_{+,\infty }\) such that for all \(Q \in \mathcal{Q}\), \((\eta \circ Q)[\operatorname{id}] \le 1\).
We denote by \(\mathcal{E}^R_{\mathcal{Q}}\) the set of randomized E-variables for a set of probability measures \(\mathcal{Q}\). Since measurable functions can be seen as deterministic Markov kernels, we have \(\mathcal{E}_{\mathcal{Q}} \subseteq \mathcal{E}^R_{\mathcal{Q}}\). Indeed, \(Q[f] = (\delta _f \circ Q)[\operatorname{id}]\), in which \(\delta _f : \mathcal{X} \rightsquigarrow \mathbb {R}_{+,\infty }\) is such that \(\delta _f(x)\) is the dirac distribution at \(f(x)\).
A Markov kernel \(\eta : \mathcal{X} \rightsquigarrow \mathbb {R}_{+,\infty }\) is a randomized E-variable for a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) if and only if the measurable function \(f : \mathcal{X} \to \mathbb {R}_{+,\infty }\) defined by \(f(x) = \eta (x)[\operatorname{id}]\) is an E-variable for \(\mathcal{Q}\).
2.1 Almost everywhere with respect to a set of measures
Let \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\) be a set of measures. A set \(S \subseteq \mathcal{X}\) is said to be \(\mathcal{Q}\)-null if \(Q[S] = 0\) for all \(Q \in \mathcal{Q}\). A property \(p : \mathcal{X} \to \mathrm{Bool}\) is said to hold \(\mathcal{Q}\)-almost everywhere (denoted by \(\mathcal{Q}\)-a.e.) if the set \(\{ x \mid \neg p(x)\} \) is \(\mathcal{Q}\)-null.
For a measure \(P \in \mathcal{M}(\mathcal{X})\), we can define the almost everywhere filter \(\operatorname{ae}_P\) of sets with \(P\)-null complement.
A set \(S \subseteq \mathcal{X}\) has a \(\mathcal{Q}\)-null complement iff \(S \in \bigsqcup _{Q \in \mathcal{Q}} \operatorname{ae}_Q\).
By definition of the \(\operatorname{ae}\) filter and of a supremum of filters.
Let \(P \in \mathcal{M}(\mathcal{X})\) and let \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\) be a set of measures. We say that \(P\) is absolutely continuous with respect to \(\mathcal{Q}\) (denoted by \(P \ll \mathcal{Q}\)) if all \(\mathcal{Q}\)-null sets are \(P\)-null sets.
Let \(P \in \mathcal{M}(\mathcal{X})\) and let \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\). Then \(P \ll \mathcal{Q}\) iff \(\operatorname{ae}_P \le \bigsqcup _{Q \in \mathcal{Q}} \operatorname{ae}_Q\).
For \(P \in \mathcal{M}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{M}(\mathcal{X})\), there is a unique decomposition \(P = P_{\mathrm{ac}} + P_{\perp }\) with \(P_{\mathrm{ac}} \ll \mathcal{Q}\) and \(P_{\perp } \perp \mathcal{Q}\) (meaning that there exists a \(\mathcal{Q}\)-null set \(A\) with \(P_{\perp }(A^c) = 0\)).
The singular set of \(P\) with respect to \(\mathcal{Q}\) is a \(\mathcal{Q}\)-null set \(A\) such that the singular part \(P_{\perp }\) satisfies \(P_{\perp }(A^c) = 0\).
The singular set of \(P\) with respect to \(\mathcal{Q}\) is unique up to \(P\) and \(\mathcal{Q}\)-null sets.
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and let \(f\) be an E-variable for \(\mathcal{Q}\). Then the set \(\{ f = \infty \} \) is \(\mathcal{Q}\)-null.
Let \(Q \in \mathcal{Q}\). Since \(f\) is an E-variable for \(\mathcal{Q}\), we have \(Q[f] \le 1\). Hence \(Q[\{ f = \infty \} ] = 0\).
2.2 Utility and maximal utility
We call utility a function \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) which is non-decreasing, concave, finite except perhaps at 0 and \(\infty \), continuous, and continuously differentiable on \((0, \infty )\).
The logarithm satisfies the properties of a utility function and we call it logarithmic utility.
For a utility function \(U\), the maximal utility for \((P, \mathcal{Q})\) is defined as
in which the expectation is the extended integral defined in Definition 1.4.
For a utility function \(U\), the maximal randomized utility for \((P, \mathcal{Q})\) is defined as
in which the expectation is the extended integral defined in Definition 1.4.
Let \(\mathcal{Q}' \subseteq \mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then
The next lemma shows that the larger set of randomized E-variables does not allow to achieve a larger expected utility than the set of E-variables.
Let \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) be a utility function and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) a set of probability measures. Let \(P \in \mathcal{P}(\mathcal{X})\). Then
Since \(\mathcal{E}_{\mathcal{Q}} \subseteq \mathcal{E}^R_{\mathcal{Q}}\), the left hand side is greater than or equal to the right hand side.
For the other direction, let \(\eta \in \mathcal{E}^R_{\mathcal{Q}}\) be a randomized E-variable. By concavity of \(U\),
The function \(x \mapsto \eta (x)[\operatorname{id}]\) is a measurable function and belongs to \(\mathcal{E}_{\mathcal{Q}}\).
Suppose that there exists an event \(A\) such that \(P(A) {\gt} 0\) and \(Q(A) = 0\) for all \(Q \in \mathcal{Q}\). That is, we don’t have \(P \ll \mathcal{Q}\). Then
The function \(f : \mathcal{X} \to \mathbb {R}_{+,\infty }\) defined by \(f(x) = +\infty \) if \(x \in A\) and \(f(x) = 0\) otherwise is an E-variable for \(\mathcal{Q}\) since \(Q[f] = 0\) for all \(Q \in \mathcal{Q}\).
The function \(R \mapsto \mathcal{U}(R, \mathcal{P})\) is convex.
Let \(R_1, R_2 \in \mathcal{P}(\mathcal{X})\) and let \(\theta \in [0,1]\).
2.3 Data-processing inequality for e-variables
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and let \(\phi : \mathcal{X} \to \mathcal{Y}\) be a measurable function. Then for all \(g \in \mathcal{E}_{\phi _* \mathcal{Q}}\), \(g \circ \phi \in \mathcal{E}_{\mathcal{Q}}\).
For \(Q \in \mathcal{Q}\),
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel. Then for all \(\xi \in \mathcal{E}_{\kappa \circ \mathcal{Q}}^R\), \(\xi \circ \kappa \in \mathcal{E}^R_{\mathcal{Q}}\).
For \(Q \in \mathcal{Q}\),
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(P \in \mathcal{P}(\mathcal{X})\). Let \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) be a utility function. Then for all Markov kernels \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\),
By Lemma 2.22, \(\{ \xi \circ \kappa \mid \xi \in \mathcal{E}^R_{\kappa \circ \mathcal{Q}}\} \subseteq \mathcal{E}^R_{\mathcal{Q}}\), hence
Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel, \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then
Let \(f : \mathcal{X} \to \mathcal{Y}\) be a measurable function, \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then
Use Lemma 2.24 with the deterministic Markov kernel given by \(f\).
Let \(\phi : \mathcal{X} \to \mathcal{Y}\) be a measurable embedding, \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then
Let \(R \in \mathcal{P}(\mathcal{X})\), \(\mathcal{P} \subseteq \mathcal{P}(\mathcal{X})\) and let \(\phi : \mathcal{X} \to \mathcal{X}\) be a measurable map such that \(\phi \circ \phi = \operatorname{id}\). Then
To prove the first inequality, we use Lemma 2.25:
For the reverse inequality,
2.4 Numeraire and duality
This section gathers results from [ LRR24 ] .
Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). A numeraire for \((P, \mathcal{Q})\) is a \(P\)-almost surely positive e-variable \(f^* \in \mathcal{E}_{\mathcal{Q}}\) such that \(P[f / f^*] \le 1\) for all \(f \in \mathcal{E}_{\mathcal{Q}}\) .
Let \(f^*\) be a numeraire for \((P, \mathcal{Q})\) and let \(f \in \mathcal{E}_{\mathcal{Q}}\). Then \(P\left[\log \frac{f}{f^*}\right] \le 0\).
Let \(f\) be a \(P\)-almost surely positive E-variable for a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). Then \(f\) is a numeraire for \((P, \mathcal{Q})\) if and only if \(P[\log \frac{g}{f}] \le 0\) for all \(g \in \mathcal{E}_{\mathcal{Q}}\).
Let \(f^*\) be a numeraire for \((P, \mathcal{Q})\). Then for all \(f \in \mathcal{E}_{\mathcal{Q}}\),
Let \(f^*\) be a numeraire for \((P, \mathcal{Q})\). Then \(P[\log f^*] \ge 0\).
Use Lemma 2.31 with the e-variable \(f = 1\).
Let \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2\). If \(f^*\) is a numeraire for \((P, \mathcal{Q}_1)\) and \(f^* \in \mathcal{E}_{\mathcal{Q}_2}\) then \(f^*\) is also a numeraire for \((P, \mathcal{Q}_2)\).
For \(f \in \mathcal{E}_{\mathcal{Q}_2}\), we have \(f \in \mathcal{E}_{\mathcal{Q}_1}\) since \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2\). Then \(P\left[f/f^*\right] \le 1\) by the numeraire property for \((P, \mathcal{Q}_1)\). Thus \(f^*\) is a numeraire for \((P, \mathcal{Q}_2)\).
Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). If \(f^*\) is a numeraire for \((P, \mathcal{Q})\) then
2.4.1 Existence of a numeraire
Let \((X_n)_{n \in \mathbb {N}}\) be a sequence of measurable functions from \(\mathcal{X}\) to \(\mathbb {R}_{+,\infty }\) and let \(Q \in \mathcal{P}(\mathcal{X})\). There exists a sequence of measurable functions \(\tilde{X}_n \in \mathrm{conv}(X_n, X_{n+1}, \ldots )\) and \(X : \mathcal{X} \to \mathbb {R}_{+, \infty }\) such that \(\tilde{X}_n \to X\) \(Q\)-almost surely.
Proved in the Brownian motion project.
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) a concave function which is bounded from above. Then there exists \(f^* \in \mathcal{E}_{\mathcal{Q}}\) such that
The properties of \(\mathcal{E}_{\mathcal{Q}}\) we use for the proof of this lemma are convexity and the fact that if \(f_n \in \mathcal{E}_{\mathcal{Q}}\) then \(\liminf _{n \to \infty } f_n \in \mathcal{E}_{\mathcal{Q}}\).
Let \((f_n)_{n \in \mathbb {N}}\) be a sequence of e-variables in \(\mathcal{E}_{\mathcal{Q}}\) such that \(n \mapsto P[U(f_n)]\) is non-decreasing and \(P[U(f_n)] \to \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)]\). Let \(\tilde{f}_n\) be a sequence of measurable functions and let \(\tilde{f} : \mathcal{X} \to \mathbb {R}_{+, \infty }\), with \(\tilde{f}_n \in \mathrm{conv}(f_n, f_{n+1}, \ldots )\) such that \(\tilde{f}_n \to \tilde{f}\) \(P\)-almost surely. That sequence exists by Lemma 2.35. By convexity of \(\mathcal{E}_{\mathcal{Q}}\), \(\tilde{f}_n \in \mathcal{E}_{\mathcal{Q}}\) for all \(n\).
Let \(f^* = \liminf _{n \to \infty } \tilde{f}_n\). Note that \(f^*\) is a measurable function such that \(f^* = \tilde{f}\) \(P\)-almost surely. We have \(f^* \in \mathcal{E}_{\mathcal{Q}}\) by an application of Fatou’s lemma: for \(Q \in \mathcal{Q}\),
By concavity of \(U\) and the fact that \(P[U(f_n)]\) is non-decreasing, we have
We also have \(f^* = \limsup _{n \to \infty } \tilde{f}_n\) \(P\)-almost surely, hence by Fatou’s lemma (using that \(U\) is bounded from above),
Since \(f^* \in \mathcal{E}_{\mathcal{Q}}\), we also have \(P[U(f^*)] \le \sup _{f \in \mathcal{E}_{\mathcal{Q}}} P[U(f)]\), which proves the equality.
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) a concave function which is bounded from above. Then there exists \(f^* \in \mathcal{E}_{\mathcal{Q}}\) such that
and such that all e-variables are \(P\)-a.s. finite on \(\{ f^* {\lt} \infty \} \).
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures and \(U : \mathbb {R}_{+,\infty } \to \overline{\mathbb {R}}\) a non-decreasing concave \(C^1\) function which is bounded from above. Then the e-variable \(f^*\) in Lemma 2.36 is such that for all e-variables \(g \in \mathcal{E}_{\mathcal{Q}}\),
For \(f \in \mathcal{E}_{\mathcal{Q}}\), let \(f_t = t f + (1-t) f^*\) for \(t \in [0,1]\). By concavity of \(U\), \(U(f_t) \ge t U(f) + (1-t) U(f^*)\), hence
TODO
Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). A numeraire for \((P, \mathcal{Q})\) exists and is \(P\)-a.e. unique.
We will write \(E^*_{P, \mathcal{Q}}\) for the numeraire for \((P, \mathcal{Q})\).
2.4.2 Products
Let \(P_1 \in \mathcal{P}(\mathcal{X}), P_2 \in \mathcal{P}(\mathcal{Y})\) and \(\mathcal{Q}_1 \subseteq \mathcal{P}(\mathcal{X}), \mathcal{Q}_2 \subseteq \mathcal{P}(\mathcal{Y})\). Then the numeraire for the product \((P_1 \times P_2, \mathcal{Q}_1 \times \mathcal{Q}_2)\) is given by the product of the numeraires for \((P_1, \mathcal{Q}_1)\) and \((P_2, \mathcal{Q}_2)\):
Its logarithmic utility is given by
We first need to check that \(E^*_{P_1, \mathcal{Q}_1} E^*_{P_2, \mathcal{Q}_2}\) is in \(\mathcal{E}_{\mathcal{Q}_1 \times \mathcal{Q}_2}\). For \(Q_1 \in \mathcal{Q}_1\) and \(Q_2 \in \mathcal{Q}_2\), we have
Let’s now check the numeraire property. Let \(f \in \mathcal{E}_{\mathcal{Q}_1 \times \mathcal{Q}_2}\). Let \(E^*_1 = E^*_{P_1, \mathcal{Q}_1}\) and \(E^*_2 = E^*_{P_2, \mathcal{Q}_2}\).
If we can show that \(x \mapsto P_2\left[\frac{f(x, \cdot )}{E^*_2}\right]\) is in \(\mathcal{E}_{\mathcal{Q}_1}\), then we can apply the definition of the numeraire for \((P_1, \mathcal{Q}_1)\) to obtain
Let then \(Q_1 \in \mathcal{Q}_1\). We want to show that \(Q_1 \left[P_2\left[\frac{f(x, \cdot )}{E^*_2}\right]\right] \le 1\). By Fubini’s theorem, we have
It now suffices to show that \(y \mapsto Q_1 \left[f(\cdot , y)\right]\) is in \(\mathcal{E}_{\mathcal{Q}_2}\). Let \(Q_2 \in \mathcal{Q}_2\). Then
The last inequality holds since \(f \in \mathcal{E}_{\mathcal{Q}_1 \times \mathcal{Q}_2}\).
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) be a set of Markov kernels. If \(f \in \mathcal{E}_{\mathcal{Q}\otimes H}\) then for all \(\eta \in H\), the function \(x \mapsto \eta (x)[f(x, \cdot )]\) is in \(\mathcal{E}_{\mathcal{Q}}\).
For \(Q \in \mathcal{Q}\) and \(f \in \mathcal{E}_{\mathcal{Q}\otimes H}\), \(Q[x \mapsto \eta (x)[f]] = (Q \otimes \eta )[f] \le 1\), hence \((x \mapsto \eta (x)[f(x, \cdot )]) \in \mathcal{E}_{\mathcal{Q}}\).
\(E^*_{P \otimes \kappa , \mathcal{Q} \otimes \kappa } =_{(P\otimes \kappa ) a.e.} (x, y) \mapsto E^*_{P, \mathcal{Q}}(x)\) for any Markov kernel \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\).
First we need to show that \(E^*_{P, \mathcal{Q}}\) is in \(\mathcal{E}_{\mathcal{Q} \otimes \kappa }\). It is measurable and for \(Q \in \mathcal{Q}\),
We then check the numeraire condition. By Lemma 2.41, \((x \mapsto \kappa (x)[f]) \in \mathcal{E}_{\mathcal{Q}}\). By the definition of the numeraire, we then have
That is, \((P \otimes \kappa )\left[\frac{f}{E^*_{P, \mathcal{Q}}}\right] \le 1\) for all \(f \in \mathcal{E}_{\mathcal{Q}\otimes \kappa }\). We have proved that \(E^*_{P, \mathcal{Q}}\) is the numeraire for \((P \otimes \kappa , \mathcal{Q} \otimes \kappa )\).
For any \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\), we have
Note that this proves the numeraire property for the candidate \(E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}\), but we don’t know if it is an e-variable, so can’t conclude that it’s the numeraire. As a consequence,
If \(E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , \{ P\} \otimes H}\) was an e-variable, then we would have equality.
Let \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\).
It suffices to show that \(f/E^*_{P, \mathcal{Q}}\) is in \(\mathcal{E}_{P \otimes H}\) to obtain that this is less than 1 by the numeraire property. Let \(\eta \in H\). By Lemma 2.42,
and since \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\) it is in \(\mathcal{E}_{\mathcal{Q} \otimes \eta }\) and the numeraire property gives that this ratio is less than or equal to 1. We conclude that \(E^*_{P, \mathcal{Q}}E^*_{P \otimes \kappa , P \otimes H}\) satisfies the numeraire property.
We then have
We conclude that
Taking the supremum over \(f \in \mathcal{E}_{\mathcal{Q} \otimes H}\), we obtain the desired inequality.
A jointly measurable function \(f : \mathcal{X} \times \mathcal{Y} \to \mathbb {R}_{+, \infty }\) is a conditional e-variable for a set of Markov kernels \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) if \(f(x, \cdot )\) is in \(\mathcal{E}_{H(x)}\) for all \(x \in \mathcal{X}\) (in which \(H(x) = \{ \eta (x) \mid \eta \in H\} \)). We denote the set of conditional e-variables for \(H\) by \(\mathcal{E}_H\).
Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) a set of Markov kernels. A conditional e-variable \(f\) for \(H\) is a conditional numeraire for \((\kappa , H)\) if for all \(x \in \mathcal{X}\), the function \(f(x, \cdot )\) is a numeraire for \((\kappa (x), H(x))\). We denote the conditional numeraire for \((\kappa , H)\) by \(E^*_{\kappa , H}\) (if it exists).
As we will see, conditional numeraires are not always guaranteed to exist.
Question: is there always a \(P\)-a.e. conditional numeraire? That is, a jointly measurable function \(f\) such that \(f(x, \cdot )\) is a numeraire for \((\kappa (x), H(x))\) for only \(P\)-almost all \(x \in \mathcal{X}\) instead of all \(x \in \mathcal{X}\)?
If there exists a conditional numeraire for \((\kappa , \{ \eta \} )\), then \(1/E^*_{\kappa , \{ \eta \} }\) is a Radon-Nikodym derivative for \(\eta \) with respect to \(\kappa \).
\(1/E^*_{\kappa , \{ \eta \} }\) is jointly measurable and satisfies for all \(x \in \mathcal{X}\), \(1/E^*_{\kappa , \{ \eta \} }(x, \cdot ) =_{a.e.} \frac{d\eta (x)}{d \kappa (x)}\) since that’s the numeraire for a singleton. That’s the definition of a Radon-Nikodym derivative of kernels.
The last lemma implies that \(E^*_{\kappa , H}\) will not always exist. Indeed, Radon-Nikodym derivatives of kernels do not always exist, although weak hypotheses on \(\mathcal{X}\) or \(\mathcal{Y}\) can ensure that they do. \(\mathcal{X}\) being countable or \(\mathcal{Y}\) having countably generated sigma-algebra (for example \(\mathcal{Y}\) standard Borel) is enough.
If a conditional numeraire exists for \((\kappa , H)\), then \(E^*_{P \otimes \kappa , \mathcal{Q} \otimes H} =_{a.e.} E^*_{P, \mathcal{Q}}E^*_{\kappa , H}\).
\(E^*_{\kappa , H}\) is a numeraire for \((P \otimes \kappa , \{ P\} \otimes H)\), so we get the numeraire property as in Theorem 2.43. We now prove that \(E^*_{P, \mathcal{Q}}E^*_{\kappa , H} \in \mathcal{E}_{\mathcal{Q} \otimes H}\). Let \(Q \in \mathcal{Q}\) and \(\eta \in H\).
For all \(x\), \(\eta (x)\left[E^*_{\kappa (x), H(x)}\right] \le 1\), since \(E^*_{\kappa (x), H(x)}\) is an e-variable for \(H(x)\).
Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) a set of Markov kernels. If \(\mathcal{X}\) is countable with measurable singletons then there exists a conditional numeraire for \((\kappa , H)\).
For each \(x \in \mathcal{X}\), we can apply Theorem 2.39 to the Markov kernel \(\kappa (x)\) and the set of kernels \(H(x)\) to obtain a numeraire \(E^*_{\kappa (x), H(x)}\). If \(\mathcal{X}\) is countable, we can simply take the function \(E^*_{\kappa , H}(x,y) = E^*_{\kappa (x), H(x)}(y)\), and the countability ensures the joint measurability.
Let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel and \(H \subseteq (\mathcal{X} \rightsquigarrow \mathcal{Y})\) a set of Markov kernels. If either \(\mathcal{X}\) is countable or \(\mathcal{Y}\) is countably generated, then there exists a conditional numeraire for \((\kappa , H)\).
The case of countable \(\mathcal{X}\) is covered by Lemma 2.48. (TODO: only for measurable singletons though)
TODO: \(\kappa \ll H\) hyp?
Let’s now consider the case where \(\mathcal{Y}\) is countably generated. Then there exists a sequence of finer and finer partitions of \(\mathcal{Y}\) into finitely many measurable sets \((A_{n,m})_{n \in \mathbb {N}, m \le 2^n}\) with \(\mathcal{Y} = \bigcup _{m \le 2^n} A_{n,m}\) for all \(n\), and such that the sigma-algebra on \(\mathcal{Y}\) is the \(\sigma \)-algebra generated by the sets \(A_{n,m}\). Let \(\mathcal{F}\) be the filtration generated by the successive partitions, that is, \(\mathcal{F}_n = \sigma (\{ A_{n,m} \mid m \le 2^n\} )\).
TODO
2.4.3 Reverse information projection and duality.
Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). The reverse information projection (RIPr) of \(P\) onto \(\mathcal{Q}\) is the finite measure \(P^*_{\mathcal{Q}}\) absolutely continuous with respect to \(P\) such that
It satisfies \(P^*_{\mathcal{Q}}[\mathcal{X}] \le 1\) and the inequality can be strict.
For a set of probability measures \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\), the effective set of measures \(\mathcal{Q}_{\operatorname{eff}}\) is the set \(\{ \mu \in \mathcal{M}_+(\mathcal{X}) \mid \forall f \in \mathcal{E}_{\mathcal{Q}}, \: \mu [f] \le 1\} \).
We could also define the effective set of measures for randomized E-variables, but it actually gives the same set.
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) be a set of probability measures. The effective set \(\mathcal{Q}_{\operatorname{eff}}\) is convex and satisfies \(0 \in \mathcal{Q}_{\operatorname{eff}}\) and \(\mathcal{Q} \subseteq \mathcal{Q}_{\operatorname{eff}}\).
Let \(Q \in \mathcal{P}(\mathcal{X})\) be a probability measure. Then \(\{ Q\} _{\operatorname{eff}} = \{ Q' \in \mathcal{M}(\mathcal{X}) \mid Q' \le Q\} \), in which \(Q' \le Q\) means that \(Q'(A) \le Q(A)\) for all measurable sets \(A \subseteq \mathcal{X}\).
This is the set of finite measures \(\{ Q' \mid Q' \le Q\} \). Indeed, if \(Q' \le Q\), then for all \(f\) with \(Q[f] \le 1\), \(Q'[f] \le Q[f] \le 1\). Conversely, if \(Q'\) is such that \((\forall f, \ Q[f] \le 1 \implies Q'[f] \le 1)\), then for any measurable set \(A\), we can take the measurable function \(f = Q(A)^{-1} \cdot \mathbb {I}_A\) (in which \(0^{-1} = \infty \)) to obtain \(Q'(A) \le Q(A)\).
Let \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\) and let \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\) be a Markov kernel. Then \(\kappa \circ \mathcal{Q}_{\operatorname{eff}} \subseteq (\kappa \circ \mathcal{Q})_{\operatorname{eff}}\).
Let \(\kappa \circ Q \in \kappa \circ \mathcal{Q}_{\operatorname{eff}}\) and \(f\) such that \(\forall Q' \in \mathcal{Q}, (\kappa \circ Q')[f] \le 1\). That is, \(\forall Q' \in \mathcal{Q}, Q'[x \mapsto \kappa (x)[f]] \le 1\). Then since \(Q \in \mathcal{Q}_{\operatorname{eff}}\), we have \(Q[x \mapsto \kappa (x)[f]] \le 1\). We have proved that \(\kappa \circ Q \in (\kappa \circ \mathcal{Q})_{\operatorname{eff}}\).
Let \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2 \subseteq \mathcal{P}(\mathcal{X})\) be two sets of probability measures. Then \(\mathcal{Q}_{1, \operatorname{eff}} \subseteq \mathcal{Q}_{2, \operatorname{eff}}\).
Since \(\mathcal{Q}_1 \subseteq \mathcal{Q}_2\) we have \(\mathcal{E}_{\mathcal{Q}_2} \subseteq \mathcal{E}_{\mathcal{Q}_1}\) (Lemma 2.2). Then
Let \(\operatorname{KL}(P, Q)\) be the Kullback-Leibler divergence from \(P\) to \(Q\), defined for finite measures as
Note that we don’t correct the divergence if \(P\) and \(Q\) are not probability measures.
Let \(P \in \mathcal{P}(\mathcal{X})\) and \(\mathcal{Q} \subseteq \mathcal{P}(\mathcal{X})\). If \(P \ll \mathcal{Q}\) then
Let \(P, Q \in \mathcal{P}(\mathcal{X})\) be two probability measures with \(P \ll Q\). Then
Since \(\operatorname{KL}\) is non-increasing in the second argument,
Since \(\{ Q\} _{\operatorname{eff}} = \{ Q' \in \mathcal{M}(\mathcal{X}) \mid Q' \le Q\} \) (Lemma 2.53), we obtained \(\operatorname{KL}(P, Q) = \inf _{Q' \in \{ Q\} _{\operatorname{eff}}} \operatorname{KL}(P, Q')\). By the duality of numeraire and \(\operatorname{KL}\) divergence (Theorem 2.56), we have
KL data processing from e-variables
The data processing inequality for the numeraire utility implies the data processing inequality for KL divergences.
Let \(P, Q \in \mathcal{P}(\mathcal{X})\). Then for all Markov kernels \(\kappa : \mathcal{X} \rightsquigarrow \mathcal{Y}\),
By Lemma 2.57, \(\operatorname{KL}(P, Q) = \sup _{f \in \mathcal{E}_{\{ Q\} }} P[\log f]\). Then by Lemma 2.24, we have
And that last term is \(\operatorname{KL}(\kappa \circ P, \kappa \circ Q)\), again by Lemma 2.57.