Utility duality under additional information: conditional measures versus filtration enlargements

The utility maximisation problem is considered for investors with anticipative additional information. We distinguish between models with conditional measures and models with enlarged filtrations. The dual functions of the maximal expected utility are determined with the help of f-divergences. We assume that our measures are absolutely continuous with respect to a local martingale measure (LMM), but not necessarily equivalent. Thus we do not exclude arbitrage.utility maximisation, additional information, enlargement of filtrations, conditional measures, convex conjugate function, dual function, f-divergence

Information and semimartingales

Die stochastische Analysis gibt Methoden zur Erfassung und Beschreibung von zufälligen numerischen Prozessen an die Hand. Die Beschreibungen hängen dabei sehr stark von der Informationsstruktur ab, die den Prozessen in Gestalt von Filtrationen zugrunde gelegt wird. Der 1. Teil der vorliegenden Arbeit handelt davon, wie sich ein Wechsel der Informationsstruktur auf das Erscheinungsbild eines stochastischen Prozesses auswirkt. Konkret geht es darum, wie sich eine Filtrationsvergrößerung auf die Semimartingalzerlegung eines Prozesses auswirkt. In dem 2. und 3. Teil der Arbeit wird die Rolle von Information im finanzmathematischen Nutzenkalkül untersucht. Im 2. Teil werden unter der Annahme, dass der maximale erwartete Nutzen eines Händlers beschränkt ist, qualitative Erkenntnisse über den Preisprozess hergeleitet. Es wird gezeigt, dass endlicher Nutzen einige strukturelle Implikationen für die intrinsische Sichtweise hat. Im 3. Teil wird quantitativ untersucht, wie sich Information auf den Nutzen auswirkt. Aus extrinsischer Sicht werden Händler mit unterschiedlichem Wissen verglichen. Falls die Präferenzen durch die logarithmische Nutzenfunktion beschrieben werden, stimmt der Nutzenzuwachs mit der gemeinsamen Information zwischen dem zusätzlichen Wissen und dem ursprünglichen Wissen überein, wobei `gemeinsame Information' im Sinne der Informationstheorie verstanden wird.Stochastic Analysis provides methods to describe random numerical processes. The descriptions depend strongly on the underlying information structure, which is represented in terms of filtrations. The first part of this thesis deals with impacts of changes in the information structure on the appearance of a stochastic process. More precisely, it analyses the consequences of a filtration enlargement on the semimartingale decomposition of the process. The second and third part discuss the role of information in financial utility calculus. The second part is of a qualitative nature: It deals with implications of the assumption that the maximal expected utility of an investor is bounded. It is shown that finite utility implies some structure properties of the price process viewed from the intrinsic perspective. The third part is of a quantitative nature: It analyzes the impact of information on utility. From an extrinsic point of view traders with different knowledge are compared. If the preferences of the investor are described by the logarithmic utility function, then the utility increment coincides with the mutual information between the additional knowledge and the original knowledge

The Shannon information of filtrations and the additional logarithmic utility of insiders

The background for the general mathematical link between utility and information theory investigated in this paper is a simple financial market model with two kinds of small traders: less informed traders and insiders, whose extra information is represented by an enlargement of the other agents' filtration. The expected logarithmic utility increment, that is, the difference of the insider's and the less informed trader's expected logarithmic utility is described in terms of the information drift, that is, the drift one has to eliminate in order to perceive the price dynamics as a martingale from the insider's perspective. On the one hand, we describe the information drift in a very general setting by natural quantities expressing the probabilistic better informed view of the world. This, on the other hand, allows us to identify the additional utility by entropy related quantities known from information theory. In particular, in a complete market in which the insider has some fixed additional information during the entire trading interval, its utility increment can be represented by the Shannon information of his extra knowledge. For general markets, and in some particular examples, we provide estimates of maximal utility by information inequalities.Comment: Published at http://dx.doi.org/10.1214/009117905000000648 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Shannon Information of Filtrations and the Additional Logarithmic Utility of Insiders

The background for the general mathematical link between utility and information theory investigated in this paper is a simple financial market model with two kinds of small traders: less informed traders and insiders, whose extra information is represented by an enlargement of the other agents’ filtration. The expected logarithmic utility increment, i.e. the difference of the insider’s and the less informed trader’s expected logarithmic utility is described in terms of the information drift, i.e. the drift one has to eliminate in order to perceive the price dynamics as a martingale from the insider’s perspective. On the one hand, we describe the information drift in a very general setting by natural quantities expressing the probabilistic better informed view of the world. This on the other hand allows us to identify the additional utility by entropy related quantities known from information theory. In particular, in a complete market in which the insider has some fixed additional information during the entire trading interval, its utility increment can be represented by the Shannon information of his extra knowledge. For general markets, and in some particular examples, we provide estimates of maximal utility by information inequalities.enlargement of filtration, logarithmic utility, utility maximization, heterogeneous information, insider model, Shannon information, information difference, entropy, differential entropy