Titlebook: Dynamic Markov Bridges and Market Microstructure; Theory and Applicati Umut ?etin,Albina Danilova Book 2018 Springer Science+Business Media

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Markov ProcessesMarkov processes model the evolution of random phenomena whose future behaviour is independent of the past given their current state. In this section we will make this notion, i.e. ., precise in a general context.

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Financial Markets with Informational Asymmetries and EquilibriumThis chapter introduces the setup for the equilibrium models that extends, among others, the works of Kyle and Back. It also contains some key results that will be relevant for the characterisation of the equilibrium. Finally the equilibrium will be derived and discussed in Chaps. . and ..

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Jaya R. Soneji,Madhugiri Nageswara Raothe filtering equations of Chap. .. This well posedness will be obtained by establishing the relationship between solutions of martingale problems and stochastic differential equations (SDEs). Thus, our focus in this chapter will be the connection between SDEs and martingale problems. To formulate t

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Jaya R. Soneji,Madhugiri Nageswara Raowill bound them to infer the information of other players from their actions. To avoid the pathological cases this information ought to be contaminated, e.g. by noise trades. This corresponds to a stochastic filtering problem which we will formalise in this chapter.

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Jaya R. Soneji,Madhugiri Nageswara Raodvance but revealed via an observation of a related process. We will call such a process dynamic Markov bridge. We provide conditions under which such a process exists as a unique solution of an SDE. This construction will be fundamental in solving the Kyle–Back models considered in the second part

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Umut ?etin,Albina DanilovaCutting-edge interdisciplinary research in the areas of applied statistics, mathematics, finance, and economics.First comprehensive text on using Dynamic Markov Bridges to study asymmetric information