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Learning by Trading in Infinite Horizon Strategic Market Games with Default

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    Abstract

    We study the consequences of dropping the perfect competition assumption in an infinite horizon model with infinitely-lived traders and collateralized assets together with asymmetric information among players and incomplete monitoring. Trading assets is not only a way to hedge oneself against uncertainty and to smooth consumption across time: It also enables learning information. We focus on learning equilibria, at the end of which no player has incorrect beliefs because they have taken time to update their prior belief. We prove a partial Folk theorem à la Wiseman (2012) of the following form: For any function that maps each state of the world to a sequence of feasible and strongly individually rational allocations, and for any degree of precision, there is a perfect Bayesian equilibrium in which patient players learn the realized state with this degree of precision and achieve a payoff close to the one specified for each state. When the number of traders grows to infinity, the resulting equilibrium set sharply contrasts with that of standard perfectly competitive equilibria.

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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2012/12062R.pdf
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    Bibliographic Info

    Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 12062r.

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    Length: 37 pages
    Date of creation: Sep 2012
    Date of revision: Oct 2013
    Handle: RePEc:mse:cesdoc:12062r

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    Related research

    Keywords: Strategic market games; infinite horizon; incomplete markets; collateral; incomplete information; learning; adverse selection.;

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