The goal of this project is to understand, both in a general theoretical way, and in concrete and empirically relevant economic problems, asset market equilibrium. Algorithms for computing equilibria in models with a mixture of private and social risk were programmed under the previous grant. They are used in this product to study a new class of models with limited insurance possibilities: debt constrained asset markets. Liquidity constraints emerge in this model from the assumption that it is not possible to force borrowers to service or pay off debts from their future income. Traders may be denied credit, and their assets may be attached for the payment of past debts. This is a reasonable representation of the way in which actual asset markets work. Debtor prison no longer exists, and indeed, modern bankruptcy laws make it possible for traders to preserve some assets even in the face of bankruptcy laws. Debt constrained markets could explain why insurance markets are incomplete in real world economies. Research is continued on the dynamics of asset distribution models. This project relaxes the assumption of a representative economic agent made in real business cycle models. Numerical simulation on the supercomputer is used to determine the global picture of the set of equilibria of asset distributions with a mix of private and social risk. The project develops and tests methods of accurately approximating these asset distribution. Methods developed in the previous project to study reputation and learning in repeated games are extended and used to study learning and approximate knowledge in asset markets. This is important because it permits rigorous analysis of asset market behavior that can't otherwise be explained. Most current theories either assume common knowledge or assume behavior that is so stupid that it is hard to imagine even a naive agent behaving this way. In sum, this research develops powerful new methodological tools for studying market dynamics with heterogeneous agents and for rigorously modelling markets in which agents make mistakes but learn from their mistakes. These tools are used to study realistic models of asset markets in which certain risks can not be insured or can be insured at prohibitive rates.
