Enterprise Hall, 318
April 12, 2009, 08:00 PM to 07:00 PM
How do institutional arrangements in banking affect the occurrence of crises? The first two chapters present an endeavor in using new modeling techniques to answer this question. Even if the results are not widely accepted, the way in which the problem is tackled here is offered for consideration and debate. Is capital the result of an evolving process that takes advantages of entrepreneurial networks? The last chapter put forth a model wherein firms develop economic ties with one another. By doing so, a market network unfolds along time as a spontaneous process.
In the first chapter, I explore the occurrence of bank runs by developing a sensitivity analysis to the model in Diamond and Dybvig (1983). I implement an agent-based economic model to analyze different modifications and extensions to the original. In 36 experiments based on three different versions of the onebank model the frequency of bank runs dropped from 42% to 17%. This was due to changes in the payoffs structure and social network effects whereby depositors go to the bank if at least three of their proximate neighbors went previously. What is the role of inter-bank markets and central banks in coping with banking crises? In experiments conducted within an agent-based framework wherein multiple banks are interacting in an inter-bank market. I found that if banks have the same market share there are no runs escalating to systemic panics. In contrast, if there is one bank with a market share twice as big as the other ones, a liquidity crisis spreads to more than one bank. Furthermore, when banks cannot interact, then runs in isolated banks occur with a higher frequency. Finally, adding a central bank unexpectedly increases the occurrence of bank runs. Institutional complexity helps to reduce the frequency of bank runs. Hence, decentralized institutional structures perform better than centralized ones.
The objective in this chapter is to implement a parsimonious agent-based computational model of economic networks whereby agents make strategic decisions based upon profits and information generated through their immediate social network. In this model firms are represented by nodes and the links between each pair of them are the result of a mutually advantageous economic decision. Therefore, links are two-sided or undirected. The economic decision is based on two elements, namely: a myopic profit motive and local information channeled through collaborating firms. Here I endogenize the formation and deletion of links. Furthermore the number of firms (nodes) in the network at each time by allowing firms (nodes) to enter and exit the market. Centrality measures are reported together with firms’ profits. The evolution of the network yields higher connectivity and profits when the (positive) externality is high and the rule to exit the market more strict. The higher the network connectivity, the higher the overall profits of firms.