Faculty Sponsor: Shyam Gouri Suresh
This paper examines the process of assets creation and asset destruction of 100 banks through a computational lens. I assume that banks are interconnected to each other based off their lending behaviors. I apply multiple modelling approaches to this lending behavior between the banks, in which a select few turn out to be large banks. Thinking of network creation between banks as an adjacency matrix, I find that this modeling process makes it very easy to model the effects of a bank run. After assigning reserves to banks based off their assets and liabilities, I apply a shock to every bank. In accordance with modern literature, I believe that all banks are related and that there should be some distribution for the so called shock. In my case, I assume the shocks follows a standard normal distribution. After doing so, I change the average size of the shock and standard deviation to see how different values affect bank runs. I find, regardless of the size of the shock or the modelling approach used, that as the time of the bank run increases, so too does the number of banks that fail. More interestingly, I find that as the standard deviation of the shock increases, that the number of bank deaths falls. Building off this paper in a future paper, I will see how the Central Bank can respond to bank runs and try and prevent them from causing further damage to banks. The purpose of this paper will be to see if government intervention is needed for banks to fix their solvency issues from a bank panic.