We propose a zero-intelligence agent-based model of the E-Mini S&P 500 futures market, which allows for a close examination of the market microstructure. Several classes of agents are characterized by their order speed and order placement within the limit order book. These agents' orders populate the simulated market in a way consistent with real world participation rates. By modeling separate trading classes the simulation is able to capture interactions between classes, which are essential to recreating market phenomenon. The simulated market is validated against empirically observed characteristics of price returns and volatility. We therefore conclude that our agent based simulation model can accurately capture the key characteristics of the nearest months E-Mini S&P 500 futures market. Additionally, to illustrate the applicability of the simulation, experiments were run, which confirm the leading hypothesis for the cause of the May 6 th 2010 Flash Crash.

A major credit shock can induce large intraday variation margin payments between counterparties in derivatives markets, which may force some participants to default on their payments. These payment shortfalls become amplified as they cascade through the network of exposures. Using detailed Depository Trust & Clearing Corporation data, we model the full network of exposures, shock-induced payments, initial margin collected, and liquidity buffers for about 900 firms operating in the U.S. credit default swaps market. We estimate the total amount of contagion, the marginal contribution of each firm to contagion, and the number of defaulting firms for a systemic shock to credit spreads. A novel feature of the model is that it allows for a range of behavioral responses to balance sheet stress, including delayed or partial payments. The model provides a framework for analyzing the relative effectiveness of different policy options, such as increasing margin requirements or mandating greater liquidity reserves. This paper was accepted by Karl Diether, finance.

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The potential impact of interconnected financial institutions on interbank financial systems is a financial stability concern for central banks and regulators. In examining how financial shocks propagate through contagion effects, we argue that endogenous individual bank choices are necessary to properly consider how losses develop as the interbank lending network evolves. We present an agent-based model to endogenously reconstruct interbank networks based on 6,600 banks' decision rules and behaviors reflected in quarterly balance sheets. We compare the results of our model to the results of a traditional stationary network framework for contagion. The model formulation reproduces dynamics similar to those of the 2007-09 financial crisis and shows how bank losses and failures arise from network contagion and lending market illiquidity. When calibrated to post-crisis data from 2011-14, the model shows the U.S. banking system has reduced its likelihood of bank failures through network contagion and illiquidity, given a similar stress scenario.

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