Publications

Publication list

Working papers

List of older working papers

Conferences organized

Heterogeneous Agents and Agent-based Modeling (Washington, DC, 2017)

Summary of recent working papers

  1. Dynamic Order Dispersion and Volatility Persistence in a Simple Limit Order Book Model
    • Joint with: Andrew Hawley, Mark Paddrik, and Nathan Palmer
    • Abstract: This preliminary paper extends the dynamics of a basic stylized limit order book model introduced in \mycite{ChiIor2002}. The original model is capable of generating some key market microstructure features, but it cannot recreate longer range persistence in volatility. We explore a very simple and intuitive addition to the stylized, near zero intelligence behavior of traders that is capable of delivering persistent volatility. We also show that this strategy depends critically on certain key features in the dynamics of supply and demand for liquidity and depth in the limit order book. We believe this is fundamental to understanding both the dynamics of volatility in financial time series, along with variations in liquidity in financial markets. We contribute a parsimonious agent-based model to the literature that may be used as a test bed or sandbox for developing agents with more complex behavior.
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  2. Learning Integrated Inflation Forecasts in a Simple Multi-agent Macroeconomic Model
    • Joint with: Karen Smith
    • Abstract: This paper implements a model with a population of heterogeneous macro forecasters. Their objectives are to forecast output and inflation, both inputs in standard New Keynesian macro models. The model is implemented by first calibrating the agents to professional forecasters at the micro level. Model runs then try to replicate both the dynamics, bias and cross sectional heterogeneity of forecasts and the economy. These are done both in a model with static forecasters, and one where the forecasters are learning from each other in a social fashion. We find that expectations about the inflation process which conjecture near random walk behavior can be self-fulfilling, yielding inflation volatility and persistence on the order of magnitude of U.S. macro data. However, our forecasting populations often fall short of the heterogeneity of predictions from survey data. In some cases, monetary policy can be used to shift the model from its volatile/persistent equilibrium over to a more stable, strongly mean reverting inflation rate.
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  3. A Century of Market Reversals: Resurrecting Volatility
    • Joint with: Vincent Bogousslavsky and Jeffrey Pontiff
    • Abstract: Inventory models posit that return autocorrelation is affected by collateral, volume, and expected volatility. We show that daily market autocorrelations are lower on negative return days, consistent with collateral concerns. Unlike previous literature, we document a strong role of volatility on autocorrelation. Puzzlingly, anticipated volume, not volume shocks, drive reversals. Sparked by these findings, we construct a liquidity risk factor in accordance with Pastor-Stambaugh (2003) that is volatility, not volume, based. The volatility-based factor is more robust and has a higher risk premium than the volume-based factor.
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  4. Epidemics with Space, Movement, and Asymptomatic Spreading
  5. Forecasting Realized Volatility with Kernel Ridge Regression
  6. Optimality of Short Term Rules of Thumb at Long Horizons for an Agent-based Financial Market