Detecting asset value dislocations in multi-agent models of market microstructure

Consider a financial market participant observing the trade flow of an asset traded through a limit order book. Trades are driven by an agent-based model where individual agents observe the trading decisions of previous agents, as well as their private signal on the value of the asset and then execute a trading decision. Given trading decisions of agents, how can a market observer detect a shock to the underlying value of the traded asset? The distribution of shock times is assumed to be phase-type distributed to allow for a general set of change time probabilities beyond geometric change times. We show that this problem is equivalent to change detection with social learning. We provide structural results that allow the optimal detection policy to be characterized by a single threshold policy.

[1]  Sanmay Das.  A learning market-maker in the Glosten–Milgrom model , 2005 .

[2]  S. Athey.  Monotone Comparative Statics under Uncertainty , 2002 .

[3]  S. Karlin,et al.  Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions , 1980 .

[4]  R. Cont.  Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models. , 2005 .

[5]  C. Chamley.  Rational Herds: Economic Models of Social Learning , 2003 .

[6]  W. Fleming.  Book Review: Discrete-time Markov control processes: Basic optimality criteria , 1997 .

[7]  Andrea Zaccaria,et al.  Minimal agent based model for financial markets I , 2008, 0808.3562.

[8]  M. Cipriani,et al.  Estimating a Structural Model of Herd Behavior in Financial Markets , 2010, SSRN Electronic Journal.

[9]  Andrea Zaccaria,et al.  Critical Overview of Agent-Based Models for Economics , 2011, 1101.1847.

[10]  M. Marchesi,et al.  VOLATILITY CLUSTERING IN FINANCIAL MARKETS: A MICROSIMULATION OF INTERACTING AGENTS , 2000 .

[11]  M. Marsili,et al.  Minority Games: Interacting agents in financial markets , 2014 .

[12]  Vikram Krishnamurthy,et al.  Quickest Detection POMDPs With Social Learning: Interaction of Local and Global Decision Makers , 2010, IEEE Transactions on Information Theory.

[13]  J. Farmer,et al.  The Predictive Power of Zero Intelligence in Financial Markets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[14]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[15]  Sebastian Jaimungal,et al.  Modelling Asset Prices for Algorithmic and High-Frequency Trading , 2010 .

[16]  H. Sabourian,et al.  Herding and Contrarian Behavior in Financial Markets , 2009 .

[17]  Paul Windrum,et al.  A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems , 2007 .

[18]  D. Madan,et al.  Pricing Equity Default Swaps under an approximation to the CGMY L\'{e}% vy Model , 2007, 0711.2807.

[19]  Estimating a Structural Model of Herd Behavior in Financial Markets , 2010 .

[20]  J. Bouchaud,et al.  Herd Behavior and Aggregate Fluctuations in Financial Markets , 1997 .

[21]  S. Asmussen,et al.  Russian and American put options under exponential phase-type Lévy models , 2004 .

[22]  L. Glosten.  Insider Trading, Liquidity, and the Role of the Monopolist Specialist , 1989 .

[23]  Sanmay Das,et al.  Adapting to a Market Shock: Optimal Sequential Market-Making , 2008, NIPS.