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Herd Behavior and Financial Crashes: An Interacting Particle System Approach


Affiliations
1 Department of Computer Science, University of Verona, Strada le Grazie 15, 37134 Verona, Italy
 

We provide an approach based on a modification of the Ising model to describe the dynamics of stock markets. Our model incorporates three different factors: imitation, the impact of external news, and private information; moreover, it is characterized by coupling coefficients, static in time, but not identical for each agent. By analogy with physical models, we consider the temperature parameter of the system, assuming that it evolves with memory of the past, hence considering how former news influences realized market returns. We show that a standard Ising potential assumption is not sufficient to reproduce the stylized facts characterizing financial markets; this is because it assigns low probabilities to rare events. Hence, we study a variation of the previous setting providing, also by concrete computations, new insights and improvements.
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  • Herd Behavior and Financial Crashes: An Interacting Particle System Approach

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Authors

Vincenzo Crescimanna
Department of Computer Science, University of Verona, Strada le Grazie 15, 37134 Verona, Italy
Luca Di Persio
Department of Computer Science, University of Verona, Strada le Grazie 15, 37134 Verona, Italy

Abstract


We provide an approach based on a modification of the Ising model to describe the dynamics of stock markets. Our model incorporates three different factors: imitation, the impact of external news, and private information; moreover, it is characterized by coupling coefficients, static in time, but not identical for each agent. By analogy with physical models, we consider the temperature parameter of the system, assuming that it evolves with memory of the past, hence considering how former news influences realized market returns. We show that a standard Ising potential assumption is not sufficient to reproduce the stylized facts characterizing financial markets; this is because it assigns low probabilities to rare events. Hence, we study a variation of the previous setting providing, also by concrete computations, new insights and improvements.