Stylized Facts and Their Microscopic Origins: Clustering, Persistence, and Stability in a 2D Ising Framework

ArXiv ID: 2512.17925 “View on arXiv”

Authors: Hernán Ezequiel Benítez, Claudio Oscar Dorso

Abstract

The analysis of financial markets using models inspired by statistical physics offers a fruitful approach to understand collective and extreme phenomena [“3, 14, 15”] In this paper, we present a study based on a 2D Ising network model where each spin represents an agent that interacts only with its immediate neighbors plus a term reated to the mean field [“1, 2”]. From this simple formulation, we analyze the formation of spin clusters, their temporal persistence, and the morphological evolution of the system as a function of temperature [“5, 19”]. Furthermore, we introduce the study of the quantity $1/2P\sum_{“i”}|S_{“i”}(t)+S_{“i”}(t+Δt)|$, which measures the absolute overlap between consecutive configurations and quantifies the degree of instantaneous correlation between system states. The results show that both the morphology and persistence of the clusters and the dynamics of the absolute sum can explain universal statistical properties observed in financial markets, known as stylized facts [“2, 12, 18”]: sharp peaks in returns, distributions with heavy tails, and zero autocorrelation. The critical structure of clusters and their reorganization over time thus provide a microscopic mechanism that gives rise to the intermittency and clustered volatility observed in prices [“2, 15”].

Keywords: Statistical Physics, Ising Model, Agent-Based Models, Stylized Facts, Criticality, General/Market Microstructure

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper uses advanced statistical physics concepts like the 2D Ising model, Glauber dynamics, and critical temperature analysis, showing high mathematical complexity. However, it lacks backtests, real market data, or implementation details, relying instead on a conceptual computational model, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal: Microscopic Origins of Stylized Facts"] --> B["Methodology: 2D Ising Model"]
    
    B --> C["Data/Input: Agent Interactions <br> - Nearest neighbors <br> - Mean-field term"]
    
    C --> D["Computation: System Evolution <br> - Vary Temperature <br> - Simulate Spin Dynamics"]
    
    D --> E{"Analysis of Outcomes"}
    
    E --> F["Morphology: Cluster Formation <br> & Persistence"]
    E --> G["Quantity Q(t): <br> Absolute Overlap <br> 1/2P Σ|S_i(t) + S_i(t+Δt)|"]
    
    F & G --> H["Key Findings: Universal Stylized Facts Explained <br> - Heavy Tails <br> - Intermittent Volatility <br> - Zero Autocorrelation"]
    
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