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This thesis explores the application of the Ising model—originally developed in

statistical physics to study ferromagnetism—to financial markets. Mainly focusing

empirical puzzles such as volatility clustering, fat-tailed return distributions, and

excess volatility, which challenge the assumptions of classical economic theories,

we investigate how the Ising model and its extensions can provide a more real-

istic framework for modeling market dynamics. The study begins with a review

of the theoretical foundations of the Ising model, which includes its mean-field

approximation and Monte Carlo simulation techniques. We then analyze the key

adaptations of the model by researchers such as Kaizoji, Bornholdt, Zhou, and

Sornette, who have utilized the model to simulate phase transitions, herding be-

havior, and self-organization in financial systems. Through numerical simulations

based on these models, we replicate stylized facts observed in financial time se-

ries, demonstrating the model’s ability to capture complex market phenomena.

Our findings support the potential of the Ising framework as a valuable tool for

understanding and analyzing the collective behavior of market participants.