Long Range Interaction Generating Fat-Tails in Finance

It's commonly known that the correlation between stocks increases during market turbulent periods. In this work we propose a modellization of this feature, viewed as a collective effect, rearranging a toy-model first proposed in 2001. Equities are modelled as quasi random walk variables, where the non-Brownian components of stocks movement are linked to the market trend via a long range interaction function. Our model generates fat tails for stock probability distributions and implied volatility surfaces analogous to real data, suggesting an unitary picture of long range interaction, fat tails and volatility smiles.