On the concentration of large deviations for fat tailed distributions, with application to financial data

TL;DR

This paper investigates how large deviations in fat tailed distributions tend to concentrate on single variables, revealing phase transitions and implications for financial data, such as stock price jumps and market eigenvalues.

Contribution

It introduces a phase transition framework for large deviations in fat tailed distributions and applies it to explain phenomena in financial markets.

Findings

Large deviations concentrate on single variables in fat tailed distributions.

Financial large fluctuations are more likely due to continuous drifts than jumps.

Market eigenvalues can be explained by large deviations with excess covariance.

Abstract

Large deviations for fat tailed distributions, i.e. those that decay slower than exponential, are not only relatively likely, but they also occur in a rather peculiar way where a finite fraction of the whole sample deviation is concentrated on a single variable. The regime of large deviations is separated from the regime of typical fluctuations by a phase transition where the symmetry between the points in the sample is spontaneously broken. For stochastic processes with a fat tailed microscopic noise, this implies that while typical realizations are well described by a diffusion process with continuous sample paths, large deviation paths are typically discontinuous. For eigenvalues of random matrices with fat tailed distributed elements, a large deviation where the trace of the matrix is anomalously large concentrates on just a single eigenvalue, whereas in the thin tailed world the large deviation affects the whole distribution. These results find a natural application to finance. Since the price dynamics of financial stocks is characterized by fat tailed increments, large fluctuations of stock prices are expected to be realized by discrete jumps. Interestingly, we find that large excursions of prices are more likely realized by continuous drifts rather than by discontinuous jumps. Indeed, auto-correlations suppress the concentration of large deviations. Financial covariance matrices also exhibit an anomalously large eigenvalue, the market mode, as compared to the prediction of random matrix theory. We show that this is explained by a large deviation with excess covariance rather than by one with excess volatility.