Asymmetric Tsallis distributions for modelling financial market dynamics

TL;DR

This paper introduces an asymmetric distribution model based on Tsallis statistics to better fit and analyze the non-symmetric, non-linear behavior of financial market returns over various time scales, especially during crises.

Contribution

It proposes a novel asymmetric q-Gaussian mixture model with different parameters for positive and negative returns, improving fit over traditional symmetric models.

Findings

Asymmetric distributions fit market data better than symmetric ones.

Positive and negative returns exhibit different q parameter behaviors over time.

Market behavior transitions from normal to superdiffusive during crises.

Abstract

Financial markets are highly non-linear and non-equilibrium systems. Earlier works have suggested that the behavior of market returns can be well described within the framework of non-extensive Tsallis statistics or superstatistics. For small time scales (delays), a good fit to the distributions of stock returns is obtained with q-Gaussian distributions, which can be derived either from Tsallis statistics or superstatistics. These distributions are symmetric. However, as the time lag increases, the distributions become increasingly non-symmetric. In this work, we address this problem by considering the data distribution as a linear combination of two independent normalized distributions - one for negative returns and one for positive returns. Each of these two independent distributions are half q-Gaussians with different non-extensivity parameter q and temperature parameter beta. Using this model, we investigate the behavior of stock market returns over time scales from 1 to 80 days. The data covers both the .com bubble and the 2008 crash periods. These investigations show that for all the time lags, the fits to the data distributions are better using asymmetric distributions than symmetric q-Gaussian distributions. The behaviors of the q parameter are quite different for positive and negative returns. For positive returns, q approaches a constant value of 1 after a certain lag, indicating the distributions have reached equilibrium. On the other hand, for negative returns, the q values do not reach a stationary value over the time scales studied. In the present model, the markets show a transition from normal to superdiffusive behavior (a possible phase transition) during the 2008 crash period. Such behavior is not observed with a symmetric q-Gaussian distribution model with q independent of time lag.