Stationary Distributions of the Mode-switching Chiarella Model

TL;DR

This paper derives the stationary distributions of a complex financial market model, revealing conditions for unimodality and bimodality in mispricing and trend distributions, and refuting some previous claims.

Contribution

It provides the first analytical derivation of stationary distributions in the extended Chiarella model, clarifying bifurcation conditions and correcting prior misconceptions.

Findings

Mispricing and trend distributions are Gaussian in small noise limit.

Bimodality in mispricing occurs only with strong trend feedback.

Bifurcation point differs from the dynamical system's bifurcation condition.

Abstract

We derive the stationary distribution in various regimes of the extended Chiarella model of financial markets. This model is a stochastic nonlinear dynamical system that encompasses dynamical competition between a (saturating) trending and a mean-reverting component. We find the so-called mispricing distribution and the trend distribution to be unimodal Gaussians in the small noise, small feedback limit. Slow trends yield Gaussian-cosh mispricing distributions that allow for a P-bifurcation: unimodality occurs when mean-reversion is fast, bimodality when it is slow. The critical point of this bifurcation is established and refutes previous ad-hoc reports and differs from the bifurcation condition of the dynamical system itself. For fast, weakly coupled trends, deploying the Furutsu-Novikov theorem reveals that the result is again unimodal Gaussian. For the same case with higher coupling we disprove another claim from the literature: bimodal trend distributions do not generally imply bimodal mispricing distributions. The latter becomes bimodal only for stronger trend feedback. The exact solution in this last regime remains unfortunately beyond our proficiency.