Moment instabilities in multidimensional systems with noise


We present a systematic study of moment evolution in multidimensional stochastic difference systems, focusing on characterizing systems whose low-order moments diverge in the neighborhood of a stable fixed point. We consider systems with a simple, dominant eigenvalue and stationary, white noise. When the noise is small, we obtain general expressions for the approximate asymptotic distribution and moment Lyapunov exponents. In the case of larger noise, the second moment is calculated using a different approach, which gives an exact result for some types of noise. We analyze the dependence of the moments on the system’s dimension, relevant system properties, the form of the noise, and the magnitude of the noise. We determine a critical value for noise strength, as a function of the unperturbed system’s convergence rate, above which the second moment diverges and large fluctuations are likely. Analytical results are validated by numerical simulations. Finally, we present a short discussion of the extension of our results to the continuous time limit. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2005

Similar works

Full text


Research Papers in Economics

Provided original full text link
Last time updated on 7/6/2012

This paper was published in Research Papers in Economics.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.