Effect of asymmetric persistence in the modified nonlinear Langevin model on behavior of extremes in generated time series

Abstract

Asymmetry in persistence introduces an additional directed force that alters the dynamics of stochastic processes, potentially affecting the behavior of extremes in their realizations (time series). In this work, we investigate these effect, unexplored so far, using a modified Langevin model that incorporates an asymmetric persistence mechanism. Extremes are defined through run theory and analyzed using informational measures, alongside examining the topological properties of visibility graphs constructed from the point processes of extremes. Our results reveal a systematic influence of asymmetry on the behavior of extremes—both their size and magnitude decrease with increasing asymmetry, while their degree of order, quantified by Shannon entropy, increases.