Nonlinear monotonic volatility duration and matching energy of random exclusion financial dynamics

Abstract

Financial markets have been known to exhibit plentiful nonlinear complex volatility behaviors. In order to reproduce the volatility dynamics of financial price changes, the agent-based financial model is established by stochastic finite-range exclusion process. The exclusion process is a kind of statistical physics system, which is considered as modeling particle Markov motion with conserved number of particles. To measure the volatility of financial return series, a novel statistic called maximum monotonic volatility rate is put forward to measure the speed of monotonic volatility of returns. Meanwhile, average monotonic volatility duration of returns is also investigated, which can reflect the average volatility level. For verifying the rationality of the model, matching energy analysis that can detect chaos and complexity in nonlinear time series is applied to study the new statistics. Further, empirical mode decomposition and multifractal are employed to study the behaviors of monotonic volatility duration. The model has similar complexity behaviors with real markets in terms of monotonic volatility with matching energy analysis, and the proposed financial model and real markets both show multifractal and anti-correlation for average monotonic volatility series by MFDFA method. The results display that the model is feasible with respect to above volatility analyses.