Emergence of Complex Spatio-Temporal Behavior in Nonlinear Field Theories

We investigate the emergence of time-dependent nonperturbative configurations during the evolution of nonlinear scalar field models with symmetric and asymmetric double-well potentials. Complex spatio-temporal behavior emerges as the system seeks to establish equipartition after a fast quench. We show that fast quenches may dramatically modify the decay rate of metastable states in first order phase transitions. We briefly suggest possible applications incondensed matter systems and early universe cosmology.Comment: 6 pages, 8 figure

Resonant emergence of global and local spatiotemporal order in a nonlinear field model

We investigate the nonequilibrium evolution of a scalar field in (2+1) dimensions. The field is set in a double-well potential in contact (open) or not (closed) with a heat bath. For closed systems, we observe the synchronized emergence of coherent spatiotemporal configurations, identified with oscillons. This initial global ordering degenerates into localized order until all oscillons disappear. We show that the synchronization is driven by resonant parametric oscillations of the field's zero mode and that local ordering is only possible outside equipartition. None of these orderings occur for open systems.Comment: 4 pages, 5 figures, LaTeX, minor corrections to eqs. 1,3,

Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging