Central limit theorem for anomalous scaling due to correlations

Relationship between nonlinearities and thermalization in classical open systems: The role of the interaction range

We discuss results on the dynamics of thermalization for a model with Gaussian interactions between two classical many-body systems trapped in external harmonic potentials. Previous work showed an approximate power-law scaling of the interaction energy with the number of particles, with particular focus on the dependence of the anomalous exponent on the interaction strength. Here we explore the role of the interaction range in determining anomalous exponents, showing that it is a more relevant parameter to differentiate distinct regimes of responses of the system. More specifically, on varying the interaction range from its largest values while keeping the interaction strength constant, we observe a crossover from an integrable system, approximating the Caldeira-Leggett interaction term in the long-range limit, to an intermediate interaction range in which the system manifests anomalous scaling, and finally to a regime of local interactions in which anomalous scaling disappears. A Fourier analysis of the interaction energy shows that nonlinearities give rise to an effective bath with a broad band of frequencies, even when starting with only two distinct trapping frequencies, yielding efficient thermalization in the intermediate regime of interaction range. We provide qualitative arguments, based on an analogous Fourier analysis of the standard map, supporting the view that anomalous scaling and features of the Fourier spectrum may be used as proxies to identify the role of chaotic dynamics. Our work, that encompasses models developed in different contexts and unifies them in a common framework, may be relevant to the general understanding of the role of nonlinearities in a variety of many-body classical systems, ranging from plasmas to trapped atoms and ions.

Central limit theorem for a class of globally correlated random variables

The standard central limit theorem with a Gaussian attractor for the sum of independent random variables may lose its validity in the presence of strong correlations between the added random contributions. Here, we study this problem for similar interchangeable globally correlated random variables. Under these conditions, a hierarchical set of equations is derived for the conditional transition probabilities. This result allows us to define different classes of memory mechanisms that depend on a symmetric way on all involved variables. Depending on the correlation mechanisms and statistics of the single variables, the corresponding sums are characterized by distinct probability densities. For a class of urn models it is also possible to characterize their domain of attraction, which, as in the standard case, is parametrized by the probability density of each random variable. Symmetric and asymmetric q-Gaussian attractors (q<1) arise in a particular two-state case of these urn models.

Scaling symmetry, renormalization, and time series modeling: the case of financial assets dynamics

We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.

Accelerating growth and size-dependent distribution of human online activities

Research on human online activities usually assumes that total activity T increases linearly with active population P, that is, T∝P(γ) (γ=1). However, we find examples of systems where total activity grows faster than active population. Our study shows that the power law relationship T∝P(γ) (γ>1) is in fact ubiquitous in online activities such as microblogging, news voting, and photo tagging. We call the pattern "accelerating growth" and find it relates to a type of distribution that changes with system size. We show both analytically and empirically how the growth rate γ associates with a scaling parameter b in the size-dependent distribution. As most previous studies explain accelerating growth by power law distribution, the model of size-dependent distribution is worth further exploration.