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We investigate a generalized stochastic model with the property known as mean reversion, that is, the tendency to relax towards a historical reference level. Besides this property, the dynamics is driven by multiplicative and additive Wiener processes. While the former is modulated by the internal behavior of the system, the latter is purely exogenous. We focus on the stochastic dynamics of volatilities, but our model may also be suitable for other financial random variables exhibiting the mean reversion property. The generalized model contains, as particular cases, many early approaches in the literature of volatilities or, more generally, of mean-reverting financial processes. We analyze the long-time probability density function associated to the model defined through an Itô-Langevin equation. We obtain a rich spectrum of shapes for the probability function according to the model parameters. We show that additive-multiplicative processes provide realistic models to describe empirical distributions, for the whole range of data.

This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved.

We study self-avoiding walks ͑SAW's͒ on the generalized Sierpinski gasket family of fractals. Each fractal can be labeled by an integer b (2рbрϱ), so that the fractal and spectral dimensions tend to the Euclidean value 2 when b→ϱ. By using an exact enumeration technique to obtain the series expansion for the chaingenerating function of SAW's on these lattices, we calculate the associated critical exponent ␥ b for 2рb р100. The large-b behavior of ␥ b is the first numerical result consistent with the asymptotic convergence toward the Euclidean value ␥ E . We also give an analytic argument supporting the assumption that lim b→ϱ ␥ b →␥ E .

This paper presents an empirical investigation of the intraday Brazilian stock market price fluctuations, considering q-Gaussian distributions that emerge from a nonextensive statistical mechanics. Our results show that, when returns are measured over intervals less than one hour, the empirical distributions are well fitted by q-Gaussians with exponential damped tails. Scaling behavior is also observed for these microscopic time intervals. We find that the time evolution of the distributions is according to a super diffusive q-Gaussian stationary process within a nonlinear Fokker-Planck equation. This regime breaks down due to the exponential fall-off of the tails, which in turn, governs the transient dynamics to the long-term macroscopic Gaussian regime. Our results suggest that this modeling provides a framework for the description of the dynamics of stock markets intraday price fluctuations.

We address a standard class of diffusion processes with linear drift and quadratic diffusion coefficients. These contributions to dynamic equations can be directly drawn from data time series. However, real data are constrained to finite sampling rates and therefore it is crucial to establish a suitable mathematical description of the required finite-time corrections. Based on Itô-Taylor expansions, we present the exact corrections to the finite-time drift and diffusion coefficients. These results allow to reconstruct the real hidden coefficients from the empirical estimates. We also derive higher-order finite-time expressions for the third and fourth conditional moments that furnish extra theoretical checks for this class of diffusion models. The analytical predictions are compared with the numerical outcomes of representative artificial time series.

We investigate the statistics of volumes of shares traded in stock markets. We show that the stochastic process of trading volumes can be understood on the basis of a mixed Poisson process at the microscopic time level. The beta distribution of the second kind (also known as q-gamma distribution), that has been proposed to describe empirical volume histograms, naturally results from our analysis. In particular, the shape of the distribution at small volumes is governed by the degree of granularity in the trading process, while the exponent controlling the tail is a measure of the inhomogeneities in market activity. Furthermore, the present case furnishes empirical evidence of how power law probability distributions can arise as a consequence of a fluctuating intrinsic parameter.

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