On discrete stochastic processes with long-lasting time dependence

In this manuscript, we analytically and numerically study statistical properties of an heteroskedastic process based on the celebrated ARCH generator of random variables whose variance is defined by a memory of $q_{m}$-exponencial, form ($e_{q_{m}=1}^{x}=e^{x}$). Specifically, we inspect the self-correlation function of squared random variables as well as the kurtosis. In addition, by numerical procedures, we infer the stationary probability density function of both of the heteroskedastic random variables and the variance, the multiscaling properties, the first-passage times distribution, and the dependence degree. Finally, we introduce an asymmetric variance version of the model that enables us to reproduce the so-called leverage effect in financial markets.Comment: 24 page

Are all highly liquid securities within the same class?

In this manuscript we analyse the leading statistical properties of fluctuations of (log) 3-month US Treasury bill quotation in the secondary market, namely: probability density function, autocorrelation, absolute values autocorrelation, and absolute values persistency. We verify that this financial instrument, in spite of its high liquidity, shows very peculiar properties. Particularly, we verify that log-fluctuations belong to the Levy class of stochastic variables.Comment: To be published in EPJ

On superstatistical multiplicative-noise processes

In this manuscript we analyse the long-term probability density function of non-stationary dynamical processes which are enclosed inward the Feller class of processes with time varying exponents for multiplicative noise. The update in the value of the exponent occurs in the same conditions presented by Beck and Cohen for superstatistics. Moreover, we are able to provide a dynamical scenario for the emergence of a generalisation of the Weibull distribution previously introduced.Comment: 7 pages, 8 figures. A note about the application on turbulence models has been added to this final published versio

Edge of chaos of the classical kicked top map: Sensitivity to initial conditions

We focus on the frontier between the chaotic and regular regions for the classical version of the quantum kicked top. We show that the sensitivity to the initial conditions is numerically well characterised by $\xi=e_q^{\lambda_q t}$, where $e_{q}^{x}\equiv [ 1+(1-q) x]^{\frac{1}{1-q}} (e_1^x=e^x)$, and $\lambda_q$ is the $q$-generalization of the Lyapunov coefficient, a result that is consistent with nonextensive statistical mechanics, based on the entropy $S_q=(1- \sum_ip_i^q)/(q-1) (S_1 =-\sum_i p_i \ln p_i$). Our analysis shows that $q$ monotonically increases from zero to unity when the kicked-top perturbation parameter $\alpha$ increases from zero (unperturbed top) to $\alpha_c$, where $\alpha_c \simeq 3.2$. The entropic index $q$ remains equal to unity for $\alpha \ge \alpha_c$, parameter values for which the phase space is fully chaotic.Comment: To appear in "Complexity, Metastability and Nonextensivity" (World Scientific, Singapore, 2005), Eds. C. Beck, A. Rapisarda and C. Tsalli

Bridging stylized facts in finance and data non-stationarities

Employing a recent technique which allows the representation of nonstationary data by means of a juxtaposition of locally stationary patches of different length, we introduce a comprehensive analysis of the key observables in a financial market: the trading volume and the price fluctuations. From the segmentation procedure we are able to introduce a quantitative description of a group of statistical features (stylizes facts) of the trading volume and price fluctuations, namely the tails of each distribution, the U-shaped profile of the volume in a trading session and the evolution of the trading volume autocorrelation function. The segmentation of the trading volume series provides evidence of slow evolution of the fluctuating parameters of each patch, pointing to the mixing scenario. Assuming that long-term features are the outcome of a statistical mixture of simple local forms, we test and compare different probability density functions to provide the long-term distribution of the trading volume, concluding that the log-normal gives the best agreement with the empirical distribution. Moreover, the segmentation of the magnitude price fluctuations are quite different from the results for the trading volume, indicating that changes in the statistics of price fluctuations occur at a faster scale than in the case of trading volume.Comment: 13 pages, 12 figure