scaling and memory in the return intervals of realized volatility abstract we perform return interval analysis of 1 min realized volatility defined by the sum of absolute high frequency intraday returns for the shanghai stock exchange composite index ssec and 22 constituent stocks of ssec the scaling behavior and memory effect of the return intervals between successive realized volatilities above a certain threshold q are carefully investigated in comparison with the volatility defined by the closest tick prices to the minute marks the return interval distribution for the realized volatility shows a better scaling behavior since 20 stocks out of 22 stocks and the ssec pass the kolmogorov smirnov ks test and exhibit scaling behaviors among which the scaling function for 8 stocks could be approximated well by a stretched exponential distribution revealed by the ks goodness of fit test under the significance level of 5 the improved scaling behavior is further confirmed by the relation between the fitted exponent and the threshold q in addition the similarity of the return interval distributions for different stocks is also observed for the realized volatility the investigation of the conditional probability distribution and the detrended fluctuation analysis dfa show that both short term and long term memory exists in the return intervals of realized volatility

a periodic solution of a differential equation with state dependent delay we study a differential equation for delayed negative feedback which models a situation where the delay depends on the present state and becomes effective in the future the main result is existence of a periodic solution in case the equilibrium is linearly unstable the proof employs the ejective fixed point principle on a compact convex set k0 c h 0 r of lipschitz continuous functions and uses that the equation generates a smooth semiflow on an infinite dimensional submanifold of the space c1 h 0 r

network analysis via partial spectral factorization and gauss quadrature large scale networks arise in many applications it is often of interest to be able to identify the most important nodes of a network or to ascertain the ease of traveling between nodes these and related quantities can be determined by evaluating expressions of the form mathbf u tf a mathbf w where a is the adjacency matrix that represents the graph of the network f is a nonlinear function such as the exponential function and mathbf u and mathbf w are vectors for instance axis vectors this paper describes a novel technique for determining upper and lower bounds for expressions mathbf u tf a mathbf w when a is symmetric and bounds for many vectors mathbf u and mathbf w are desired the bounds are computed by first evaluating a low rank approximation of a which is used to determine rough bounds for the desired quantities for all nodes these rough bounds indicate for which vectors mathbf u and mathbf w more accurate bounds should be computed with the aid

extreme value statistics in records with long term persistence many natural records exhibit long term correlations characterized by a power law decay of the autocorrelation function c s ensuremath sim s ensuremath ensuremath gamma with time lag s and correlation exponent 0l ensuremath gamma l1 we study how the presence of such correlations affects the statistics of the extreme events i e the maximum values of the signal within time segments of the fixed duration r we find numerically that i the integrated distribution function of the maxima converges to a gumbel distribution for large r similar to uncorrelated signals ii the deviations for finite r depend on the initial distribution of the records and on their correlation properties iii the maxima series exhibit long term correlations similar to those of the original data and most notably iv the maxima distribution as well as the mean maxima significantly depend on the history in particular on the previous maximum the last item implies that conditional mean maxima and conditional maxima distributions with the value of the previous maximum as condition should be considered for an improved extreme event prediction we provide indications that this dependence of the mean maxima on the previous maximum occurs also in observational long term correlated records

universal fluctuations and extreme value statistics we study the effect of long range algebraic correlations on extreme value statistics and demonstrate that correlations can produce a limit distribution which is indistinguishable from the ubiquitous bramwell holdsworth pinton distribution we also consider the square width fluctuations of the avalanche signal we find as recently predicted by antal et al for logarithmic correlated 1 f signals that these fluctuations follow the fisher tippett gumbel distribution from uncorrelated extreme value statistics

the poincare bendixson theorem for monotone cyclic feedback systems with delay abstract we consider cyclic nearest neighbor systems of differential delay equations in which the coupling between neighbors possesses a monotonicity property using a discrete integer valued lyapunov function we prove that the poincare bendixson theorem holds for such systems we also obtain results on piecewise monotonicity and stability of periodic solutions of such systems

method for generating long range correlations for large systems we propose a new method to generate a sequence of random numbers with long range power law correlations that overcomes known difficulties associated with large systems the new method presents an improvement on the commonly used methods we apply the algorithm to generate enhanced diffusion isotropic and anisotropic self affine surfaces and isotropic and anisotropic correlated percolation

long term memory in extreme returns of financial time series abstract it is well known that while daily price returns of financial markets are uncorrelated their absolute values volatility are long term correlated here we provide evidence that certain subsequences of the returns themselves also exhibit long term memory these subsequences consist of maxima or minima of returns in consecutive time windows of r days our analysis shows that for both stocks and currency exchange rates long term correlations are significant for r 4 we argue that this long term memory which is similar to that observed in volatility clustering sheds further insight on price dynamics that might be used for risk estimation

renormalization group theory for finite size scaling in extreme statistics we present a renormalization group rg approach to explain universal features of extreme statistics applied here to independent identically distributed variables the outlines of the theory have been described in a previous paper the main result being that finite size shape corrections to the limit distribution can be obtained from a linearization of the rg transformation near a fixed point leading to the computation of stable perturbations as eigenfunctions here we show details of the rg theory which exhibit remarkable similarities to the rg known in statistical physics besides the fixed points explaining universality and the least stable eigendirections accounting for convergence rates and shape corrections the similarities include marginally stable perturbations which turn out to be generic for the fisher tippett gumbel class distribution functions containing unstable perturbations are also considered we find that after a transitory divergence they return to the universal fixed line at the same or at a different point depending on the type of perturbation

long range anticorrelations and non gaussian behavior of the heartbeat we find that the successive increments in the cardiac beat to beat interals of healthy subjects display scale invariant long range anticorrelations up to 10 4 heart beats furthermore we find that the histogram for the heartbeat intervals increments is well described by a l evy stable distribution for a group of subjects with severe heart disease we find that the distribution is unchanged but the long range correlations vanish therefore the different scaling behavior in health and disease must relate to the underlying dynamics of the heartbeat

quadrature rule based bounds for functions of adjacency matrices bounds for entries of matrix functions based on gauss type quadrature rules are applied to adjacency matrices associated with graphs this technique allows to develop inexpensive and accurate upper and lower bounds for certain quantities estrada index subgraph centrality communicability that describe properties of networks

airy distribution function from the area under a brownian excursion to the maximal height of fluctuating interfaces the airy distribution function describes the probability distribution of the area under a brownian excursion over a unit interval surprisingly this function has appeared in a number of seemingly unrelated problems mostly in computer science and graph theory in this paper we show that this distribution function also appears in a rather well studied physical system namely the fluctuating interfaces we present an exact solution for the distribution p h m l of the maximal height h m measured with respect to the average spatial height in the steady state of a fluctuating interface in a one dimensional system of size l with both periodic and free boundary conditions for the periodic case we show that p h m l l 1 2f h m l 1 2 for all l 0 where the function f x is the airy distribution function this result is valid for both the edwards wilkinson ew and the kardar parisi zhang interfaces for the free boundary case the same scaling holds p h m l l 1 2f h m l 1 2 but the scaling function f x is different from that of the periodic case we compute this scaling function explicitly for the ew interface and call it the f airy distribution function numerical simulations are in excellent agreement with our analytical results our results provide a rather rare exactly solvable case for the distribution of extremum of a set of strongly correlated random variables some of these results were announced in a recent letter s n majumdar and a comtet phys rev lett 92 225501 2004

the solution manifold and c1 smoothness for differential equations with state dependent delay abstract let h 0 u c 1 h 0 r n open and f u r n continuously differentiable if f satisfies two mild additional smoothness conditions then the set x u 0 f is a c1 submanifold of codimension n in c 1 h 0 r n the maximal solutions x of the initial value problems x t f x t x 0 x define a continuous semiflow f on x and all operators f t are continuously differentiable their derivatives d2f t are given in the usual way by solutions v to the variational equation along x with segments vt in the tangent spaces tf t x the additional conditions on f are motivated by properties of differential equations with state dependent delay and are verified for an example

extreme values and the level crossing problem an application to the feller process we review the question of the extreme values attained by a random process we relate it to level crossings to one boundary first passage problems as well as to two boundaries escape problems the extremes studied are the maximum the minimum the maximum absolute value and the range or span we specialize in diffusion processes and present detailed results for the wiener and feller processes

extreme value statistics of hierarchically correlated variables deviation from gumbel statistics and anomalous persistence we study analytically the distribution of the minimum of a set of hierarchically correlated random variables e1 e2 ellipsis e n where e i represents the energy of the ith path of a directed polymer on a cayley tree if the variables were uncorrelated the minimum energy would have an asymptotic gumbel distribution we show that due to the hierarchical correlations the forward tail of the distribution of the minimum energy becomes highly nonuniversal depends explicitly on the distribution of the bond energies epsilon and is generically different from the superexponential forward tail of the gumbel distribution the consequence of these results to the persistence of hierarchically correlated random variables is discussed and the persistence is also shown to be generically anomalous

power spectrum and detrended fluctuation analysis application to daily temperatures the variability measures of fluctuation analysis fa and detrended fluctuation analysis dfa are expressed in terms of the power spectral density and of the autocovariance of a given process the diagnostic potential of these methods is tested on several model power spectral densities in particular we find that both fa and dfa reveal an algebraic singularity of the power spectral density at small frequencies corresponding to an algebraic decay of the autocovariance a scaling behavior of the power spectral density in an intermediate frequency regime is better reflected by dfa than by fa we apply fa and dfa to ambient temperature data from the 20th century with the primary goal to resolve the controversy in literature whether the low frequency behavior of the corresponding power spectral densities are better described by a power law or a stretched exponential as a third possible model we suggest a weibull distribution however it turns out that neither fa nor dfa can reliably distinguish between the proposed models

boundary layer phenomena for differential delay equations with state dependent time lags i in this paper we begin a study of the differential delay equation mathtype mtef 2 1 feaafiart1ev1aaatcvaufebsjuyzl2yd9gzlbvynv2caerbulwbln hiov2dgi1btfmbaexatlxbi9gbaerbd9wdylwzybitldharqqtubsr 4rnchbgeagqivu0je9sqqrpepc0xbbl8f4rqqrffpeea0jc9yq jc9 vqaqpepm0xbba9pwe9q8fs0 yqaqpepae9pg0firpeps0he9vr0 vr 0 vqpwqaaeaabigaciaacaqabeaadaqaaqaaaoqaaiabew7aljqadi hagaqbaiaacicacawg0bgaaiykaiabg2da9iabgkhitiaadihacagg oagaamidaiaacmcacqghrawkcawgmbgaaiikaiaadihacaggoagaam idaiabgkhitiaadkhacaggpagaaiykaiaacycacaqggagaaeiiaiaa dkhacqgh9aqpcawgybgaaiikaiaadihacaggoagaamidaiaacmcaca ggpaaaaa 5192 varepsilon x t x t f x t r r r x t we prove the existence of periodic solutions for 0 0 where 0 is an optimal positive number we investigate regularity and monotonicity properties of solutions x t which are defined for all t and of associated functions like t t r x t we begin the development of a poincare bendixson theory and phase plane analysis for such equations in a companion paper these results will be used to investigate the limiting profile and corresponding boundary layer phenomena for periodic solutions as approaches zero

morse decompositions for delay differential equations abstract the attractor for the scalar delay differential equation x t f x t x t 1 is shown to possess a morse decomposition under the hypothesis of a negative feedback condition in the delay term using this decomposition we prove results on the asymptotic rate of oscillation of solutions of the initial value problem existence of both slowly and rapidly oscillating periodic solutions is also proved

statistics of return intervals in long term correlated records we consider long term correlated data with several distribution densities gaussian exponential power law and log normal and various correlation exponents ensuremath gamma 0l ensuremath gamma l1 and study the statistics of the return intervals r j between events above some threshold q we show that irrespective of the distribution the return intervals are long term correlated in the same way as the original record but with additional uncorrelated noise due to this noise the correlations are difficult to observe by the detrended fluctuation analysis which exhibits a crossover behavior but show up very clearly in the autocorrelation function the distribution p q r of the return intervals is characterized at large scales by a stretched exponential with exponent ensuremath gamma and at short scales by a power law with exponent ensuremath gamma ensuremath 1 we discuss in detail the occurrence of finite size effects for large threshold values for all considered distributions we show that finite size effects are most pronounced in exponentially distributed data sets where they can even mask the stretched exponential behavior in records of up to 10 6 data points finally in order to quantify the clustering of extreme events due to the long term correlations in the return intervals we study the conditional distribution function and the related moments we find that they show pronounced memory effects irrespective of the distribution of the original data

maximal height statistics for 1 f alpha signals numerical and analytical results are presented for the maximal relative height distribution of stationary periodic gaussian signals one dimensional interfaces displaying a 1 f alpha power spectrum for 0 1 regime of strong correlations and a highly accurate picture gallery of distribution functions can be constructed numerically analytical results can be obtained in the limit alpha infinity and for large alpha by perturbation expansion furthermore using path integral techniques we derive a trace formula for the distribution function valid for alpha 2n even integer from the latter we extract the small argument asymptote of the distribution function whose analytic continuation to arbitrary alpha 1 is found to be in agreement with simulations comparison of the extreme and roughness statistics of the interfaces reveals similarities in both the small and large argument asymptotes of the distribution functions

the poincare bendixson theorem for a class of delay equations with state dependent delay and monotonic feedback abstract we consider the real valued differential equation x t f x t x t d x t with state dependent delay where f is strictly monotonic in its second argument we describe a class of such equations for which a version of the poincare bendixson theorem holds

statistical properties of volatility return intervals of chinese stocks abstract the statistical properties of the return intervals q between successive 1 min volatilities of 30 liquid chinese stocks exceeding a certain threshold q are carefully studied the kolmogorov smirnov ks test shows that 12 stocks exhibit scaling behaviors in the distributions of q for different thresholds q furthermore the ks test and weighted ks test show that the scaled return interval distributions of 6 stocks out of the 12 stocks can be nicely fitted by a stretched exponential function f e with 0 31 under the significance level of 5 where is the mean return interval the investigation of the conditional probability distribution p q 0 and the mean conditional return interval 0 demonstrates the existence of short term correlation between successive return interval intervals we further study the mean return interval 0 after a cluster of n intervals and the fluctuation f l using detrended fluctuation analysis and find that long term memory also exists in the volatility return intervals

return times for stochastic processes with power law scaling an analytical study of the return time distribution of extreme events for stochastic processes with power law correlation has been carried out the calculation is based on an expansion in the correlation exponent c t ensuremath mid t ensuremath mid ensuremath 1 the fixed point of the theory is associated with stretched exponential scaling of the distribution analytical expressions have been provided in the preasymptotic regime also the permanence time distribution appears to be characterized by stretched exponential scaling the conditions for application of the theory to non gaussian processes have been analyzed and the relations with the issue of return times in the case of multifractal measures have been discussed

recurrence time analysis long term correlations and extreme events the recurrence times between extreme events have been the central point of statistical analyses in many different areas of science simultaneously the poincar e recurrence time has been extensively used to characterize nonlinear dynamical systems we compare the main properties of these statistical methods pointing out their consequences for the recurrence analysis performed in time series in particular we analyze the dependence of the mean recurrence time and of the recurrence time statistics on the probability density function on the interval whereto the recurrences are observed and on the temporal correlations of time series in the case of long term correlations we verify the validity of the stretched exponential distribution which is uniquely defined by the exponent ensuremath gamma at the same time showing that it is restricted to the class of linear long term correlated processes simple transformations are able to modify the correlations of time series leading to stretched exponentials recurrence time statistics with different ensuremath gamma which shows a lack of invariance under the change of observables

order statistics of 1 fα signals order statistics of periodic gaussian noise with 1 f power spectrum is investigated using simulations and phenomenological arguments we find three scaling regimes for the average gap d k x k x k 1 between the kth and k 1 st largest values of the signal the result d k k 1 known for independent identically distributed variables remains valid for 0 5 the spectra of average ordered values e k x 1 x k k is also examined the exponent is derived from the gap scaling as well as by relating e k to the density of near extreme states known results for the density of near extreme states combined with scaling suggest that 2 1 2 4 3 2 and 2 are exact values we also show that parallels can be drawn between e k and the quantum mechanical spectra of a particle in power law potentials

estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse abstract a number of applications require the computation of the trace of a matrix that is implicitly available through a function a common example of a function is the inverse of a large sparse matrix which is the focus of this paper when the evaluation of the function is expensive the task is computationally challenging because the standard approach is based on a monte carlo method which converges slowly we present a different approach that exploits the pattern correlation if present between the diagonal of the inverse of the matrix and the diagonal of some approximate inverse that can be computed inexpensively we leverage various sampling and fitting techniques to fit the diagonal of the approximation to the diagonal of the inverse depending on the quality of the approximate inverse our method may serve as a standalone kernel for providing a fast trace estimate with a small number of samples furthermore the method can be used as a variance reduction method for monte carlo in some cases this is decided dynamically by our algorithm an extensive set of experiments with various technique combinations on several matrices from some real applications demonstrate the potential of our method

testing for homogeneity of variance in time series long memory wavelets and the nile river 1 we consider the problem of testing for homogeneity of variance in a time series with long memorystructure wedemonstratethat atest whose nullhypothesis isdesigned tobewhite noisecan in fact be applied on a scale by scale basis to the discrete wavelet transform of long memory processes in particular we show that evaluating a normalized cumulative sum of squares test statistic using critical levels for the null hypothesis of white noise yields approximately the same null hypothesis rejection rates when applied to the discrete wavelet transform of samples from a fractionally differenced process the point at which the test statistic using a nondecimated version of the discrete wavelet transform achieves its maximum value can be used to estimate the time of the unknown variance change we apply our proposed test statistic on five time series derived from the historical record of nile river yearly minimum water levels covering 622 1922 a d each series exhibiting various degrees of serial correlation including long memory in the longest subseries spanning 622 1284 a d the test confirms an inhomogeneity of variance at short time scales and identifies the change point around 720 a d which coincides closely with the construction of a new device around 715 a d for measuring the nile river the test also detects a change in variance for a record of only 36 years index terms 1829 hydrology groundwater hydrology 1869 hydrology stochastic processes 3299 mathematical geophysics general or miscellaneous

preservation of long range temporal correlations under extreme random dilution abstract many natural time series exhibit long range temporal correlations that may be characterized by power law scaling exponents however in many cases the time series have uneven time intervals due to for example missing data points noisy data and outliers here we study the effect of randomly missing data points on the power law scaling exponents of time series that are long range temporally correlated the fourier transform and detrended fluctuation analysis dfa techniques are used for scaling exponent estimation we find that even under extreme dilution of more than 50 the value of the scaling exponent remains almost unaffected random dilution is also applied on heart interbeat interval time series it is found that dilution of 70 80 of the data points leads to a reduction of only 8 in the scaling exponent it is also found that it is possible to discriminate between healthy and heart failure subjects even under extreme dilution of more than 90

surrogate time series before we apply nonlinear techniques e g those inspired by chaos theory to dynamical phenomena occurring in nature it is necessary to first ask if the use of such advanced techniques is justified by the data while many processes in nature seem very unlikely a priori to be linear the possible nonlinear nature might not be evident in specific aspects of their dynamics the method of surrogate data has become a very popular tool to address such a question however while it was meant to provide a statistically rigorous foolproof framework some limitations and caveats have shown up in its practical use in this paper recent efforts to understand the caveats avoid the pitfalls and to overcome some of the limitations are reviewed and augmented by new material in particular we will discuss specific as well as more general approaches to constrained randomisation providing a full range of examples new algorithms will be introduced for unevenly sampled and multivariate data and for surrogate spike trains the main limitation which lies in the interpretability of the test results will be illustrated through instructive case studies we will also discuss some implementational aspects of the realisation of these methods in the tisean software package

improved surrogate data for nonlinearity tests current tests for nonlinearity compare a time series to the null hypothesis of a gaussian linear stochastic process for this restricted null assumption random surrogates can be constructed which are constrained by the linear properties of the data we propose a more general null hypothesis allowing for nonlinear rescalings of a gaussian linear process we show that such rescalings cannot be accounted for by a simple amplitude adjustment of the surrogates which leads to spurious detection of nonlinearity an iterative algorithm is proposed to make appropriate surrogates which have the same autocorrelations as the data and the same probability distribution

extreme statistics for time series distribution of the maximum relative to the initial value the extreme statistics of time signals is studied when the maximum is measured from the initial value in the case of independent identically distributed iid variables we classify the limiting distribution of the maximum according to the properties of the parent distribution from which the variables are drawn then we turn to correlated periodic gaussian signals with a 1 f ensuremath alpha power spectrum and study the distribution of the maximum relative height with respect to the initial height mathrm mrh mathrm i the exact mathrm mrh mathrm i distribution is derived for ensuremath alpha 0 iid variables ensuremath alpha 2 random walk ensuremath alpha 4 random acceleration and ensuremath alpha ensuremath infty single sinusoidal mode for other intermediate values of ensuremath alpha the distribution is determined from simulations we find that the mathrm mrh mathrm i distribution is markedly different from the previously studied distribution of the maximum height relative to the average height for all ensuremath alpha the two main distinguishing features of the mathrm mrh mathrm i distribution are the much larger weight for small relative heights and the divergence at zero height for ensuremath alpha g3 we also demonstrate that the boundary conditions affect the shape of the distribution by presenting exact results for some nonperiodic boundary conditions finally we show that for signals arising from time translationally invariant distributions the density of near extreme states is the same as the mathrm mrh mathrm i distribution this is used in developing a scaling theory for the threshold singularities of the two distributions

maximal and minimal height distributions of fluctuating interfaces maximal and minimal height distributions mahd mihd of two dimensional interfaces grown with the nonlinear equations of kardar parisi zhang kpz second order and of villain lai das sarma vlds fourth order are shown to be different two universal curves may be mahd or mihd of each class depending on the sign of the relevant nonlinear term which is confirmed by results of several lattice models in the kpz and vlds classes the difference between mahd and midh is connected with the asymmetry of the local height distribution a simple exactly solvable deposition erosion model is introduced to illustrate this feature the average extremal heights scale with the same exponent of the average roughness in contrast to other correlated systems generalized gumbel distributions do not fit those mahd and mihd nor those of edwards wilkinson growth

correlations in economic time series a financial index of the new york stock exchange the sp here f is called the crossover frequency iii we confirm these results using the dfa detrended fluctuation analysis method see fig 2b which allows accurate estimates of exponents independent of local trends

density of near extreme events we provide a quantitative analysis of the phenomenon of crowding of near extreme events by computing exactly the density of states dos near the maximum of a set of independent and identically distributed random variables we show that the mean dos converges to three different limiting forms depending on whether the tail of the distribution of the random variables decays slower than pure exponential faster than pure exponential or as a pure exponential function we argue that some of these results would remain valid even for certain correlated cases and verify it for power law correlated stationary gaussian sequences satisfactory agreement is found between the near maximum crowding in the summer temperature reconstruction data of western siberia and the theoretical prediction

extreme matrices or how an exponential map links classical and free extreme laws using our proposed approach to describe extreme matrices we find an explicit exponentiation formula linking the classical extreme laws of frechet gumbel and weibull given by the fisher tippet gnedenko classification and free extreme laws of free frechet free gumbel and free weibull of ben arous and voiculescu we also develop an extreme random matrix formalism in which refined questions about extreme matrices can be answered in particular we demonstrate explicit calculations for several more or less known random matrix ensembles providing examples of all three free extreme laws finally we present an exact mapping showing the equivalence of free extreme laws to the peak over threshold method in classical probability

estimation of the bilinear form y f a x for hermitian matrices abstract for a hermitian matrix a c p p given vectors x y c p and for suitable functions f the bilinear form y f a x is estimated by extending the extrapolation method proposed by c brezinski in 1999 families of one term and two term estimates e f c and e f n k n k z respectively are derived by extrapolation of the moments of the matrix a for the positive definite case bounds for the optimal value of which leads to an efficient one term estimate in only one matrix vector product are derived for f a a 1 a formula approximating this optimal value of is specified numerical results for several matrix functions and comparisons are provided to demonstrate the effectiveness of the extrapolation method

the use of copula functions for predictive analysis of correlations between extreme storm tides abstract in this paper we present a method used in quantitative description of weakly predictable hydrological extreme events at inland sea investigations for correlations between variations of individual measuring points employing combined statistical methods were carried out as a main tool for this analysis we used a two dimensional copula function sensitive for correlated extreme effects additionally a new proposed methodology based on detrended fluctuations analysis dfa and anomalous diffusion ad was used for the prediction of negative and positive auto correlations and associated optimum choice of copula functions as a practical example we analysed maximum storm tides data recorded at five spatially separated places at the baltic sea for the analysis we used gumbel clayton and frank copula functions and introduced the reversed clayton copula the application of our research model is associated with modelling the risk of high storm tides and possible storm flooding