Continuous Time Random Walk Formalism Research Articles

Continuous-Time Random Walk with multi-step memory: an application to market dynamics

An extended version of the Continuous-Time Random Walk (CTRW) model with memory is herein developed. This memory involves the dependence between arbitrary number of successive jumps of the process while waiting times between jumps are considered as i.i.d. random variables. This dependence was established analyzing empirical histograms for the stochastic process of a single share price on a market within the high frequency time scale. Then, it was justified theoretically by considering bid-ask bounce mechanism containing some delay characteristic for any double-auction market. Our model appeared exactly analytically solvable. Therefore, it enables a direct comparison of its predictions with their empirical counterparts, for instance, with empirical velocity autocorrelation function. Thus, the present research significantly extends capabilities of the CTRW formalism.

Generalized run-and-turn motions: From bacteria to Lévy walks.

Swimming bacteria exhibit a repertoire of motility patterns, in which persistent motion is interrupted by turning events. What are the statistical properties of such random walks? If some particular instances have long been studied, the general case where turning times do not follow a Poisson process has remained unsolved. We present a generic extension of the continuous time random walks formalism relying on operators and noncommutative calculus. The approach is first applied to a unimodal model of bacterial motion. We examine the existence of a minimum in velocity correlation function and discuss the maximum of diffusivity at an optimal value of rotational diffusion. The model is then extended to bimodal patterns and includes as particular cases all swimming strategies: run-and-tumble, run-stop, run-reverse and run-reverse-flick. We characterize their velocity correlation functions and investigate how bimodality affects diffusivity. Finally, the wider applicability of the method is illustrated by considering curved trajectories and Lévy walks. Our results are relevant for intermittent motion of living beings, be they swimming micro-organisms or crawling cells.

Many facets of intermittent dynamics in colloidal and molecular glasses

A popular picture of the intermittent dynamics of glassy materials depicts each particle as localized in a temporary cage formed by its neighbors, and seldom jumping to another cage. Drawing on experiments on hard sphere colloidal glasses and molecular dynamic simulations of supercooled liquids, here we show how this simplified scenario leads to quantitative relations between single particle motion and macroscopic properties, within a Continuous Time Random Walk (CTRW) framework. For example, the diffusivity and the relaxation time at different length scales can be directly predicted from the jump properties. In the accessible range of temperatures and volume fractions, these predictions work well, although the CTRW formalism neglects the spatially heterogeneous dynamics of glassy systems. In this respect, we show how these dynamic heterogeneities can be highlighted from a study of the correlations between jumps. Finally, the intermittent motion is exploited to infer correlations between local structure and dynamics.

Cattaneo-type subdiffusion-reaction equation.

Subdiffusion in a system in which mobile particles A can chemically react with static particles B according to the rule A+B→B is considered within a persistent random-walk model. This model, which assumes a correlation between successive steps of particles, provides hyperbolic Cattaneo normal diffusion or fractional subdiffusion equations. Starting with the difference equation, which describes a persistent random walk in a system with chemical reactions, using the generating function method and the continuous-time random-walk formalism, we will derive the Cattaneo-type subdiffusion differential equation with fractional time derivatives in which the chemical reactions mentioned above are taken into account. We will also find its solution over a long time limit. Based on the obtained results, we will find the Cattaneo-type subdiffusion-reaction equation in the case in which mobile particles of species A and B can chemically react according to a more complicated rule.

How coupled elementary units determine the dynamics of macroscopic glass-forming systems

We investigate the dynamics of a binary mixture Lennard-Jones system of different system sizes with respect to the importance of the properties of the underlying potential energy landscape (PEL). We show that the dynamics of small systems can be very well described within the continuous time random walk formalism, which is determined solely by PEL parameters. Finite size analysis shows that the diffusivity of large and small systems are very similar. This suggests that the PEL parameters of the small system also determine the local dynamics in large systems. The structural relaxation time, however, displays significant finite size effects. Furthermore, using a nonequilibrium configuration of a large system, we find that causal connections exist between nearby regions of the system. These findings can be described by the coupled landscape model for which a macroscopic system is described by a superposition of elementary systems, each described by its PEL. A minimum coupling is introduced which accounts for the finite size behavior. The coupling strength, as the single adjustable parameter, becomes smaller closer to the glass transition.

Conciliating the nonadditive entropy approach and the fractional model formulation when describing subdiffusion

AbstractWe consider here two different models describing subdiffusion. One of them is derived from Continuous Time Random Walk formalism and utilizes a subdiffusion equation with a fractional time derivative. The second model is based on Sharma-Mittal nonadditive entropy formalism where the subdiffusive process is described by a nonlinear equation with ordinary derivatives. Using these two models we describe the process of a substance released from a thick membrane and we find functions which determine the time evolution of the amount of substance remaining inside this membrane. We then find ‘the agreement conditions’ under which these two models provide the same relation defining subdiffusion and give the same function characterizing the process of the released substance. These agreement conditions enable us to determine the relation between the parameters occuring in both models.

Model for interevent times with long tails and multifractality in human communications: An application to financial trading

Social, technological, and economic time series are divided by events which are usually assumed to be random, albeit with some hierarchical structure. It is well known that the interevent statistics observed in these contexts differs from the Poissonian profile by being long-tailed distributed with resting and active periods interwoven. Understanding mechanisms generating consistent statistics has therefore become a central issue. The approach we present is taken from the continuous-time random-walk formalism and represents an analytical alternative to models of nontrivial priority that have been recently proposed. Our analysis also goes one step further by looking at the multifractal structure of the interevent times of human decisions. We here analyze the intertransaction time intervals of several financial markets. We observe that empirical data describe a subtle multifractal behavior. Our model explains this structure by taking the pausing-time density in the form of a superstatistics where the integral kernel quantifies the heterogeneous nature of the executed tasks. A stretched exponential kernel provides a multifractal profile valid for a certain limited range. A suggested heuristic analytical profile is capable of covering a broader region.

Financial Data Analysis by means of Coupled Continuous-Time Random Walk in Rachev-Rűschendorf Model

We adapt the continuous-time random walk formalism to describe assetprice evolution. We expand the idea proposed by Rachev and Rusc˜ hendorfwho analyzed the binomial pricing model in the discrete time with random-ization of the number of price changes. As a result, in the framework of theproposed model we obtain a mixture of the Gaussian and a generalized arc-sine laws as the limiting distribution of log-returns. Moreover, we derive anEuropean-call-option price that is an extension of the Black{Scholes formula.We apply the obtained theoretical results to model actual flnancial data andtry to show that the continuous-time random walk ofiers alternative tools todeal with several complex issues of flnancial markets.PACS numbers: 89.65.Gh, 05.40.Fb

Fluorescence Recovery after Photobleaching: The Case of Anomalous Diffusion

The method of FRAP (fluorescence recovery after photobleaching), which has been broadly used to measure lateral mobility of fluorescent-labeled molecules in cell membranes, is formulated here in terms of continuous time random walks (CTRWs), which offer both analytical expressions and a scheme for numerical simulations. We propose an approach based on the CTRW and the corresponding fractional diffusion equation (FDE) to analyze FRAP results in the presence of anomalous subdiffusion. The FDE generalizes the simple diffusive picture, which has been applied to FRAP when assuming regular diffusion, to account for subdiffusion. We use a subordination relationship between the solutions of the fractional and normal diffusion equations to fit FRAP recovery curves obtained from CTRW simulations, and compare the fits to the commonly used approach based on the simple diffusion equation with a time dependent diffusion coefficient (TDDC). The CTRW and TDDC describe two different dynamical schemes, and although the CTRW formalism appears to be more complicated, it provides a physical description that underlies anomalous lateral diffusion.

A Model for Interevent Times with Long Tails and Multifractality in Human Communications: An Application to Financial Trading

Social, technological and economic time series are divided by events which are usually assumed to be random albeit with some hierarchical structure. It is well known that the interevent statistics observed in these contexts differs from the Poissonian profile by being long-tailed distributed with resting and active periods interwoven. Understanding mechanisms generating consistent statistics have therefore become a central issue. The approach we present is taken from the Continuous Time Random Walk formalism and represents an analytical alternative to models of non-trivial priority that have been recently proposed. Our analysis also goes one step further by looking at the multifractal structure of the interevent times of human decisions. We here analyze the inter-transaction time intervals of several financial markets. We observe that empirical data describes a subtle multifractal behavior. Our model explains this structure by taking the pausing-time density in the form of a superstatistics where the integral kernel quantifies the heterogeneous nature of the executed tasks. An stretched exponential kernel provides a multifractal profile valid for a certain limited range. A suggested heuristic analytical profile is capable of covering a broader region.

Mean exit time and survival probability within the CTRW formalism

An intense research on financial market microstructure is presently in progress. Continuous time random walks (CTRWs) are general models capable to capture the small-scale properties that high frequency data series show. The use of CTRW models in the analysis of financial problems is quite recent and their potentials have not been fully developed. Here we present two (closely related) applications of great interest in risk control. In the first place, we will review the problem of modelling the behaviour of the mean exit time (MET) of a process out of a given region of fixed size. The surveyed stochastic processes are the cumulative returns of asset prices. The link between the value of the MET and the timescale of the market fluctuations of a certain degree is crystal clear. In this sense, MET value may help, for instance, in deciding the optimal time horizon for the investment. The MET is, however, one among the statistics of a distribution of bigger interest: the survival probability (SP), the likelihood that after some lapse of time a process remains inside the given region without having crossed its boundaries. The final part of the manuscript is devoted to the study of this quantity. Note that the use of SPs may outperform the standard “Value at Risk (VaR) method for two reasons: we can consider other market dynamics than the limited Wiener process and, even in this case, a risk level derived from the SP will ensure (within the desired quintile) that the quoted value of the portfolio will not leave the safety zone. We present some preliminary theoretical and applied results concerning this topic.

Kinetic equations for reaction-subdiffusion systems: Derivation and stability analysis

We derive general kinetic equations for reacting and subdiffusing entities based on a nonlinear continuous time random walk formalism proposed by Vlad and Ross [Phys. Rev. E 66, 061908 (2002)]. Reaction and diffusion processes are separable in a typical reaction-diffusion system, and their combined influence on the evolution of the density of a species is a simple sum. Our derivation shows that this is no longer true for subdiffusive entities undergoing reactions. The strong memory effects in the transport process, i.e., the non-Markovian nature of subdiffusion, results in a nontrivial combination of reactions and spatial dispersal, which we discuss in detail. We carry out a linear stability analysis of the derived reaction-subdiffusion system to understand the effects of memory on pattern formation. We find that the Turing instability persists in the subdiffusive system. However, the memory modifies the Turing threshold and the characteristics of the band of unstable modes close to this threshold.

The continuous time random walk formalism in financial markets

We adapt continuous time random walk (CTRW) formalism to describe asset price evolution and discuss some of the problems that can be treated using this approach. We basically focus on two aspects: (i) the derivation of the price distribution from high-frequency data, and (ii) the inverse problem, obtaining information on the market microstructure as reflected by high-frequency data knowing only the daily volatility. We apply the formalism to financial data to show that the CTRW offers alternative tools to deal with several complex issues of financial markets.

Mean Exit Time and Survival Probability within the CTRW Formalism

An intense research on financial market microstructure is presently in progress. Continuous time random walks (CTRWs) are general models capable to capture the small-scale properties that high frequency data series show. The use of CTRW models in the analysis of financial problems is quite recent and their potentials have not been fully developed. Here we present two (closely related) applications of great interest in risk control. In the first place, we will review the problem of modelling the behaviour of the mean exit time (MET) of a process out of a given region of fixed size. The surveyed stochastic processes are the cumulative returns of asset prices. The link between the value of the MET and the timescale of the market fluctuations of a certain degree is crystal clear. In this sense, MET value may help, for instance, in deciding the optimal time horizon for the investment. The MET is, however, one among the statistics of a distribution of bigger interest: the survival probability (SP), the likelihood that after some lapse of time a process remains inside the given region without having crossed its boundaries. The final part of the article is devoted to the study of this quantity. Note that the use of SPs may outperform the standard Value at Risk (VaR) method for two reasons: we can consider other market dynamics than the limited Wiener process and, even in this case, a risk level derived from the SP will ensure (within the desired quintile) that the quoted value of the portfolio will not leave the safety zone. We present some preliminary theoretical and applied results concerning this topic.

The foundations of diffusion revisited

Diffusion is essentially the macroscopic manifestation of random (Brownian) microscopic motion. This idea has been generalized in the continuous time random walk formalism, which under quite general conditions leads to a generalized master equation (GME) that provides a useful modelling framework for transport. Here we review some of the basic ideas underlying this formalism from the perspective of transport in (magnetic confinement) plasmas.Under some specific conditions, the fluid limit of the GME corresponds to the Fokker–Planck (FP) diffusion equation in inhomogeneous systems, which reduces to Fick's law when the system is homogeneous. It is suggested that the FP equation may be preferable in fusion plasmas due to the inhomogeneity of the system, which would imply that part of the observed inward convection (‘pinch’) can be ascribed to this inhomogeneity.The GME also permits a mathematically sound approach to more complex transport issues, such as the incorporation of critical gradients and non-local transport mechanisms. A toy model incorporating these ingredients was shown to possess behaviour that bears a striking similarity to certain unusual phenomena observed in fusion plasmas.

Morphogen gradient formation in a complex environment: An anomalous diffusion model

Current models of morphogen-induced patterning assume that morphogens undergo normal, or Fickian, diffusion, although the validity of this assumption has never been examined. Here we argue that the interaction of morphogens with the complex extracellular surrounding may lead to anomalous diffusion. We present a phenomenological model that captures this interaction, and derive the properties of the morphogen profile under conditions of anomalous (non-Fickian) diffusion. In this context we consider the continuous time random walk formalism and extend its application to account for degradation of morphogen particles. We show that within the anomalous diffusion model, morphogen profiles are fundamentally distinct from the corresponding Fickian profiles. Differences were found in several key aspects, including the role of degradation in determining the profile, the rate by which it spreads in time and its long-term behavior. We analyze the effect of an abrupt change in the extracellular environment on the concentration profiles. Furthermore, we discuss the robustness of the morphogen distribution to fluctuations in morphogen production rate, and describe a feedback mechanism that can buffer such fluctuations. Our study also provides rigorous criteria to distinguish experimentally between Fickian and anomalous modes of morphogen transport.

Signatures of non-Markovian turbulent transport in reversed field pinch plasmas

Transport of field lines is studied for a realistic model of magnetic field configuration in a Reversed Field Pinch. It is shown that transport is anomalous, i.e., it cannot be described within the standard diffusive paradigm. To fit numerical results we present a transport model based upon the Continuous Time Random Walk formalism. Fairly good quantitative agreement appears for exponential memory functions.

Extreme times in financial markets

We apply the theory of continuous time random walks (CTRWs) to study some aspects involving extreme events in financial time series. We focus our attention on the mean exit time (MET). We derive a general equation for this average and compare it with empirical results coming from high-frequency data of the U.S. dollar and Deutsche mark futures market. The empirical MET follows a quadratic law in the return length interval which is consistent with the CTRW formalism.

Autocorrelation functions for phase separation in ternary mixtures

We present numerical and analytical results for the autocorrelation functions which characterize domain growth in ternary mixtures. The numerical results are obtained from Monte Carlo simulations of the spin-1 Blume-Emery-Griffiths model with spin-exchange kinetics. Further, we model the autocorrelation functions using an approach based on the continuous-time random walk formalism. The aging property of these functions is related to the time dependence of the domain-size distribution. Our analytical results are found to be in good agreement with the numerical data.

Remarks on the possible universal mechanism of the non-linear long-term autocorrelations in financial time-series

The paper consists of two parts: (i) the empirical one where the non-linear, long-term autocorrelations present in high-frequency data extracting from the Warsaw Stock Exchange were analyzed and (ii) theoretical one where predictions of our model (Quantitative Finance 3 (2003) 201; Physica A (2003); Chem. Phys. 284 (2002) 481; Phys. Comm. 147 (2002) 565; Physica A 264 (1999) 84; Physica A 264 (1999) 107; Lecture Notes in Computer Science 2657 (2003) 407; Eur. Phys. J. B 33 (2003) 495) were shown and discussed. This model introduces the possibility that the Weierstrass (hierarchical) random walk can be occasionally intermitted by momentary localizations; the localizations themselves are again described by the Weierstrass process. In other words, this combined walk is a kind of the non-separable, generalized continuous-time random walk formalism. To adapt the model to the description of empirical data recorded at time horizon Δ t = 1 min , we applied a discretization procedure into the continuous-time series produced by the model. We observed that such a procedure generates the non-linear, long-term autocorrelations even in the Gaussian regime, as turning points of the random walk trajectory are, most often, incommensurable with discretization time-step. These autocorrelations appear to be similar to those observed in the financial time series (Physica A 287 (2000) 396; Physica A 299 (2001) 1; Physica A 299 (2001) 16; Physica A 299 (2001) 28), although single steps of the walker within continuous time are, by definition, uncorrelated. Our approach suggests a surprising origin of the non-linear, long-term autocorrelations alternative to the one proposed very recently (cf. Phys. Rev. E 67 (2003) 021112 and refs. therein) although both approaches involve related variants of the well-known CTRW formalism applied in yet many different branches of knowledge (Phys. Rep. 158 (1987) 263; Phys. Rep. 195 (1990) 127; in: A. Bunde, S. Havlin (Eds.), Fractals in Science, Springer, Berlin, 1995, pp. 119–161).