Continuous Time Random Walk Limit Research Articles

Time-fractional Caputo derivative versus other integrodifferential operators in generalized Fokker-Planck and generalized Langevin equations.

Fractional diffusion and Fokker-Planck equationsare widely used tools to describe anomalous diffusion in a large variety of complex systems. The equivalent formulations in terms of Caputo or Riemann-Liouville fractional derivatives can be derived as continuum limits of continuous-time random walks and are associated with the Mittag-Leffler relaxation of Fourier modes, interpolating between a short-time stretched exponential and a long-time inverse power-law scaling. More recently, a number of other integrodifferential operators have been proposed, including the Caputo-Fabrizio and Atangana-Baleanu forms. Moreover, the conformable derivative has been introduced. We study here the dynamics of the associated generalized Fokker-Planck equationsfrom the perspective of the moments, the time-averaged mean-squared displacements, and the autocovariance functions. We also study generalized Langevin equationsbased on these generalized operators. The differences between the Fokker-Planck and Langevin equations with different integrodifferential operators are discussed and compared with the dynamic behavior of established models of scaled Brownian motion and fractional Brownian motion. We demonstrate that the integrodifferential operators with exponential and Mittag-Leffler kernels are not suitable to be introduced to Fokker-Planck and Langevin equationsfor the physically relevant diffusion scenarios discussed in our paper. The conformable and Caputo Langevin equationsare unveiled to share similar properties with scaled and fractional Brownian motion, respectively.

Anomalous Diffusions in Option Prices: Connecting Trade Duration and the Volatility Term Structure

Anomalous diffusions arise as scaling limits of continuous-time random walks (CTRWs) whose innovation times are distributed according to a power law. The impact of a non-exponential waiting time does not vanish with time and leads to different distribution spread rates compared to standard models. In financial modelling this has been used to accommodate for random trade duration in the tick-by-tick price process. We show here that anomalous diffusions are able to reproduce the market behaviour of the implied volatility more consistently than usual Levy or stochastic volatility models.We focus on two distinct classes of underlying asset models, one with independent price innovations and waiting times, and one allowing dependence between these two components. These two models capture the well-known paradigm according to which shorter trade duration is associated with higher return impact of individual trades. We fully describe these processes in a semimartingale setting leading no-arbitrage pricing formulae, and study their statistical properties. We observe that skewness and kurtosis of the asset returns do not tend to zero as time goes by. We also characterize the large-maturity asymptotics of Call option prices, and find that the convergence rate is slower than in standard Levy regimes, which in turn yields a declining implied volatility term structure and a slower decay of the skew.

Anomalous Diffusions in Option Prices: Connecting Trade Duration and the Volatility Term Structure

Anomalous diffusions arise as scaling limits of continuous-time random walks whose innovation times are distributed according to a power law. The impact of a nonexponential waiting time does not va...

Finite dimensional Fokker–Planck equations for continuous time random walk limits

Continuous Time Random Walk (CTRW) is a model where particle’s jumps in space are coupled with waiting times before each jump. A Continuous Time Random Walk Limit (CTRWL) is obtained by a limit procedure on a CTRW and can be used to model anomalous diffusion. The distribution p(dx,t) of a CTRWL Xt satisfies a Fractional Fokker–Planck Equation (FFPE). Since CTRWLs are usually not Markovian, their one dimensional FFPE is not enough to completely determine them. In this paper we find the FFPEs of the distribution of Xt at multiple times, i.e. the distribution of the random vector (Xt1,…,Xtn) for t1<⋯<tn for a large class of CTRWLs. This allows us to define CTRWLs by their finite dimensional FFPEs.

A Semi-Markov Algorithm for Continuous Time Random Walk Limit Distributions

The Semi-Markov property of Continuous Time Random Walks (CTRWs) and their limit processes is utilized, and the probability distributions of the bivariate Markov process $(X(t),V(t))$ are calculated: $X(t)$ is a CTRW limit and $V(t)$ a process tracking the age, i.e. the time since the last jump. For a given CTRW limit process $X(t)$, a sequence of discrete CTRWs in discrete time is given which converges to $X(t)$ (weakly in the Skorokhod topology). Master equations for the discrete CTRWs are implemented numerically, thus approximating the distribution of $X(t)$. A consequence of the derived algorithm is that any distribution of initial age can be assumed as an initial condition for the CTRW limit dynamics. Four examples with different temporal scaling are discussed: subdiffusion, tempered subdiffusion, the fractal mobile/immobile model and the tempered fractal mobile/immobile model.

Semi-Markov approach to continuous time random walk limit processes

Continuous time random walks (CTRWs) are versatile models for anomalous diffusion processes that have found widespread application in the quantitative sciences. Their scaling limits are typically non-Markovian, and the computation of their finite-dimensional distributions is an important open problem. This paper develops a general semi-Markov theory for CTRW limit processes in $\mathbb{R}^d$ with infinitely many particle jumps (renewals) in finite time intervals. The particle jumps and waiting times can be coupled and vary with space and time. By augmenting the state space to include the scaling limits of renewal times, a CTRW limit process can be embedded in a Markov process. Explicit analytic expressions for the transition kernels of these Markov processes are then derived, which allow the computation of all finite dimensional distributions for CTRW limits. Two examples illustrate the proposed method.

Limit theorems of continuous-time random walks with tails

We study the functional limits of continuous-time random walks (CTRWs) with tails under certain conditions. We find that the scaled CTRWs with tails converge weakly to an α-stable Levy process in D([0, 1]) with M 1-topology but the corresponding scaled CTRWs converge weakly to the same limit in D([0, 1]) with J 1-topology.

FRACTIONAL DYNAMICS AT MULTIPLE TIMES.

A continuous time random walk (CTRW) imposes a random waiting time between random particle jumps. CTRW limit densities solve a fractional Fokker-Planck equation, but since the CTRW limit is not Markovian, this is not sufficient to characterize the process. This paper applies continuum renewal theory to restore the Markov property on an expanded state space, and compute the joint CTRW limit density at multiple times.

Triangular array limits for continuous time random walks

A continuous time random walk (CTRW) is a random walk subordinated to a renewal process, used in physics to model anomalous diffusion. Transition densities of CTRW scaling limits solve fractional diffusion equations. This paper develops more general limit theorems, based on triangular arrays, for sequences of CTRW processes. The array elements consist of random vectors that incorporate both the random walk jump variable and the waiting time preceding that jump. The CTRW limit process consists of a vector-valued Lévy process whose time parameter is replaced by the hitting time process of a real-valued nondecreasing Lévy process (subordinator). We provide a formula for the distribution of the CTRW limit process and show that their densities solve abstract space–time diffusion equations. Applications to finance are discussed, and a density formula for the hitting time of any strictly increasing subordinator is developed.

On multiple‐particle continuous‐time random walks

Scaling limits of continuous‐time random walks are used in physics to model anomalous diffusion in which particles spread at a different rate than the classical Brownian motion. In this paper, we characterize the scaling limit of the average of multiple particles, independently moving as a continuous‐time random walk. The limit is taken by increasing the number of particles and scaling from microscopic to macroscopic view. We show that the limit is independent of the order of these limiting procedures and can also be taken simultaneously in both procedures. Whereas the scaling limit of a single‐particle movement has quite an obscure behavior, the multiple‐particle analogue has much nicer properties.