A step size control algorithm for the weak approximation of stochastic differential equations

A vriable step size control algorithm for the weak approximation of stochastic differential equations is introduced. The algorithm is based on embedded Runge–Kutta methods which yield two approximations of different orders with a negligible additional computational effort. The difference of these two approximations is used as an estimator for the local error of the less precise approximation. Some numerical results are presented to illustrate the effectiveness of the introduced step size control method.      

Optimal approximation of stochastic differential equations by adaptive step-size control

We study the pathwise (strong) approximation of scalar stochastic differential equations with respect to the global error in the -norm. For equations with additive noise we establish a sharp lower error bound in the class of arbitrary methods that use a fixed number of observations of the driving Brownian motion. As a consequence, higher order methods do not exist if the global error is analyzed. We introduce an adaptive step-size control for the Euler scheme which performs asymptotically optimally. In particular, the new method is more efficient than an equidistant discretization. This superiority is confirmed in simulation experiments for equations with additive noise, as well as for general scalar equations.

     

Continuous weak approximation for stochastic differential equations

A convergence theorem for the continuous weak approximation of the solution of stochastic differential equations (SDEs) by general one-step methods is proved, which is an extension of a theorem due to Milstein. As an application, uniform second order conditions for a class of continuous stochastic Runge–Kutta methods containing the continuous extension of the second order stochastic Runge–Kutta scheme due to Platen are derived. Further, some coefficients for optimal continuous schemes applicable to Itô SDEs with respect to a multi–dimensional Wiener process are presented.     

An adaptive discretization algorithm for the weak approximation of stochastic differential equations

Numerical methods with fixed step size have limitations if they are applied for example to stiff stochastic differential equations where the step size is forced to be very small. In this paper, an adaptive step size control algorithm for the weak approximation of stochastic differential equations is introduced. The proposed algorithm calculates an estimation of the local error in order to determine the optimal step size such that the local error is bounded by some given tolerances. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adaptive weak approximation of stochastic differential equations

Adaptive time‐stepping methods based on the Monte Carlo Euler method for weak approximation of Itô stochastic differential equations are developed. The main result is new expansions of the computational error, with computable leading‐order term in a posteriori form, based on stochastic flows and discrete dual backward problems. The expansions lead to efficient and accurate computation of error estimates. Adaptive algorithms for either stochastic time steps or deterministic time steps are described. Numerical examples illustrate when stochastic and deterministic adaptive time steps are superior to constant time steps and when adaptive stochastic steps are superior to adaptive deterministic steps. Stochastic time steps use Brownian bridges and require more work for a given number of time steps. Deterministic time steps may yield more time steps but require less work; for example, in the limit of vanishing error tolerance, the ratio of the computational error and its computable estimate tends to 1 with negligible additional work to determine the adaptive deterministic time steps. © 2001 John Wiley & Sons, Inc.     

approximation for the solutions of stochastic differential equations. iii. jointly weak convergence

We give sufficient conditions for a family Z, e > 0 of continuous finite variation processes to converge weakly to a diffusion process Z. Then we consider the integral equation dXE(t) = (l)(Xe(t))dZE{t) and the stochastic equation dX{i) = (j)(X{t))dZ{t) and denote by X(t,x,w respectively X{t,x,(jo), the solution starting at x. We prove that PoX~l, e>0 converge weakly to Pol     

An optimization approach to weak approximation of stochastic differential equations with jumps

We propose an optimization approach to weak approximation of stochastic differential equations with jumps. A mathematical programming technique is employed to obtain numerically upper and lower bound estimates of the expectation of interest, where the optimization procedure ends up with a polynomial programming. A major advantage of our approach is that we do not need to simulate sample paths of jump processes, for which few practical simulation techniques exist. We provide numerical results of moment estimations for Doléans-Dade stochastic exponential, truncated stable Lévy processes and Ornstein-Uhlenbeck-type processes to illustrate that our method is able to capture very well the distributional characteristics of stochastic differential equations with jumps.     

On the approximation of stochastic differential equations

The stability properties of stochastic differential equations with respetct to the perturbation of the coefficients and of the driving processes are investigated in the topology of uniform convergence in probability     

Second-order weak approximations for stratonovich stochastic differential equations

Translated from Lietuvos Matematikos Rinkinys, Vol. 34, No. 2, pp. 226–247, April–June, 1994.     

A successive approximation algorithm for stochastic control problems

We consider optimal stochastic control problems in which the state variables are governed by Itô equations. A successive approximation algorithm for optimal stochastic control is obtained. This algorithm, together with the existing numerical methods for parabolic or elliptic PDEs, provides numerical schemes for the solution of Bellman equations.     

On Wong-Zakai approximation of stochastic differential equations

An approximation theorem of stochastic differential equations driven by semimartingales is proved, based on approximation of semimartingales by a sequence of processes with piecewise monotonic sample functions.     

A weak second-order split-step method for numerical simulations of stochastic differential equations

In an analogy from symmetric ordinary differential equation numerical integrators, we derive a three-stage, weak 2nd-order procedure for Monte-Carlo simulations of Itô stochastic differential equations. Our composite procedure splits each time step into three parts: an \(h/2\) -stage of trapezoidal rule, an \(h\) -stage martingale, followed by another \(h/2\) -stage of trapezoidal rule. In \(n\) time steps, an \(h/2\) -stage deterministic step follows another \(n-1\) times. Each of these adjacent pairs may be combined into a single \(h\) -stage, effectively producing a two-stage method with partial overlap between successive time steps.     

On the approximation of stochastic partial differential equations i

The stability of abstract stochastic partial differential equations with respect to the simultaneous perturbation of the driving processes and of the differential operators is investigated. The results obtained here will be applied to concrete stochastic partial differential equations in the continuation of this paper     

An efficient approximation for stochastic differential equations on the partition ofsymmetricalirst

In certain applications of stochastic differential equations a numerical solution must be found corresponding to a particular sample path of the driving process. The order of convergence of approximations based on regular samples of the path is limited, and some approximations are asymptotically efficient in that they minimise the leading coefficient in the expansion of mean-square errors as power series in the sample step size. This paper considers approximations based on irregular samples taken at the passage times of the driving process through a series of thresholds. Such approximations can involve less computation than their regular sample counterparts, particularly for real-time applications. The orders of convergence of the Euler and Milshtein approximations are derived and a new approximation is defined which is asymptotically efficient with respect to the irregular samples. Its asymptotic mean-square error is less than half that of efficient approximations based on regular sample     

A nonparametric variable step-size NLMS algorithm for transversal filters

A nonparametric adaptive filtering approach is proposed in this paper. The algorithm is obtained by exploiting a time-varying step size in the traditional NLMS weight update equation. The step size is adjusted according to the square of a time-averaging estimate of the autocorrelation of a priori and a posteriori error. Therefore, the new algorithm has more effective sense proximity to the optimum solution independent of uncorrelated measurement noise. Moreover, this algorithm has fast convergence at the early stages of adaptation and small final misadjustment at steady-state process. It works reliably and is easy to implement since the update function is nonparametric. Furthermore, the experimental results in system identification applications are presented to illustrate the principle and efficiency of the proposed algorithm.     

Implicit Milstein method for stochastic differential equations via the Wong-Zakai approximation

The aim of this paper is to derive a numerical scheme for solving stochastic differential equations (SDEs) via Wong-Zakai approximation. One of the most important methods for solving SDEs is Milstein method, but this method is not so popular because the cost of simulating the double stochastic integrals is high. For overcoming this complexity, we present an implicit Milstein scheme based on Wong-Zakai approximation by approximating the Brownian motion with its truncated Haar expansion. The main advantages of this method lie in the fact that it preserves the convergence order and also stability region of the Milstein method while its simulation is much easier than Milstein scheme. We show the convergence rate of the method by some numerical examples.