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We study the construction of symplectic Runge-Kutta methods for stochastic Hamiltonian systems (SHS). Three types of systems, SHS with multiplicative noise, special separable Hamiltonians and multiple additive noise, respectively, are considered in this paper. Stochastic Runge-Kutta (SRK) methods for these systems are investigated, and the corresponding conditions for SRK methods to preserve the symplectic property are given. Based on the weak/strong order and symplectic conditions, some effective schemes are derived. In particular, using the algebraic computation, we obtained two classes of high weak order symplectic Runge-Kutta methods for SHS with a single multiplicative noise, and two classes of high strong order symplectic Runge-Kutta methods for SHS with multiple multiplicative and additive noise, respectively. The numerical case studies confirm that the symplectic methods are efficient computational tools for long-term simulations.
In this paper, we investigate the mean-square convergence of the split-step θ-scheme for nonlinear stochastic differential equations with jumps. Under some standard assumptions, we rigorously prove that the strong rate of convergence of the split-step θ-scheme in strong sense is one half. Some numerical experiments are carried out to assert our theoretical result.
We review, implement, and compare numerical integration schemes for spatially bounded
diffusions stopped at the boundary which possess a convergence rate of the
discretization error with respect to the timestep h higher than
. We address specific implementation issues of the most
general-purpose of such schemes. They have been coded into a single Matlab program
and compared, according to their accuracy and computational cost, on a wide range of
problems in up to ℝ48. The paper is self-contained and the code will be
made freely downloadable.
Wahba (1978) and Weinert et al. (1980), using different models, show that an optimal smoothing spline can be thought of as the conditional expectation of a stochastic process observed with noise. This observation leads to efficient computational algorithms. By going back to the Hilbert space formulation of the spline minimization problem, we provide a framework for linking the two different stochastic models. The last part of the paper reviews some new efficient algorithms for spline smoothing.
We describe an Euler scheme to approximate solutions of Lévy driven stochastic differential equations (SDEs) where the grid points are given by the arrival times of a Poisson process and thus are random. This result extends the previous work of Ferreiro-Castilla et al. (2014). We provide a complete numerical analysis of the algorithm to approximate the terminal value of the SDE and prove that the mean-square error converges with rate O(n-1/2). The only requirement of the methodology is to have exact samples from the resolvent of the Lévy process driving the SDE. Classical examples, such as stable processes, subclasses of spectrally one-sided Lévy processes, and new families, such as meromorphic Lévy processes (Kuznetsov et al. (2012), are examples for which our algorithm provides an interesting alternative to existing methods, due to its straightforward implementation and its robustness with respect to the jump structure of the driving Lévy process.
The multi-level Monte Carlo method proposed by Giles (2008) approximates the expectation of some functionals applied to a stochastic process with optimal order of convergence for the mean-square error. In this paper a modified multi-level Monte Carlo estimator is proposed with significantly reduced computational costs. As the main result, it is proved that the modified estimator reduces the computational costs asymptotically by a factor (p / α)2 if weak approximation methods of orders α and p are applied in the case of computational costs growing with the same order as the variances decay.
A numerical comparison of finite difference (FD) and finite element (FE) methods for a stochastic ordinary differential equation is made. The stochastic ordinary differential equation is turned into a set of ordinary differential equations by applying polynomial chaos, and the FD and FE methods are then implemented. The resulting numerical solutions are all non-negative. When orthogonal polynomials are used for either continuous or discrete processes, numerical experiments also show that the FE method is more accurate and efficient than the FD method.
In this paper we propose the asymptotic error distributions of the Euler scheme for a stochastic differential equation driven by Itô semimartingales. Jacod (2004) studied this problem for stochastic differential equations driven by pure jump Lévy processes and obtained quite sharp results. We extend his results to a more general pure jump Itô semimartingale.
The large deviation principle in the small noise limit is derived for solutions of possibly degenerate Itô stochastic differential equations with predictable coefficients, which may also depend on the large deviation parameter. The result is established under mild assumptions using the Dupuis-Ellis weak convergence approach. Applications to certain systems with memory and to positive diffusions with square-root-like dispersion coefficient are included.
Although many studies have addressed the topic of stability versus change in depressive symptoms, few have further decomposed the change to continuous accumulation versus non-systematic state fluctuations or measurement errors. This further step requires a longitudinal follow-up and an appropriate stochastic model; it would, for example, evaluate the hypothesis that women accumulate more susceptibility events than men.
A linear stochastic differential equation model was estimated for a 16-year longitudinal course of depressive symptoms in the Young Finns community sample of 3596 participants (1832 women, 1764 men). This model enabled us to decompose the variance in depression symptoms into a stable trait, cumulative effects and state/error fluctuations.
Women showed higher mean levels and higher variance of depressive symptoms than men. In men, the stable trait accounted for the majority [61%, 90% confidence interval (CI) 48.9–69.2] of the total variance, followed by cumulative effects (23%, 90% CI 9.9–41.7) and state/error fluctuations (16%, 90% CI 5.6–23.2). In women, the cumulative sources were more important than among men and accounted for 44% (90% CI 23.6–58.9) of the variance, followed by stable individual differences (32%, 90% CI 18.5–54.2) and state fluctuations (24%, 90% CI 19.1–27.3).
The results are consistent with previous observations that women suffer more depression than men, and have more variance in depressive symptoms. We also found that continuously accumulating effects are a significant contributor to between-individual differences in depression, especially for women. Although the accumulating effects are often confounded with non-systematic state fluctuations, the latter are unlikely to exceed 27% of the total variance of depressive symptoms.
In this paper, using direct and inverse images for fractional stochastic tangent sets, we
establish the deterministic necessary and sufficient conditions which control that the
solution of a given stochastic differential equation driven by the fractional Brownian
motion evolves in some particular sets K. As a consequence, a comparison
theorem is obtained.
For a given bivariate Lévy process (Ut, Lt)t≥0, distributional properties of the stationary solutions of the stochastic differential equation dVt = Vt-dUt + dLt are analysed. In particular, the expectation and autocorrelation function are obtained in terms of the process (U, L) and in several cases of interest the tail behavior is described. In the case where U has jumps of size −1, necessary and sufficient conditions for the law of the solutions to be (absolutely) continuous are given.
We consider a stochastic differential equation (SDE) with piecewise linear drift driven by a spectrally one-sided Lévy process. We show that this SDE has some connections with queueing and storage models, and we use this observation to obtain the invariant distribution.
Jakeman's random walk model with step number fluctuations describes the coherent amplitude scattered from a rough medium in terms of the summation of individual scatterers' contributions. If the scattering population conforms to a birth-death immigration model, the resulting amplitude is K-distributed. In this context, we derive a class of diffusion processes as an extension of the ordinary birth-death immigration model. We show how this class encompasses four different cross-section models commonly studied in the literature. We conclude by discussing the advantages of this unified description.
This paper is concerned with the numerical approximations of semi-linear stochastic partial differential equations of elliptic type in multi-dimensions. Convergence analysis and error estimates are presented for the numerical solutions based on the spectral method. Numerical results demonstrate the good performance of the spectral method.
We present conditions that imply the conditional full support (CFS) property, introduced in Guasoni, Rásonyi and Schachermayer (2008), for processes Z := H + ∫K dW, where W is a Brownian motion, H is a continuous process, and processes H and K are either progressive or independent of W. Moreover, in the latter case, under an additional assumption that K is of finite variation, we present conditions under which Z has CFS also when W is replaced with a general continuous process with CFS. As applications of these results, we show that several stochastic volatility models and the solutions of certain stochastic differential equations have CFS.
The dynamics of dendritic growth of a crystal in an undercooled melt is
determined by macroscopic diffusion-convection of heat and by capillary forces
acting on the nanometer scale of the solid-liquid interface width.
Its modelling is useful for instance in processing techniques based on casting.
The phase-field method is widely used to study evolution of such microstructural phase transformations on
a continuum level; it couples the energy equation to a phenomenological Allen-Cahn/Ginzburg-Landau
equation modelling the dynamics of an order parameter determining the solid and liquid phases,
including also stochastic fluctuations to obtain the qualitatively correct
result of dendritic side branching.
This work presents a method to determine stochastic phase-field models from atomistic
formulations by coarse-graining molecular dynamics. It has
(1) a precise
quantitative atomistic definition of the phase-field variable, based on the local
(2) derivation of its coarse-grained
dynamics model, from microscopic Smoluchowski molecular dynamics (that is Brownian or over damped Langevin dynamics);
(3) numerical computation of the coarse-grained model functions.
The coarse-grained model approximates Gibbs ensemble averages of the atomistic phase-field, by
choosing coarse-grained drift and diffusion functions that minimize the approximation error of observables in this
Using key tools such as Itô's formula for general semimartingales, Kunita's moment estimates for Lévy-type stochastic integrals, and the exponential martingale inequality, we find conditions under which the solutions to the stochastic differential equations (SDEs) driven by Lévy noise are stable in probability, almost surely and moment exponentially stable.
We consider the operation of an insurer with a large initial surplus x>0. The insurer's surplus process S(t) (with S(0)=x) evolves in the range S(t)≥ 0 as a generalized renewal process with positive mean drift and with jumps at time epochs T1,T2,…. At the time Tη(x) when the process S(t) first becomes negative, the insurer's ruin (in the ‘classical’ sense) occurs, but the insurer can borrow money via a line of credit. After this moment the process S(t) behaves as a solution to a certain stochastic differential equation which, in general, depends on the indebtedness, -S(t). This behavior of S(t) lasts until the time θ(x,y) at which the indebtedness reaches some ‘critical’ level y>0. At this moment the line of credit will be closed and the insurer's absolute ruin occurs with deficit -S(θ(x,y)). We find the asymptotics of the absolute ruin probability and the limiting distributions of η(x), θ(x,y), and -S(θ(x,y)) as x → ∞, assuming that the claim distribution is regularly varying. The second-order approximation to the absolute ruin probability is also obtained. The abovementioned results are obtained by using limiting theorems for the joint distribution of η(x) and -S(Tη(x)).