An adaptive time-stepping method based on a posteriori weak error analysis for large SDE systems

We develop an adaptive algorithm for large SDE systems, which automatically selects (quasi-)deterministic time steps for the semi-implicit Euler method, based on an a posteriori weak error estimate. Main tools to construct the a posteriori estimator are the representation of the weak approximation error via Kolmogorov’s backward equation, a priori bounds for its solution and the Clark–Ocone formula. For a certain class of SDE systems, we validate optimal weak convergence order 1 of the a posteriori estimator, and termination of the adaptive method based on it within $${{\mathcal {O}}}(\mathtt{Tol}^{-1})$$ O ( Tol - 1 ) steps.

The Weak Convergence Rate of Two Semi-Exact Discretization Schemes for the Heston Model

Inspired by the article Weak Convergence Rate of a Time-Discrete Scheme for the Heston Stochastic Volatility Model, Chao Zheng, SIAM Journal on Numerical Analysis 2017, 55:3, 1243–1263, we studied the weak error of discretization schemes for the Heston model, which are based on exact simulation of the underlying volatility process. Both for an Euler- and a trapezoidal-type scheme for the log-asset price, we established weak order one for smooth payoffs without any assumptions on the Feller index of the volatility process. In our analysis, we also observed the usual trade off between the smoothness assumption on the payoff and the restriction on the Feller index. Moreover, we provided error expansions, which could be used to construct second order schemes via extrapolation. In this paper, we illustrate our theoretical findings by several numerical examples.

Weak and strong error analysis of recursive quantization: a general approach with an application to jump diffusions

Observing that the recent developments of spatial discretization schemes based on recursive (product) quantization can be applied to a wide family of discrete time Markov chains, including all standard time discretization schemes of diffusion processes, we establish in this paper a generic strong error bound for such quantized schemes under a Lipschitz propagation assumption. We also establish a marginal weak error estimate that is entirely new to our best knowledge. As an illustration of their generality, we show how to recursively quantize the Euler scheme of a jump diffusion process, including details on the algorithmic aspects grid computation, transition weight computation, etc. Finally, we test the performances of the recursive quantization algorithm by pricing a European put option in a jump Merton model.

Thinning and multilevel Monte Carlo methods for piecewise deterministic (Markov) processes with an application to a stochastic Morris–Lecar model

In the first part of this paper we study approximations of trajectories of piecewise deterministic processes (PDPs) when the flow is not given explicitly by the thinning method. We also establish a strong error estimate for PDPs as well as a weak error expansion for piecewise deterministic Markov processes (PDMPs). These estimates are the building blocks of the multilevel Monte Carlo (MLMC) method, which we study in the second part. The coupling required by the MLMC is based on the thinning procedure. In the third part we apply these results to a two-dimensional Morris–Lecar model with stochastic ion channels. In the range of our simulations the MLMC estimator outperforms classical Monte Carlo.