Strong order-one convergence of the Euler method for random ordinary differential equations driven by semi-martingale noises
Peter E. Kloeden, Ricardo M. S. Rosa

TL;DR
This paper proves that the Euler method for random ODEs driven by semi-martingale noises achieves strong order 1 convergence in many typical cases, extending previous results and including various types of stochastic processes.
Contribution
It establishes strong order 1 convergence for the Euler method under semi-martingale noise, broadening the class of stochastic processes with optimal convergence results.
Findings
Euler method attains strong order 1 convergence for semi-martingale noises.
Numerical simulations confirm the optimality of the convergence order.
Example with fractional Brownian motion shows lower convergence order, highlighting the semi-martingale case's advantage.
Abstract
It is well known that the Euler method for a random ordinary differential equation driven by a stochastic process with -H\"older sample paths is estimated to be of strong order with respect to the time step, provided is sufficiently regular and with suitable bounds. This order is known to increase to in some special cases. Here, it is proved that, in many more typical cases, further structures on the noise can be exploited so that the strong convergence is of order 1. In fact, we prove so for any semi-martingale noise. This includes It\^o diffusion processes, point-process noises, transport-type processes with sample paths of bounded variation, and time-changed Brownian motion. The result follows from estimating the global error as an iterated integral over both large and small mesh scales, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and financial applications · Financial Risk and Volatility Modeling · Meteorological Phenomena and Simulations
