Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.786155
Title: Adaptive timestepping for SDEs with non-globally Lipschitz drift
Author: Fang, Wei
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
In this thesis, we focus on the numerical approximation of SDEs with a drift which is not globally Lipschitz, and corresponding sensitivity calculations. First, we propose an adaptive timestep construction for an Euler- Maruyama approximation of these SDEs over a finite time interval. It is proved that if the timestep is bounded appropriately, then over a finite time interval the numerical approximation is stable, and the expected number of timesteps is finite. Moreover, we extend this scheme to ergodic SDEs with contractivity over an infinite time interval and prove that the bound for moments and the strong error of the numerical solution are uniform in T, which allow us to introduce the adaptive multilevel Monte Carlo (MLMC) method to further improve the efficiency. Next, we apply a new MLMC method for the ergodic SDEs without contractivity. By introducing a change of measure technique, we simulate the paths with contractivity and add the Radon-Nikodym derivative to the estimator. It is shown that the variance of the new level estimators increases linearly in T, which is a great reduction compared with the exponential increase in the standard MLMC. Lastly, we derive a new pathwise sensitivity estimator for chaotic SDEs by introducing a spring term between the original and perturbed SDEs. The variance of the new estimator increases only linearly in time T; compared with the exponential increase of the standard pathwise estimator. We also consider using this estimator for the SDE with Richardson-Romberg extrapolation on the volatility parameter to approximate the sensitivities of the invariant measure of chaotic ODEs. All of the analysis is supported by numerical results.
Supervisor: Giles, Mike Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.786155  DOI: Not available
Keywords: Mathematics
Share: