Mathematical Finance, Stochastic Analysis, and Machine Learning Seminar by Konstantinos Dareiotis: Approximation of Stochastic Equations with Irregular Drifts

Speaker:

Konstantinos Dareiotis, University of Leeds, UK

 

Title:

Approximation of Stochastic Equations with Irregular Drifts 

 

Abstract:

In this talk we will discuss the rate of convergence of the Euler scheme for stochastic differential equations with irregular drifts. Our approach relies on regularisation-by-noise techniques and more specifically, on the recently developed stochastic sewing lemma. The advantages of this approach are numerous and include the derivation of improved (optimal) rates and the treatment of non-Markovian settings. We will consider drifts in Hölder and Sobolev classes, but also merely bounded and measurable. The latter is the first and at the same time optimal quantification of a convergence theorem of Gyöngy and Krylov. This talk is based on joint works with Oleg Butkovsky, Khoa Lê, and Máté Gerencsér.

 

Note:Face coverings will be required. Even if you are fully vaccinated, all students, staff, faculty, and guests must wear a face covering indoors. The university will review and revise the mask protocol as appropriate given changes to state and city public-health guidelines.

 

Mathematical Finance, Stochastic Analysis, and Machine Learning Seminar