SMS scnews item created by Anna Aksamit at Tue 14 Jul 2020 1947
Type: Seminar
Distribution: World
Expiry: 28 Jul 2020
Calendar1: 21 Jul 2020 1400-1500
CalLoc1: zoom talk
Auth: (aaks9559) in SMS-WASM

Stochastics and Finance: Samuel Drapeau -- Robust Uncertainty Analysis

Dear All, 

You are kindly invited to attend the next Stochastic and Finance seminar.  On Tuesday
July 21 at 2pm (Sydney time) Samuel Drapeau will give a talk via Zoom.  Zoom link: 

Speaker: Samuel Drapeau (Shanghai Jiao Tong University) 

Title: Robust Uncertainty Analysis 

In this talk, we will showcase how methods from optimal transport and
distributionally robust optimisation allow to capture and quantify sensitivity to model
uncertainty for a large class of problems.  We consider a generic stochastic
optimisation problem.  This could be a mean-variance or a utility maximisation portfolio
allocation problem, a risk measure computation, a standard regression or a deep learning
problem.  At the heart of the optimisation is a probability measure, or a model, which
describes the system.  It could come from data, simulations or a modelling effort for
which there is always exists a degree of uncertainty.  We take a non-parametric approach
and capture model uncertainty using Wasserstein balls around the postulated measure.
Our main results provide explicit formulae for the first order correction to both the
value function and the optimiser.  We further extend our results to optimisation under
linear constraints.  Our sensitivity analysis of the distributionally robust
optimisation problems finds applications in statistics, machine learning, mathematical
finance and uncertainty quantification.  

In the talk, we will discuss several financial examples anchored in a one-step financial
model and compute their sensitivity to model uncertainty.  These include: option
pricing, mean-variance portfolio selection, optimised certainty equivalent and similar
risk assessments.  We will also address briefly some other applications, such as
explicit formulae for first-order approximations of square-root LASSO and square-root
Ridge optimisers and measures of NN architecture robustness wrt to adversarial data.  

This talk is based on joint works with Daniel Bartl, Jan Obloj and Johannes Wiesel. 

Please feel free to forward this message to anyone who might be interested in this

Kind regards, 


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