# Sensitivity of robust optimization problems under drift and volatility uncertainty

arXiv: https://arxiv.org/abs/2311.11248 · HTML: https://mlsanity.com/paper/2311.11248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Robust Exploratory Stopping under Ambiguity in Reinforcement Learning](https://mlsanity.com/paper/2510.10260/) (2025)

## Abstract (arXiv)

We examine optimization problems in which an investor has the opportunity to trade in d stocks with the goal of maximizing her worst-case cost of cumulative gains and losses. Here, worst-case refers to taking into account all possible drift and volatility processes for the stocks that fall within a varepsilon-neighborhood of predefined fixed baseline processes. Although solving the worst-case problem for a fixed varepsilon>0 is known to be very challenging in general, we show that it can be approximated as varepsilon→ 0 by the baseline problem (computed using the baseline processes) in the following sense: Firstly, the value of the worst-case problem is equal to the value of the baseline problem plus varepsilon times a correction term. This correction term can be computed explicitly and quantifies how sensitive a given optimization problem is to model uncertainty. Moreover, approximately optimal trading strategies for the worst-case problem can be obtained using optimal strategies from the corresponding baseline problem.