# Distributionally robust Kalman filtering with volatility uncertainty

arXiv: https://arxiv.org/abs/2302.05993 · HTML: https://mlsanity.com/paper/2302.05993/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2023.

## Built on by (most cited first, 2 of 2)

- [Fitted value iteration methods for bicausal optimal transport](https://mlsanity.com/paper/2306.12658/) (2023)
- [Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces](https://mlsanity.com/paper/2406.11281/) (2024)

## Abstract (arXiv)

This work presents a distributionally robust Kalman filter to address uncertainties in noise covariance matrices and predicted covariance estimates. We adopt a distributionally robust formulation using bicausal optimal transport to characterize a set of plausible alternative models. The optimization problem is transformed into a convex nonlinear semi-definite programming problem and solved using the trust-region interior point method with the aid of LDL^top decomposition. The empirical outperformance is demonstrated through target tracking and pairs trading.