Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.05993 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915347126288384 |
|---|---|
| author | Han, Bingyan |
| author_facet | Han, Bingyan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_05993 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distributionally robust Kalman filtering with volatility uncertainty Han, Bingyan Optimization and Control 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. |
| title | Distributionally robust Kalman filtering with volatility uncertainty |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2302.05993 |