Distributionally robust Kalman filtering with volatility uncertainty
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915347126288384 |
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| 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 |