Distributionally robust Kalman filtering with volatility uncertainty

2023/02/12 by Bingyan Han, Han, Bingyan · 2 citations
Computer Science · Social Sciences · #Target Tracking and Data Fusion in Sensor Networks #Insurance, Mortality, Demography, Risk Management

paper · pdf · doi:10.48550/arxiv.2302.05993

Abstract

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.

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• Distributionally Robust Kalman Filter
• Distributionally robust LMI synthesis for LTI systems

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