Robust Recursive Filtering and Smoothing
Giuseppe Buccheri, Giacomo Bormetti, Fulvio Corsi, Fabrizio Lillo

TL;DR
This paper introduces a new robust filtering and smoothing method for state-space models that relaxes Gaussian assumptions, is easy to implement, and provides reliable confidence intervals, demonstrated through financial time-series applications.
Contribution
It develops a perturbation-based approach that generalizes existing methods, applicable to diverse models, with simple recursive algorithms and uncertainty quantification.
Findings
Small mean square loss compared to exact methods
Effective in univariate and multivariate models
Successful application to financial data
Abstract
Using a perturbation technique, we derive a new approximate filtering and smoothing methodology generalizing along different directions several existing approaches to robust filtering based on the score and the Hessian matrix of the observation density. The main advantages of the methodology can be summarized as follows: (i) it relaxes the critical assumption of a Gaussian prior distribution for the latent states underlying such approaches; (ii) can be applied to a general class of state-space models including univariate and multivariate location, scale and count data models; (iii) has a very simple structure based on forward-backward recursions similar to the Kalman filter and smoother; (iv) allows a straightforward computation of confidence bands around the state estimates reflecting the combination of parameter and filtering uncertainty. We show through an extensive Monte Carlo study…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFinancial Risk and Volatility Modeling · Forecasting Techniques and Applications · Insurance, Mortality, Demography, Risk Management
