Abstract
The sequential inference regarding the state of a system based on new observations is commonly known as the filtering problem. The present work discusses and implements the basic particle filter algorithm of Gordon, Salmond, and Smith (1993) and its robustified counterpart developed by Calvet, Czellar, and Ronchetti (2015). I test the algorithm in simulations and an empirical setting and show that the robustified particle filter performs better than its non-robust counterpart both from a statistical perspective and in terms of numerical stability. I discuss a model for contamination by replacement outliers and show that the performance of the robust particle filter is superior to that of the standard filter in simulations. I apply the algorithm to a time series of daily natural gas futures trading volumes and conclude by highlighting how some existing financial industry applications may benefit from robustification of the model observation density.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
