A Regularized Kalman Filter (rgKF) for Spiky Data
Darolles, Serge; Duvaut, Patrick; Jay, Emmanuelle (2013), A Regularized Kalman Filter (rgKF) for Spiky Data, in Darolles, Serge; Duvaut, Patrick; Jay, Emmanuelle, Multi-factor models and signal processing techniques: application to quantitative finance, ISTE ; J. Wiley : London ; Hoboken, NJ, p. 117-132. 10.1002/9781118577387.ch4
Type
Chapitre d'ouvrageDate
2013Book title
Multi-factor models and signal processing techniques: application to quantitative financeBook author
Darolles, Serge; Duvaut, Patrick; Jay, EmmanuellePublisher
ISTE ; J. Wiley
Published in
London ; Hoboken, NJ
ISBN
978-1-84821-419-4
Number of pages
184Pages
117-132
Publication identifier
Metadata
Show full item recordAbstract (EN)
This chapter presents a new family of algorithms named regularized Kalman Filters (rgKFs) that have been derived to detect and estimate exogenous outliers that might occur in the observation equation of a standard Kalman filter (KF). Inspired from the robust Kalman filter (RKF) of Mattingley and Boyd, which makes use of a l1-regularization step, the authors introduce a simple but efficient detection step in the recursive equations of the RKF. This solution is one means by which to solve the problem of adapting the value of the l1-regularization parameter: when an outlier is detected in the innovation term of the KF, the value of the regularization parameter is set to a value that will let the l1-based optimization problem estimate the amplitude of the spike. The chapter deals with the application of algorithm to detect irregularities in hedge fund returns.Subjects / Keywords
regularized Kalman filter (rgKF); robust Kalman filter (RKF); spiky dataRelated items
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