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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'ouvrage
Date
2013
Book title
Multi-factor models and signal processing techniques: application to quantitative finance
Book author
Darolles, Serge; Duvaut, Patrick; Jay, Emmanuelle
Publisher
ISTE ; J. Wiley
Published in
London ; Hoboken, NJ
ISBN
978-1-84821-419-4
Number of pages
184
Pages
117-132
Publication identifier
10.1002/9781118577387.ch4
Metadata
Show full item record
Author(s)
Darolles, Serge
Dauphine Recherches en Management [DRM]
Duvaut, Patrick

Jay, Emmanuelle
Abstract (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 data
JEL
C30 - General
G12 - Asset Pricing; Trading Volume; Bond Interest Rates

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