A Regularized Kalman Filter (rgKF) for Spiky Data

Abstract : 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.
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Chapitre d'ouvrage
Multi-factor models and signal processing techniques: application to quantitative finance, pp.117-132, 2013, 〈10.1002/9781118577387.ch4〉
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https://hal.archives-ouvertes.fr/hal-01632887
Contributeur : Christine Okret-Manville <>
Soumis le : vendredi 10 novembre 2017 - 16:52:58
Dernière modification le : samedi 11 novembre 2017 - 01:14:46

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Serge Darolles, Patrick Duvaut, Emmanuelle Jay. A Regularized Kalman Filter (rgKF) for Spiky Data. Multi-factor models and signal processing techniques: application to quantitative finance, pp.117-132, 2013, 〈10.1002/9781118577387.ch4〉. 〈hal-01632887〉

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