A Regularized Kalman Filter (rgKF) for Spiky Data

Serge Darolles, Patrick Duvaut and Emmanuelle Jay

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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.

Keywords: regularized Kalman filter (rgKF); robust Kalman filter (RKF); spiky data (search for similar items in EconPapers)
Date: 2013
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Published in Multi-factor models and signal processing techniques: application to quantitative finance, pp.117-132, 2013, ⟨10.1002/9781118577387.ch4⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01632887

DOI: 10.1002/9781118577387.ch4

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