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lq-regularization of the Kalman filter for exogenous outlier removal: application to hedge funds analysis

dc.contributor.authorJay, Emmanuelle
dc.contributor.authorDuvaut, Patrick
dc.contributor.authorDarolles, Serge
dc.contributor.authorGouriéroux, Christian
dc.date.accessioned2012-03-13T15:41:30Z
dc.date.available2012-03-13T15:41:30Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/8472
dc.language.isoenen
dc.subjectKalman filteren
dc.subjectHedge fundsen
dc.subject.ddc332en
dc.subject.classificationjelG12en
dc.titlelq-regularization of the Kalman filter for exogenous outlier removal: application to hedge funds analysisen
dc.typeCommunication / Conférence
dc.description.abstractenThis paper presents a simple and efficient exogenous outlier detection & estimation algorithm introduced in a regularized version of the Kalman Filter (KF). Exogenous outliers that may occur in the observations are considered as an additional stochastic impulse process in the KF observation equation that requires a regularization of the innovation in the KF recursive equations. Regularizing with a l1- or l2-norm needs to determine the value of the regularization parameter. Since the KF innovation error is assumed to be Gaussian we propose to first detect the possible occurrence of an exogenous impulsive spike and then to estimate its amplitude using an adapted value of the regularization parameter. The algorithm is first validated on synthetic data and then applied to a concrete financial case that deals with the analysis of hedge fund returns. The proposed algorithm can detect anomalies frequently observed in hedge returns such as illiquidity issues.en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelEconomie financièreen
dc.relation.conftitleFourth International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)en
dc.relation.confdate2011-12
dc.relation.confcitySan Juanen
dc.relation.confcountryPorto Ricoen


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