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

dc.contributor.authorGouriéroux, Christian
dc.contributor.authorDarolles, Serge
dc.contributor.authorJay, Emmanuelle
dc.contributor.authorDuvaut, Patrick
dc.date.accessioned2012-03-12T15:34:47Z
dc.date.available2012-03-12T15:34:47Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/8441
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.abstractenA simple and efficient exogenous outlier detection & estimation algorithm introduced in a regularized version of the Kalman filter is presented. Exogenous outliers that may occur in the observations are considered as an additional stochastic impulse process in the observation equation of the Kalman filter that requires a regularization of the innovation in the recursive equations of the Kalman filter. Regularizing with a l1 or l2-norm needs to determine the value of the regularization parameter. Since the innovation error of the KF is assumed to be Gaussian we propose to first detect the possible occurrence of a non-Gaussian 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. We show that the proposed algorithm can detect anomalies frequently observed in hedge returns such as illiquidity issues.en
dc.identifier.citationpages4en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelEconomie financièreen
dc.relation.conftitle5th CSDA International Conference on Computational and Financial Econometrics (CFE'11)en
dc.relation.confdate2011-12
dc.relation.confcityLondresen
dc.relation.confcountryRoyaume-Unien


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