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hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorDarolles, Serge
hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorLe Fol, Gaëlle
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorLu, Yang
HAL ID: 15957
ORCID: 0000-0002-7141-1048
hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorSun, Ran
dc.date.accessioned2021-10-01T10:34:47Z
dc.date.available2021-10-01T10:34:47Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/21888
dc.language.isoenen
dc.subjectFinancial Econometricsen
dc.subjectHedge Funds/Mutual Fundsen
dc.subjectMarket Microstructure/Liquidityen
dc.subject.ddc332.6en
dc.subject.classificationjelC.C5.C55en
dc.subject.classificationjelC.C5.C58en
dc.subject.classificationjelG.G1.G11en
dc.subject.classificationjelG.G2.G23en
dc.titleA Self-Exciting Model for Mutual Fund Flows: Investor Behaviour and Liability Risken
dc.typeCommunication / Conférence
dc.description.abstractenThis paper analyses the purchase and redemption behaviour of mutual fund investors and its implications on fund liquidity risk. We collect a novel set of proprietary data which contains a large number of French investors holding funds with various degrees of asset liquidity. We build a Self-Exciting Poisson model capturing fund flows' clustering effects and over-dispersion. The model improves the forecast accuracy of future flows and provides a reliable risk indicator (Flow Value at Risk.) Accordingly, we introduce the notion of liability risk where investor's behaviour increases mutual fund liquidity risk. We further decompose fund flows into investor categories. We find that investors exhibit high heterogeneous behaviour, and a lead-lag relation exists between them. Finally, we control flow dynamics for various economic conditions. We show that although flows evolve with economic conditions, investor's behaviour stays the main significant determinant of flows' randomness. Our findings encourage fund manager to adopt an ALM approach.en
dc.subject.ddclabelBourse, Marchés financiers. Taux d’intérêt – Stocks options – Optimisation mathématique – Analyse financièreen
dc.relation.conftitleAFFIen
dc.relation.confdate2018-05
dc.relation.confcityParisen
dc.relation.confcountryFranceen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2021-09-29T15:46:41Z
dc.subject.classificationjelHALC - Mathematical and Quantitative Methods::C5 - Econometric Modeling::C55 - Large Data Sets: Modeling and Analysisen
dc.subject.classificationjelHALC - Mathematical and Quantitative Methods::C5 - Econometric Modeling::C58 - Financial Econometricsen
dc.subject.classificationjelHALG - Financial Economics::G1 - General Financial Markets::G11 - Portfolio Choice; Investment Decisionsen
dc.subject.classificationjelHALG - Financial Economics::G2 - Financial Institutions and Services::G23 - Non-bank Financial Institutions; Financial Instruments; Institutional Investorsen
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