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hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorLe Fol, Gaëlle
hal.structure.identifierInstituto de Análisis Económico (CSIC) and Barcelona GSE
dc.contributor.authorBrownless, Christian
hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorDarolles, Serge
hal.structure.identifierDauphine Recherches en Management [DRM]
dc.contributor.authorSagna, Béatrice
dc.date.accessioned2021-10-15T14:55:39Z
dc.date.available2021-10-15T14:55:39Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/22034
dc.language.isoenen
dc.subject.ddc332en
dc.subject.classificationjelE.E4.E44en
dc.subject.classificationjelC.C5.C53en
dc.titleForecasting Intra-daily Liquidity in Large Panelsen
dc.typeDocument de travail / Working paper
dc.description.abstractenIn this work we propose a forecasting methodology suitable for large panels of liquidity measures based on exploiting the cross-sectional commonality structure of volume. We begin by providing a number of stylized facts for a panel comprising the CAC40 constituents. We document the presence of a strong common component that is correlated with market volatility. Moreover, after the common component is filtered out, we find evidence of dependence across a number of ticker pairs. These stylized facts motivate us to propose a hybrid forecasting model that is made up of a factor and sparse vector-autoregressive components. We estimate such a model by combining PCA (Principal Component Analysis) and LASSO (Least Absolute Shrinkage and Selection Operator) estimation. We apply our methodology to forecast the intra-daily liquidity of the CAC40 constituents across different intra-daily frequencies. Results show that our approach systematically improves forecasting accuracy over a number of univariate and multivariate benchmarks.en
dc.publisher.cityParisen
dc.relation.ispartofseriestitleUniversité Paris-Dauphineen
dc.subject.ddclabelBourse, Marchés financiers. Taux d’intérêt – Stocks options – Optimisation mathématique – Analyse financièreen
dc.description.ssrncandidatenon
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2021-10-15T14:11:01Z
hal.identifierhal-03380670
hal.version1
dc.subject.classificationjelHALE - Macroeconomics and Monetary Economics::E4 - Money and Interest Rates::E44 - Financial Markets and the Macroeconomyen
dc.subject.classificationjelHALC - Mathematical and Quantitative Methods::C5 - Econometric Modeling::C53 - Forecasting and Prediction Methods; Simulation Methodsen
hal.date.transferred2021-10-15T14:55:42Z
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