Forecasting Intra-daily Liquidity in Large Panels
Le Fol, Gaëlle; Brownless, Christian; Darolles, Serge; Sagna, Béatrice (2019), Forecasting Intra-daily Liquidity in Large Panels. https://basepub.dauphine.psl.eu/handle/123456789/22034
Type
Document de travail / Working paperDate
2019Series title
Université Paris-DauphinePublished in
Paris
Metadata
Show full item recordAuthor(s)
Le Fol, GaëlleDauphine Recherches en Management [DRM]
Brownless, Christian
Instituto de Análisis Económico (CSIC) and Barcelona GSE
Darolles, Serge
Dauphine Recherches en Management [DRM]
Sagna, Béatrice
Dauphine Recherches en Management [DRM]
Abstract (EN)
In 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.Related items
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