Author
Listed:
- Gaëlle Le Fol
(DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)
- Christian Brownless
(Instituto de Análisis Económico (CSIC) and Barcelona GSE - Instituto de Análisis Económico (CSIC) and Barcelona GSE)
- Serge Darolles
(DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)
- Béatrice Sagna
(DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)
Abstract
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.
Suggested Citation
Gaëlle Le Fol & Christian Brownless & Serge Darolles & Béatrice Sagna, 2021.
"Forecasting Intra-daily Liquidity in Large Panels,"
Working Papers
hal-03380670, HAL.
Handle:
RePEc:hal:wpaper:hal-03380670
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