Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows

Serge Darolles, Gaelle Le Fol and Gulten Mero
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Serge Darolles: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Gulten Mero: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, THEMA - Théorie économique, modélisation et applications - CNRS - Centre National de la Recherche Scientifique - CY - CY Cergy Paris Université

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Abstract: Two types of liquidity problems are distinguished, called respectively liquidity frictions and illiquidity events. The first one is related to orderimbalances that are resorbed within the trading day. It can be assimilated to "immediacy cost" and impacts the traded volume at the intraday anddaily frequencies while affecting the price increments only at the intraday periodicity. The second one is inherent to the long lasting liquidityproblems and is responsible for the time-dependence of the daily returns and volume. We extend the MDHL framework of Darolles to accountfor the presence of the illiquidity events. We then propose a two-step signal extraction formulation of the MDHL model in order to separate the two liquidity problem impacts on the daily returns and volume. We also provide, for a set of FTSE100 individual stocks, long lasting illiquidityindicators.

Date: 2014-12
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Published in 8th International Conference on Computational and Financial Econometrics (CFE 2014), Dec 2014, Pisa, Italy

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Related works:
Journal Article: Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows (2017)
Working Paper: Mixture of Distribution Hypothesis: Analyzing daily liquidity frictions and information flows (2017)
Working Paper: Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows (2016)
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