Mixture of Distribution Hypothesis: Analyzing Daily Liquidity Frictions and Information Flows

Posted: 30 Jan 2020

See all articles by Gaëlle Le Fol

Gaëlle Le Fol

Université Paris-Dauphine, PSL Research University, CNRS, UMR 7088, DRM, Finance, 75016 Paris, France; National Institute of Statistics and Economic Studies (INSEE) - Center for Research in Economics and Statistics (CREST)

Date Written: January 7, 2017

Abstract

The mixture of distribution hypothesis (MDH) model offers an appealing explanation for the positive relation between trading volume and volatility of returns. In this specification, the information flow constitutes the only mixing variable responsible for all changes. However, this single static latent mixing variable cannot account for the observed short-run dynamics of volume and volatility. In this paper, we propose a dynamic extension of the MDH that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. We distinguish between short-term and long-term liquidity frictions. Our results highlight the economic value and statistical accuracy of our specification. First, based on some goodness of fit tests, we show that our dynamic two-latent factor model outperforms all competing specifications. Second, the information flow latent variable can be used to propose a new momentum strategy. We show that this signal improves once we allow for a second signal – the liquidity frictions latent variable – as the momentum strategies based on our model present better performance than those based on competing models.

Keywords: Strategic liquidity trading Market efficiency, Mixture of distribution hypothesis, Information-based trading, Extended Kalman Filter

JEL Classification: C51, C52, G12, G14

Suggested Citation

Le Fol, Gaëlle, Mixture of Distribution Hypothesis: Analyzing Daily Liquidity Frictions and Information Flows (January 7, 2017). Journal of Econometrics, Vol. 201, 2017; Université Paris-Dauphine Research Paper No. 3515235. Available at SSRN: https://ssrn.com/abstract=3515235

Gaëlle Le Fol (Contact Author)

Université Paris-Dauphine, PSL Research University, CNRS, UMR 7088, DRM, Finance, 75016 Paris, France ( email )

Place du Maréchal de Lattre de Tassigny
Paris Cedex 16, 75775
France

National Institute of Statistics and Economic Studies (INSEE) - Center for Research in Economics and Statistics (CREST) ( email )

15 Boulevard Gabriel Peri
Malakoff Cedex, 1 92245
France

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