Systemwide Commonalities in Market Liquidity

78 Pages Posted: 31 May 2015

See all articles by Mark D. Flood

Mark D. Flood

R. H. Smith School of Business, U. of Maryland

John Liechty

Pennsylvania State University, University Park

Thomas Piontek

Government of the United States of America - Office of Financial Research

Date Written: May 28, 2015

Abstract

We explore statistical commonalities among granular measures of market liquidity with the goal of illuminating systemwide patterns in aggregate liquidity. We calculate daily invariant price impacts described by Kyle and Obizhaeva [2014] to assemble a granular panel of liquidity measures for equity, corporate bond, and futures markets. We estimate Bayesian models of hidden Markov chains and use Markov chain Monte Carlo analysis to measure the latent structure governing liquidity at the systemwide level. Three latent liquidity regimes -- high, medium, and low price-impact -- are adequate to describe each of the markets. Focusing on the equities subpanel, we test whether a collection of systemwide market summaries can recover the estimated liquidity dynamics. This allows an economically meaningful attribution of the latent liquidity states and yields meaningful predictions of liquidity disruptions as far as 15 trading days in advance of the 2008 financial crisis.

Keywords: market liquidity, commonalities, Markov chain Monte Carlo

JEL Classification: E44, G18, C11, C82, C33

Suggested Citation

Flood, Mark D. and Liechty, John and Piontek, Thomas, Systemwide Commonalities in Market Liquidity (May 28, 2015). Available at SSRN: https://ssrn.com/abstract=2612348 or http://dx.doi.org/10.2139/ssrn.2612348

Mark D. Flood (Contact Author)

R. H. Smith School of Business, U. of Maryland ( email )

College Park
College Park, MD 20742
United States

John Liechty

Pennsylvania State University, University Park ( email )

University Park
State College, PA 16802
United States

Thomas Piontek

Government of the United States of America - Office of Financial Research ( email )

717 14th Street, NW
Washington DC, DC 20005
United States

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