Forecasting prices from level-I quotes in the presence of hidden liquidity

Marco Avellaneda, Josh Reed and Sasha Stoikov ()
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Marco Avellaneda: Courant Institute, New York University and Finance Concepts LLC, NY, USA
Josh Reed: Stern School of Business, New York University, NY, USA
Sasha Stoikov: Cornell Financial Engineering Manhattan, NY, USA

Algorithmic Finance, 2011, vol. 1, issue 1, 35-43

Abstract: Bid and ask sizes at the top of the order book provide information on short-term price moves. Drawing from classical descriptions of the order book in terms of queues and order-arrival rates (Smith et al., 2003), we consider a diffusion model for the evolution of the best bid/ask queues. We compute the probability that the next price move is upward, conditional on the best bid/ask sizes, the hidden liquidity in the market and the correlation between changes in the bid/ask sizes. The model can be useful, among other things, to rank trading venues in terms of the “information content” of their quotes and to estimate hidden liquidity in a market based on high-frequency data. We illustrate the approach with an empirical study of a few stocks using quotes from various exchanges

JEL-codes: A10 C00 C02 C15 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0004

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