Trade informativeness and liquidity in Bitcoin markets

Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten’s seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.

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High Frequency Asymptotics for the Limit Order Book

Market Microstructure and Liquidity ◽

10.1142/s2382626616500040 ◽

2016 ◽

Vol 02 (01) ◽

pp. 1650004 ◽

Cited By ~ 12

Author(s):

Peter Lakner ◽

Josh Reed ◽

Sasha Stoikov

Keyword(s):

Poisson Process ◽

High Frequency ◽

Limit Order Book ◽

Order Book ◽

Limit Order ◽

Limit Orders ◽

Long Run ◽

Frequency Regime ◽

Market Orders ◽

The One

We study the one-sided limit order book corresponding to limit sell orders and model it as a measure-valued process. Limit orders arrive to the book according to a Poisson process and are placed on the book according to a distribution which varies depending on the current best price. Market orders to buy periodically arrive to the book according to a second, independent Poisson process and remove from the book the order corresponding to the current best price. We consider the above described limit order book in a high frequency regime in which the rate of incoming limit and market orders is large and traders place their limit sell orders close to the current best price. Our first set of results provide weak limits for the unscaled price process and the properly scaled measure-valued limit order book process in the high frequency regime. In particular, we characterize the limiting measure-valued limit order book process as the solution to a measure-valued stochastic differential equation. We then provide an analysis of both the transient and long-run behavior of the limiting limit order book process.

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Optimal Hedge Tracking Portfolios in a Limit Order Book

Market Microstructure and Liquidity ◽

10.1142/s238262661850003x ◽

2017 ◽

Vol 03 (02) ◽

pp. 1850003

Author(s):

Simon Ellersgaard ◽

Martin Tegnér

Keyword(s):

Three Dimensional ◽

Limit Order Book ◽

Order Book ◽

Limit Order ◽

Limit Orders ◽

Viscosity Sense ◽

Quasi Variational Inequality ◽

Market Orders ◽

Hamilton Jacobi Bellman ◽

Optimal Hedge

Derivative hedging under transaction costs has attracted considerable attention over the past three decades. Yet comparatively little effort has been made towards integrating this problem in the context of trading through a limit order book. In this paper, we propose a simple model for a wealth-optimizing option seller, who hedges his position using a combination of limit and market orders, while facing certain constraints as to how far he can deviate from a targeted (Bachelierian) delta strategy. By translating the control problem into a three-dimensional Hamilton–Jacobi–Bellman quasi-variational inequality (HJB QVI) and solving numerically, we are able to deduce optimal limit order quotes alongside the regions surrounding the targeted delta surface in which the option seller must place limit orders vis-à-vis the more aggressive market orders. Our scheme is shown to be monotone, stable, and consistent and thence, modulo a comparison principle, convergent in the viscosity sense.

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Price Discovery in the U.S. Treasury Cash Market: On Principal Trading Firms and Dealers

Finance and Economics Discussion Series ◽

10.17016/feds.2020.096 ◽

2020 ◽

Vol 2020 (095) ◽

pp. 1-36

Author(s):

James Collin Harkrader ◽

◽

Michael Puglia ◽

Keyword(s):

Theoretical Models ◽

Limit Order Book ◽

Price Impact ◽

Market Liquidity ◽

Order Book ◽

Limit Order ◽

Limit Orders ◽

Cash Transaction ◽

Informational Advantage

We explore the following question: does the trading activity of registered dealers on Treasury interdealer broker (IDB) platforms differ from that of principal trading firms (PTF), and if so, how and to what effect on market liquidity? To do so, we use a novel dataset that combines Treasury cash transaction reports from FINRA’s Trade Reporting and Compliance Engine (TRACE) and publicly available limit order book data from BrokerTec. We find that trades conducted in a limit order book setting have high permanent price impact when a PTF is the passive party, playing the role of liquidity provider. Conversely, we find that dealer trades have higher price impact when the dealer is the aggressive party, playing the role of liquidity taker. Trades in which multiple firms (whether dealers or PTFs) participate on one or both sides, however, have relatively low price impact. We interpret these results in light of theoretical models suggesting that traders with only a “small” informational advantage prefer to use (passive) limit orders, while traders with a comparatively large informational advantage prefer to use (aggressive) market orders. We also analyze the events that occurred in Treasury markets in March 2020, during the onset of the COVID-19 pandemic.

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