Limit Order Book and Commonality in Liquidity

Waiting costs and limit order book liquidity: Evidence from the ex-dividend deadline in Australia

Information content of the limit order book for crude oil futures price volatility

Which is Limit Order Traders More Fearful of: Non-Execution Risk or Adverse Selection Risk'

We examine the soundness of high-frequency trading (HFT) proxies that are widely defined on the limit order book (LOB) information. We use a unique TRTH (Thomson Reuters Tick History) millisecond time-stamped intraday trades and quotes dataset enriched with 10 levels of LOB depth messages for 149 highly fragmented LSE listed stocks for the period 2005 to 2016. We explore a sharp uptrend in HFT activities and accompanying improvement in market liquidity in the European market. We show that alternative HFT proxies built on LOB are not equally powerful. The HFT proxy defined on the five best LOB prices (the mid point of a typical limit order book) provides a better HFT identification than the one popularly defined on the first best prices (BBO). We suggest that picking the LOB information beyond a certain level (e.g., the best five prices) of market depth in developing HFT proxy is counterintuitive. Evidence indicates that high-frequency traders (HFTs) participate in both competitive (narrow) and passive (wider) quoting as a market making strategy; however, they do not participate in passive quoting excessively.

An experimental approach to the study of limit order books lies in the availability of data. Most of the results presented in this book - in any case, those we have produced ourselves - use the Thomson Reuters Tick History (TRTH) database. All exchange-traded assets worldwide are present in the TRTH database, where they are identified by their Reuters Identification Code (RIC). Similar to most historical databases directly provided by the exchanges, the TRTH data come into the form of two separate files, a trade file recording all transactions, and an event file recording every change in the limit order book. Some very specific information, such as traders’ identities, cannot be publicly disclosed for obvious confidentiality reasons, but in theory, one could reconstruct the sequence of order arrivals of all types using this trade and event files. After explaining the algorithm used for the processing of limit order book data, we describe the specific data sets that have been used at various places in this book. That way, our results can be reproduced, extended and, of course, challenged, based on the very same data sets we have used. Limit Order Book Data Processing Because one cannot distinguish market orders from cancellations just by observing changes in the limit order book (the “event” file), and since, the timestamps of the “trade” and “event” files are asynchronous , we use a matching procedure to reconstruct the order book events. In a nutshell, we proceed as follows for each stock and each trading day: 1. Parse the “event” file to compute order book state variations: • If the variation is positive (volume at one or more price levels has increased), then label the event as a limit order. • If the variation is negative (volume at one or more price levels has decreased), then label the event as a “likely market order”. • If no variation—this happens when there is just a renumbering in the field “Level” that does not affect the state of the book—do not count an event. 2. Parse the “trade” file and for each trade: • Compare the trade price and volume to likely market orders whose timestamps are in, where t Tr is the trade timestamp and Δ t is a predefined, market-dependent time window.

In line with market trade signals, we develop a micro-founded structural price formation model featuring partially informed and noise traders. Imposing zero expected net profit per trade under market efficiency, we derive the model’s likelihood to reliably estimate daily market and funding liquidity from intra-day transaction prices. We examine the dynamic interplay among stock volatilities, market liquidity, and funding liquidity via an MGARCH-VAR process. Fitting the model using standard realized volatility econometrics on intra-day data estimates, our analysis of NYSE stocks uncovers four stylized facts: (i) stock volatilities, market liquidity, and funding liquidity co-move; (ii) these co-movements intensify during funding liquidity shortages; (iii) lower volatility stocks tend to have higher market liquidity; and (iv) funding liquidity constraints more adversely affect the market liquidity of high-volatility stocks.

We model the trading strategy of an investor who spoofs the limit order book (LOB) to increase the revenue obtained from selling a position in a security. The strategy employs, in addition to sell limit orders (LOs) and sell market orders (MOs), a large number of spoof buy LOs to manipulate the volume imbalance of the LOB. Spoofing is illegal, so the strategy trades off the gains that originate from spoofing against the expected financial losses due to a fine imposed by the financial authorities. As the fine increases, the investor relies less on spoofing, and if the fine is large, the investor does not spoof the LOB. The arrival rate of buy MOs increases because other traders interpret the spoofed buy-heavy LOB as an upward pressure on prices. When the fine is low, spoofing considerably increases the revenues from liquidating a position. Spoofing increases the PnL because (i) the investor employs fewer MOs to draw the inventory to zero and benefits from roundtrip trades, which stem from spoof buy LOs that are ‘inadvertently’ filled and subsequently unwound with sell LOs; and (ii) the midprice trends upward when the book is buy-heavy; therefore the spoofer sells the asset at better prices.

Order Book Commonalities and Bond Return Predictability

Non-Parametric Prediction of the Mid-Price Dynamics in a Limit Order Book

This paper introduces a framework for analysis of cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We rst consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Next, we study an idiosyncratic volatility factor model, in which we decompose the co-movements in idiosyncratic volatilities into two parts: those related to factors such as the market volatility, and the residual co-movements. When using high frequency data, naive estimators of all of the above measures are biased due to the estimation errors in idiosyncratic volatility. We provide bias-corrected estimators and establish their asymptotic properties. We apply our estimators to high-frequency data on 27 individual stocks from nine dierent sectors, and document strong cross-sectional dependence in their idiosyncratic volatilities. We also nd that on average 74% of this dependence can be explained by the market volatility.

This study provides new evidence regarding the effect of limit order book disclosure on trading behavior. The natural experiment affected by the Sydney Futures Exchange in January 2001, when it increased limit order book disclosure from depth at the best bid and ask prices to depth at the three best bid and ask prices is examined. Evidence was found consistent with a change in trading behavior coinciding with the increase in pre‐trade transparency. Consistent with predictions of a theoretical model based on execution risk, a statistically significant decline in depth was found at the best quotes. There is little evidence of an increase in bid‐ask spreads. Further, the proportion of market orders exceeding depth at the best quotes increases in a transparent limit order book, reflecting a reduction in execution risk. The study concludes that in a transparent market, limit order traders charge market order traders a higher premium for execution certainty by withdrawing depth from the best quotes, but not by increasing bid‐ask spreads. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:1147–1167, 2006

Market impact is one of the most important aspects of real-world trading. It is typically a major component of transaction costs, and trading strategies are carefully optimized to minimize it. But most of the research has focused on one-off trades, not the repeated series of transactions required in delta-hedging an options position. In this article, the authors explore optimal hedging in an illiquid environment for a large trader whose trades absorb a nonmarginal fraction of the standing orders in the limit order book. The trader needs to take account of both the immediate price impact of each trade and the speed at which the limit order book is restored afterward—the market’s resilience. These two aspects of market dynamics together can produce a permanent effect of a big trade and also a temporary one that dissipates over time. Depending on the parameters of the liquidity process, the optimal strategy can be the canonical Black–Scholes delta hedge (in the limiting case of infinite liquidity) or one that is distinctly less volatile (when market impact is large, but there is some resilience in prices) or even one that entails price manipulation (when there is no resilience and the price impact is permanent). <b>TOPICS:</b>Derivatives, quantitative methods

We introduce new machine learning methods for clustering traders who are actively trading in a modern electronic exchange which uses a matching engine to track aggregate and individual‐level limit order books. Each trader's individual limit order book is centered (with the current best bid and ask prices acting as a central reference), and the patterns in the individual limit order books are identified using a Wasserstein distance. The method is illustrated using simulated limit order book data and limit order book data from a stock exchange in Canada.

Liquidity provision in a limit order book without adverse selection

Since Kalman Filter was first developed by Kalman (1960), it has been widely used in engineering, statistics and econometrics. Filtering is different from forecasting as forecasting is made for predicting the future while filtering aims the estimation of unobservable parameters in the same period. For techniques as Kalman filters, on one hand, it can help to filter away the “noisy order”, revealing the true state of limit order book. On the other hand, it can be also use for prediction of state for next time step. In this paper, we estimate the limit order book using gamma distribution. Then each order book snapshot can be described using four parameters. After that, the basic filter is introduced with applications to disclose the pattern in limit order book.