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Order aggressiveness and order book dynamics
Abstract
In this paper, we study the determinants of order aggressiveness and traders’ order submission strategy in an open limit order book market. Applying an order classification scheme, we model the most aggressive market orders, limit orders as well as cancellations on both sides of the market employing a six-dimensional autoregressive conditional intensity model. Using order book data from the Australian Stock Exchange, we find that market depth, the queued volume, the bid-ask spread, recent volatility, as well as recent changes in both the order flow and the price play an important role in explaining the determinants of order aggressiveness. Overall, our empirical results broadly confirm theoretical predictions on limit order book trading. However, we also find evidence for behavior that can be attributed to particular liquidity and volatility effects.
... Several studies such as Biais, Hillion and Spatt (1995), Cao, Hansch andWang (2008), Griffiths et al. (2000), Ranaldo (2004), Hall and Hautsch (2006) and Pascual and Veredas (2008) examine this phenomenon and find strong evidence supporting the crowding out effect. Though during the preopening period there is no active execution of trades, the general principle of the crowding out effect can be applied. ...
... Empirical evidence supporting the impact of the size of the spread and the level of order aggressiveness is confirmed by Biais, Hillion and Spatt (1995), Cao, Hansch and Wang (2008), Griffiths et al. (2000), Ranaldo (2004), Hall and Hautsch (2006) and Pascual and Veredas (2008). However, the preopening period presents a separate challenge in determining its likely impact on the aggressiveness of trader strategy. ...
... These results confirm that order book depth plays an important role in determining the aggressiveness of order submissions during the preopening period. as confirmed for the active trading period by Ranaldo (2004), Hall and Hautsch (2006), Cao et al (2008) among others. ...
Using a unique dataset we examine the aggressiveness of order submissions, revisions and cancellations in the absence of trade execution. Specifically we study the impact of the limit order book information on the aggressiveness observed in the submission of limit orders and the revision or cancellation of orders queued in the preopening limit order book. The empirical results indicate that the aggressiveness of order submissions and forward price revisions react to the existing and subsequent changes in the execution probability driven in part by the depth on either side of the order book. We find that the aggressiveness of order cancellations increases on both sides of the order book when the depth at the top of the ask order book increases. In addition, we find that the height and the side of the inside spread impacts the aggressiveness of order submissions, revisions and cancellations.
... These driving forces have been recently identified as key mechanisms of modern trading (see, e.g., Foucault et al. (2005) and Hendershott and Mendelson (2002)). Our findings complement this line of research and demonstrate that fundamental empirical market microstructure relationships in limit order book markets (see, e.g., Biais (1993), Ranaldo (2004), and Hall and Hautsch (2006)) can be explained by the interplay between liquidity competition and the disclosure of trading intentions. For instance, we show that liquidity suppliers hide a larger fraction of their orders when the spread is wide and when the opposite side of the book is thin. ...
... For instance, Bessembinder et al. (2009) report that the decision to hide and the magnitude of the hidden size are positively affected by the size of the spread and negatively affected by opposite-side depth. Likewise, Biais et al. (1995), Ranaldo (2004), Cao et al. (2009) and Hall and Hautsch (2006) show that liquidity competition increases if spreads widen. Ranaldo (2004) and Cao et al. (2009) report that liquidity competition increases when same-side depth is large. ...
... Using order imbalances allows us to test market-side-specific effects of hidden and displayed volume. Biais et al. (1995), Ranaldo (2004), Chordia et al. (2002), Hall and Hautsch (2006), and Cao et al. (2009) show that order imbalances carry more information than individual order depth levels about the state of the market. Moreover, directly modeling buy-sell depth imbalances simplifies the analysis, as these variables are less persistent than the underlying depth levels. ...
We develop a model of an order-driven exchange competing for order flow with off-exchange trading mechanisms. Liquidity suppliers face a trade-off between benefits and costs of order exposure. If they display trading intentions, they attract additional trade demand. We show, in equilibrium, hiding trade intentions can induce mis-coordination between liquidity supply and demand, generate excess price fluctuations and harm price efficiency. Econometric high-frequency analysis based on unique data on hidden orders from NASDAQ reveals strong empirical support for these predictions: We find abnormal reactions in prices and order flow after periods of high excess-supply of hidden liquidity.
... Empirical analysis of investors' order submission strategies generally provides support for theoretical predictions of the effect of spread and market depth on the order aggressiveness of investors. This empirical evidence is consistent and robust for different markets and over different sample periods (see for example Biais et al., 1995;Griffiths et al., 2000;Al-Suhaibani and Kryzanowski, 2000;Ranaldo, 2004;Verhoeven et al., 2004;Beber and Caglio, 2005;Hall and Hautsch, 2006;Ellul et al., 2007;Aitken, Brown and Wee, 2007;Cao et al., 2008). ...
... This finding supports Hypothesis 2, which suggests a negative relation between the order aggressiveness of institutional investors and the bid-ask spread. The order aggressiveness of individual investors is also negatively related 19 See, for example, Biais et al. (1995), Parlour (1998), Griffiths et al. (2000), Ranaldo (2004), Beber and Caglio (2005), Hall and Hautsch (2006), Ellul et al. (2007), Aitken, Brown and Wee (2007) and Cao et al. (2008). ...
... The results presented in the previous sections are based only on new order submissions and order revisions. This is motivated by the argument ofHall and Hautsch (2006) for the need to model order submissions and order cancellations differently. In the sample period under investigation, 25.70% of all orders are subsequently cancelled and order cancellation accounts for 16.11% of all new order submissions, order revisions and order cancellations. ...
... These driving forces have been recently identified as key mechanisms of modern trading (see, e.g., Foucault et al. (2005) and Hendershott and Mendelson (2002)). Our findings complement this line of research and demonstrate that fundamental empirical market microstructure relationships in limit order book markets (see, e.g., Biais (1993), Ranaldo (2004), and Hall and Hautsch (2006)) can be explained by the interplay between liquidity competition and the disclosure of trading intentions. For instance, we show that liquidity suppliers hide a larger fraction of their orders when the spread is wide and when the opposite side of the book is thin. ...
... For instance, Bessembinder et al. (2009) report that the decision to hide and the magnitude of the hidden size are positively affected by the size of the spread and negatively affected by opposite-side depth. Likewise, Biais et al. (1995), Ranaldo (2004), Cao et al. (2009) and Hall and Hautsch (2006) show that liquidity competition increases if spreads widen. Ranaldo (2004) and Cao et al. (2009) report that liquidity competition increases when same-side depth is large. ...
... Using order imbalances allows us to test market-side-specific effects of hidden and displayed volume. Biais et al. (1995), Ranaldo (2004), Chordia et al. (2002), Hall and Hautsch (2006), and Cao et al. (2009) show that order imbalances carry more information than individual order depth levels about the state of the market. Moreover, directly modeling buy-sell depth imbalances simplifies the analysis, as these variables are less persistent than the underlying depth levels. ...
We show that the excessive use of hidden orders causes artificial price pressures and
abnormal asset returns. Using a simple game-theoretical setting, we demonstrate that this
effect naturally arises from mis-coordination in trading schedules between traders, when
suppliers of liquidity do not sufficiently disclose their trade intentions. As a result, hid-
den liquidity can increase trading costs and induce excess price fluctuations unrelated to
information. Using NASDAQ order book data, we find strong empirical support and illus-
trate that hidden liquidity is higher if bid-ask spreads are smaller and relative tick sizes are
higher.
... Several studies such as Biais, Hillion and Spatt (1995), Cao, Hansch and Wang (2008), Griffiths et al. (2000), Ranaldo (2004), Hall and Hautsch (2006) and Pascual and Veredas (2008) examine this phenomenon and find strong evidence supporting the crowding out effect. Though during the preopening period there is no active execution of trades, the general principle of the crowding out effect can be applied. ...
... Empirical evidence supporting the impact of the size of the spread and the level of order aggressiveness is confirmed by Biais, Hillion and Spatt (1995), Cao, Hansch and Wang (2008), Griffiths et al. (2000), Ranaldo (2004), Hall and Hautsch (2006) and Pascual and Veredas (2008). However, the preopening period presents a separate challenge in determining its likely impact on the aggressiveness of trader strategy. ...
... These results confirm that order book depth plays an important role in determining the aggressiveness of order submissions during the preopening period. as confirmed for the active trading period by Ranaldo (2004), Hall and Hautsch (2006), Cao et al (2008) among others. ...
This paper analyses the impact of the limit order book information on the aggressiveness observed in the submission of limit orders and the revision or cancellation of orders queued in the preopening period of an electronic limit order book equity market. To our knowledge we are the first to do so. We model each side of the order book separately using ordered probit models for submissions, forward and backward revisions, and order cancellations. In essence, we rank the aggressiveness of order submissions, revisions and cancellations based upon the impact of the action on the execution probability of the order. The empirical results indicate that the aggressiveness of order submissions and forward price revisions react to the existing and subsequent changes in the execution probability driven in part by the depth on either side of the order book. We find that backward price revisions less affected by order book depth, except that aggressive backward bid price revisions reduce when there is an increase in the ask depth below the top of the order book. This suggests that the bid side relies on the ask side to provide liquidity. We find that the aggressiveness of order cancellations increases on both sides of the order book when the depth at the top of the ask order book increases. Order submissions and forward and backward revisions aggression increase on the bid side when there is an increase in the height on both sides; however, we find mixed reactions on the ask side. Finally, the results indicates that aggressiveness observed in order cancellations is not impacted by the magnitude of the inside spread. wishes to gratefully acknowledge the financial support of the Commonwealth Scholarship Commission. The authors wish to thank the MSE (Malta Stock Exchange) for providing the data.
... Notice, however, that the order flow is more symmetric in the transparent case. Concerning the unconditional probabilities of six order types in a quasi-transparent market, we compare the distribution to the empirical data from Paris Bourse in Biais et al (1995) and the Australian Stock Exchange in Hall and Hautsch (2006), both of which allow reduced quote transparency. In the data from Paris Bourse, the sellers, and most impatient ones, outnumber the buyers. ...
... In the data from Paris Bourse, the sellers, and most impatient ones, outnumber the buyers. On the contrary, out of five stocks from the Australian Stock Exchange analyzed by Hall and Hautsch (2006) buyers dominate in two markets compared to fairly symmetric order flows in the other three, which we reproduce inTable 3 for our order classification . In terms of order flow pattern and overall order flow aggressiveness the simulated markets bear the closest resemblance to the market for News Corporation shares (NCP): the proportions for buy and sell order are symmetric with a large fraction of passive limit orders. ...
This chapter investigates the interrelation between pre-trade quote transparency and stylised properties of order-driven markets populated by traders with heterogeneous beliefs. In a modified version of Chiarella et al. (2009) model we address the ability of the artificial stock market to replicate the empirical phenomena detected in financial markets. Our framework captures negative skewness of stock returns and volatility clustering once book depth is visible to traders. Further simulation analysis reveals that full quote transparency contributes to convergence in traders' actions, while exogenous partial transparency restriction may exacerbate long-range dependencies. © 2014 Springer International Publishing Switzerland. All rights are reserved.
... In the second step, the trader chooses the best submission strategy according to the level of aggressiveness determined previously. Hall andHautsch (2006) adopt BHS's (1995) ranking to examine the order arrival process through an autoregressive conditional intensity model, and they find support for a multivariate dynamics of order arrivals; in a way, their paper follows the previous research by Bisière and Kamionka (2000). Lo and Sapp (2010) present a simultaneous equation model to evaluate order aggressiveness in the currency markets. ...
... Therefore, we expect to find a positive relationship between price aggressiveness and volume available at the same side of the market, as in Ranaldo (2004) or Pascual and Veredas (2009). As to volume aggressiveness, we expect a negative relationship between order size and depth on the own side, since the traders seeking for a faster execution combine the high price aggressiveness with a small quantity (see Sapp, 2010 or Hall andHautsch, 2006). ...
In this paper, we considerjoint estimation of objective and risk-neutral parameters for stochastic volatility option pricing models using both stock and option prices. A common strategy simplifies the task by limiting the analysis to just one option per date. We first discuss its drawbacks on the basis of model interpretation, estimation results and pricing exercises. We then turn the attention to a more flexible approach, that successfully exploits the wealth of information contained in large heterogeneous panels of options, and we apply it to actual S&P 500 index and index call options data. Our approach breaks the stochastic singularity between contemporaneous option prices by assuming that every observation is affected by measurement error, essentially recasting the problem as a non-linear filtering one. The resulting likelihood function is evaluated using a Monte Carlo Importance Sampling (MCIS) strategy, combined with a Particle Filter algorithm. The results provide useful intuitions on the directions that should be followed to extend the model, in particular by allowing jumps or regime switching in the volatility process.
... The limit orders submitted and canceled are identified by numbers characterizing the aggressiveness and direction (buyer-versus seller-initiated) of orders. Specifically, the buyer-initiated (or seller-initiated) orders are differentiated into six aggressive catalogs from less aggressive to more aggressive: canceled orders, orders inside the book, orders on the same best price, orders inside the spread, filled orders, and unfilled orders [44,45,46]. More information about the market can be found in Ref. [47]. ...
We study the distributions of event-time returns and clock-time returns at different microscopic timescales using ultra-high-frequency data extracted from the limit-order books of 23 stocks traded in the Chinese stock market in 2003. We find that the returns at the one-trade timescale obey the inverse cubic law. For larger timescales (2-32 trades and 1-5 minutes), the returns follow the Student distribution with power-law tails. With the decrease of timescale, the tail becomes fatter, which is consistent with the vibrational theory.
... Traders can either be patient and submit limit-orders or pay the bid-ask spread premium to submit marketorders for immediate execution (Copeland and Galai 1983;Kyle 1985;Glosten and Milgrom 1985;Easley and O'Hara 1992;Foucault 1999;Foucault et al. 2005). Limit-orders and market-orders submission strategies have also been shown to be dependent upon quoted bid-ask spreads (Biais et al. 1995;Harris and Hasbrouck 1996;Ranaldo 2004;Hall and Hautsch 2006;Pascual and Veredas 2009). Liquidity has also been shown to be the dominant determinant of volatility in equity markets for short time scales; and that long-memory of liquidity rather than volume is a key factor influencing volatility on the NYSE, London and Shenzen stock markets (Farmer et al. 2005;Lillo and Farmer 2008;Plerou et al. 2005;Weber and Rosenow 2006;Gillemot et al. 2006;Gu et al. 2007;La Spada et al. 2008;Mike and Farmer 2008). ...
This paper analyses high frequency MTS data to comprehensively evaluate the liquidity of the European sovereign bond markets before and during the European sovereign debt crisis for eleven countries. The Hill index, Generalized Hurst exponent and Dynamic Conditional Score are employed to evaluate the properties of the bid-ask spread. Sovereign bonds exhibit the stylized facts reported for a range of financial markets. The 1-min interval analysis indicates the level of bid-ask spread exhibits long-memory and the change in bid-ask spread experiences volatility clustering. In a dynamic setting, the volatility of bid-ask spread also exhibits long-memory in most European sovereign bond markets across all three maturities. Long-memory effects diminish (disappear) for 5-min (15-min) interval, and for short-term maturity (peripheral countries) is stronger than long-term maturity (core countries). Analysis of sub-periods indicates that long-memory process reached its peak during European sovereign debt crisis from May 2010 to December 2011. This analysis suggests that estimating long-memory parameters for high-frequency data could be a useful tool to monitor market stability.
... Because of the log-linear structure of the ACI model, the marginal change of Δ B (t) induced by a change of the covariates is computed as γ B − γ S , where γ B and γ S denote the coefficients associated with covariates affecting the buy and sell intensity, respectively (see eq. (35)). Hall and Hautsch (2006) study the determinants of order aggressiveness and traders' order submission strategy at the ASX by applying a six-dimensional ACI model to study the arrival rates of aggressive market orders, limit orders as well as cancellations on both sides of the market. In a related paper, Large (2007) studies the resiliency of an electronic limit order book by modelling the processes of orders and cancellations on the London Stock Exchange using a ten-dimensional Hawkes process. ...
... The empirical literature on the effects of order exposure and order aggressiveness provides rich and extensive evidence for the predictions of corollary 3. For instance, in line with (i) and (ii), Cebiroglu and Horst (2013), Ranaldo (2004) and Cao et al. (2009) report that orders are more aggressive when same-side depth is large, while Biais et al. (1995), Ranaldo (2004), Cao et al. (2009), Hall andHautsch (2006) show that liquidity competition is more likely, when the spread is wide. Likewise, empirical evidence is reported for conclusion (iii), (iv) and (v), see, for instance, Cao et al. (2009), Cebiroglu andHorst (2013) and Harris (1994Harris ( , 1996Harris ( , 2003, respectively. ...
An den Handelsbörsen der Welt, hat der Anteil unsichtbarer Luidität in den letzten Jahren dramatisch zugenommen. Obwohl dieser Trend zunehmend in den Fokus regulatorischer Debatten und akademischer Dikussionen rückt, sind sich Forscher und die Aufsichtsbehörden über die Implikationen und entsprechende regulatorische Maßnahmen uneins. In der vorliegenden Arbeit, werden die damit verbundenen Fragestellungen in drei separaten Kapiteln theoretisch und empirisch untersucht. Mit Hilfe eines speziellen NASDAQ Datensatzes, werden in Kapitel 1 die Marktfaktoren, die unsichtbaren Liquidität begünstigen sowie den Einfluß, den unsichtbare Liquidät auf Märkte ausübt, empirisch ausgewertet. Wir zeigen, daß die Querschnittsvariation unsichtbarer Liquidität entlang des Aktienuniversums in einem hohen Maße durch sichtbare Markteigenschaften erklärt wird. Wir zeigen, daß unsichtbare Order gegenüber sichtbaren Ordern signifikant stärkere Preisfluktuationen hervorrufen. Unsere Resultate geben Grund zu der Annahme, daß Märkte mit hoher unsichtbarer Liquidät volatiler sind und höheren Marktreibungen ausgesetzt sind. In Kapitel 2 entwickeln wir ein strukturelles Handelsmodell und untersuchen die optimale Handelsstrategie mit unsichtbaren Ordern. In diesem Rahmen leiten wir für verschiedene Marktspezifikationen explizite Charakterisierungen der sogenannten optimalen Exposure-Größe her. Unter anderem zeigen wir, daß der Einsatz unsichtbarer Order Transaktionskosten signifikant reduzieren kann. In Kapitel 3 entwickeln wir ein dynamisches, Gleichgewichtsmodell in einem Limitorderbuchmarkt. Innerhalb dieses theoretischen Rahmens können die empirischen Beobachtungen des ersten un zweiten Kapitels rationalisiert werden. Insbesondere zeigen wir daß große versteckte Order Marktineffizienzen hervorrufen und Preisfluktuationen verstärken, indem sie die Koordination zwischen Angebots- und Nachfrageseite schwächen können.
... Other studies discuss only partially the shape, dynamics or order splitting strategies. We refer here to studies by, e.g., Biais et al. [1995], Griffiths et al. [2000], Ahn et al. [2001], Ranaldo [2004], Hollifield et al. [2004], Bloomfield et al. [2005], Degryse et al. [2005], Hall and Hautsch [2006], Hall and Hautsch [2007], Large [2007], Hasbrouck and Saar [2009] and Cao et al. [2009]. In the central focus of recent literature are furthermore the analysis of liquidity risks, see, e.g., Johnson [2008], Liu [2009], Garvey and Wu [2009] or Goyenko et al. [2009], and the treatment of liquidity costs, see, e.g., Chacko et al. [2008] and Hasbrouck [2009]. ...
Moderne statistische und ökonometrische Methoden behandeln erfolgreich stilisierte Fakten auf den Finanzmärkten. Die vorgestellten Techniken erstreben die Dynamik von Finanzmarktdaten genauer als traditionelle Ansätze zu verstehen. Wirtschaftliche und finanzielle Vorteile sind erzielbar. Die Ergebnisse werden hier in praktischen Beispielen ausgewertet, die sich vor allem auf die Prognose von Finanzmarktdaten fokussieren. Unsere Anwendungen umfassen: (i) die Modellierung und die Vorhersage des Liquiditätsangebotes, (ii) die Lokalisierung des ’Multiplicative Error Model’ und (iii) die Erbringung von Evidenz für den empirischen Zustandsfaktorparadox über Landern.
... Therefore, an increase in buy-side market depth also makes limit orders to sell more attractive, as a market order in the opposite direction is more likely, and vice-versa. A positive relationship between increases in limit order book depth and increases in the use of market orders is supported empirically (Griffiths, Smith, Turnbull, and White, 2000;Ranaldo, 2004;Hall and Hautsch, 2006;Duong, Kalev, and Krishnamurti, 2009). ...
... • Using order book data from the Australian Stock Exchange, Hall and Hautsch (2006) found that market depth, the queued volume, the bidask spread, recent volatility, as well as recent changes in both the order flow and the price play an important role in explaining the determinants of order aggressiveness. In short, order book information plays the dominant role in explaining order aggressiveness. ...
An order book is a compiled list of orders (prices at which traders are willing to buy or sell) received. • Bollerslev and Domowitz (1993) used computer simulations to study the effects of varying the length of an electronic order book. They state that the appearance and increasing persistence of serial correlation in the vari-ance of transactions price returns is traced to the existence and length of the electronic book, as is the degree of non-normality in transactions returns. Whilst increases in the serial correlation of the market bid-ask spread as the book lengthens is isolated as one possible transmission mech-anism of serial dependence in the variance of transactions prices. • Hamao and Hasbrouck (1995) investigated the behaviour of intraday trades and quotes for individual stocks on the Tokyo Stock Exchange. They found that when orders that would otherwise walk through the limit order book are converted into limit orders, execution is delayed, but some orders ex-ecute (at least in part) at more favourable prices; an order that is held with an indicative quote has a larger cumulative price impact than one that is immediately executed in full; and after a market order is executed the quote hit by the market order generally tends to continue to move in the same direction (this is due in part to order autocorrelation and in part to the cancellation of limit orders). • Biais, Hillion and Spatt (1995) analysed the history of the order book for the 40 stocks in the CAC 40 and found evidence of information effects in the order process.
... To this end, we propose a data driven method to estimate the space basis ψ 1 (x), . . . , ψ K (x), motivated by Hall et al. (2006), which combines smoothing techniques with ideas related to functional principal component analysis. We summarize the basic steps as follows: 1 Estimate the covariance operator. ...
High dimensional nonstationary time series, which reveal both complex trends and stochastic behavior, occur in many scientific fields, e.g. macroeconomics, finance, neuro-economics, etc. To model them, we propose a generalized dynamic semiparametric factor model with a two-step estimation procedure. After choosing smoothed functional principal components as space functions (factor loadings), we extract various temporal trends by employing variable selection techniques for the time basis (common factors), and establish this estimator's non-asymptotic statistical properties under the dependent scenario (β-mixing and m-dependent) with the weakly cross-correlated error term. At the second step, we obtain a detrended low dimensional stochastic process that exhibits the dynamics of the original high dimensional (stochastic) objects and further justify statistical inference based on it. Crucially required for pricing weather derivatives, an analysis of temperature dynamics in China is presented to illustrate the performance of our method together with a simulation study designed to mimic it. This article is protected by copyright. All rights reserved.
... An increase in the depth on the same side of the market would indicate an increase in the competition for order execution resulting in dealers wanting to submit smaller, more aggressive orders to try to increase their likelihood of timely execution (e.g. Parlour (1998), Biais, Hillion and Spatt (1995) and Hautsch (2006 and). ...
This paper empirically examines how dispersions across investors beliefs influence traders order submission decisions in the foreign exchange market. Previous research has found that dispersion in traders beliefs regarding future macroeconomic announcements has a significant impact on both price dynamics and trading volume before the announcements in the foreign exchange and other financial markets. However, little is known about how this dispersion impacts traders choice in submitting different types of orders and thus to supply and demand liquidity either before or after such announcements. Since the types of orders submitted by traders at these times are the building blocks of the observed price and trading dynamics, it is important to understand how differences in investors' information sets before and after important macroeconomic announcements affect their order submission decisions. We find that (i) belief dispersion affects the size and aggressiveness of orders both before and after macroeconomic announcements, (ii) the magnitude of the impact of factors known to affect order choice depends on the level of belief dispersion, and (iii) the influence of information shocks (the revelation of unexpected information) on order choices depends on the level of belief dispersion.
... While BHS decomposition is seminal and remains popular, it lacks some dimensions. In a recent paper, Hall and Hautsch (2006) criticize the BHS classification for i) not being dynamic, ii) not distinguishing between trades and orders as well as cancellations whilst the former behave quite differently from the latter two 2 iii) ignoring the timing of orders 3 . We could further say that BHS classification is quite discrete, so cannot distinguish well between orders with different prices or quantities. ...
We give a new definition of order aggressiveness based jointly on three major concepts: time, price and quantity. Using correlations on an original dataset derived by reconstructing limit order book, we analyze to what level aggressiveness on one side affects the aggressiveness on both sides of trading. Besides, we provide further evidence of intraday effects in orders and trades.
... Based on their seminal work, many studies have concentrated on its further development in order to describe limit order book activities more accurately (for a survey, see Fernandes and Grammig (2006) or Hautsch (2004)). According to Cox and Isham (1980), alternative approaches to deal with point processes are count models (see Grammig, Heinen, and Rengifo (2004)) or intensity models (see Hall and Hautsch (2005)). Although these three types of models have been improved by many authors and shown a good performance in numerous previous studies, they still have their drawbacks and limits. ...
This paper suggests the application of advanced methods from Fourier Analysis in order to describe ultra-high frequent data in limit order books. Using Lomb's normalized periodogram and Scargle's Dis-crete Fourier Transforms (SDF T) to take account of the irregularity in spacing, the power spectra of different time series processes can be easily estimated. With empirical data extracted from the German XE-TRA system, the spectral analysis shows that the entire trading process contains various different periodic components. While duration and volume processes have a strong cyclical behavior in the low-frequency domain, seasonalities of price differences arise in the high-frequency do-main. Contrarily, the time series of the spread reveals no periodicity, neither in the long term, nor in the middle or short term.
... The order aggressiveness calibration follows the more (less) aggressive and smaller (larger) calibration; yet, we cannot observe the quotes below (above) the best fifth bid (ask), so the unobservable orders are substituted by the cancelled orders. 5 5 Hall and Hautsch (2006) also include cancelled orders in their study of the determinants of order aggressiveness. ...
ABSTRACT This study investigates the relationship between order aggressiveness and the distance between,stock market prices and price ,limits in an ,attempt to some ,shed light on the ‘heating’ and ‘cooling-off’ effects of price limits. As a stock approaches its price limits, distinctive dynamic,changes take place amongstinformed,and uninformed traders in their recognition of the risks arising from non-execution and adverse selection, which thereby reflect their specific levels of order aggressiveness. As such, in contrastto informed traders, uninformed traders (who account for the majority of traders) are easily manipulated by the price limit mechanism, a mechanism designed to suppress market overreaction. Using intraday data on the Taiwan Stock Exchange (TSE), in conjunction with piecewise ordered probit regressions, we find that a significant ‘inverted-N’ (‘N’) shape pattern is discernible on the ,sell (buy) side of the ,relationship between ,order aggressiveness and the price distance, which is consistent with the heating effect of
This paper employs order-, trade-, and quote-level data to examine the determinants of order choices and the impacts of order choices on execution quality by various investor types in the Taiwan Stock Exchange. We find marketable-quote orders have a higher degree of price aggressiveness, larger order size, higher trade value, shorter duration, and higher fill rate than behind-the-quote orders. There exists a transient order serial correlation. Different types of investors have their own preferences in order choices, while market microstructure factors, such as transitory volatility, spread, market depth, and trading interval, significantly influence stock traders’ order choices. Findings show that marketable-quote orders tend to perform better in terms of order duration. Moreover, institutional investors spend less time on completing their trades than do individuals, particularly for foreign investors after controlling all other factors.
We show that the excessive use of hidden orders causes artificial price pressures and abnormal asset returns. Using a simple game-theoretical setting, we demonstrate that this effect naturally arises from mis-coordination in trading schedules between traders, when suppliers of liquidity do not sufficiently disclose their trade intentions. As a result, hidden liquidity can increase trading costs and induce excess price fluctuations unrelated to information. Using NASDAQ order book data, we find strong empirical support and illustrate that hidden liquidity is higher if bid–ask spreads are smaller and relative tick sizes are higher.
We propose empirical measures of non-execution and picking-off risks and demonstrate that a minimum tick size reduction decreases non-execution risk but increases picking-off risk on the Tokyo Stock Exchange. This results in a higher tendency to submit aggressive orders for some stocks and cancel limit orders for the others. We conclude that our two limit order submission risks are crucial for understanding the results of past empirical studies that examine how minimum tick size reduction impacts limit order submission risks and why traders become aggressive in their order choice. We further show that our proposed measures of non-execution and picking-off risks are better variables than are proxies for the two risks such as spread (which have been suggested by previous empirical studies) or transaction cost measured by the relative tick size when analyzing the determination of the order choice and/or evaluating a minimum tick size reduction policy.
This study investigates the multiple events that occur in the life of each limit order by utilising a survival analysis methodology with a multiple-spell duration model. The estimates suggest that the hazard rates of limit order event transitions are determined by a number of factors and their impacts depend on whether the initial order event is a limit order submission, partial execution or revision. The differences in estimates across initial order events increase as exchange latency reduces in recent years. Using a multiple-spell duration model to examine the full spectrum of events that occur in the life of a limit order is thus shown to be informative and essential in modelling dynamic limit order placement strategies.
Forecasting the risk of extreme losses is an important issue in the management of financial risk and has attracted a great deal of research attention. However, little attention has been paid to extreme losses in a higher frequency intraday setting. This paper proposes a novel marked point process model to capture extreme risk in intraday returns, taking into account a range of trading activity and liquidity measures. A novel approach is proposed for defining the threshold upon which extreme events are identified taking into account the diurnal patterns in intraday trading activity. It is found that models including covariates, mainly relating to trading intensity and spreads offer the best in-sample fit, and prediction of extreme risk, in particular at higher quantiles.
This paper studies short-term liquidity withdrawal in the FX spot market for eight currency pairs. We include over 3 million limit order submissions, worth more than $5 trillion, and investigate the drivers of two different measures of volume-based liquidity. Overall, we find that market participants react differently to changes in the state of the market for different currency pairs. Moreover, the liquidity withdrawal process also differs depending on the perceived information content of new limit orders submitted. Finally, we document that a ‘liquidity illusion’ might exist in FX spot markets electronic trading platforms where algorithmic and high-frequency trading is prominent.
Recent empirical research has documented the clustered volatility and fat tails of return distribution in stock markets, yet returns are uncorrelated over time. Certain agent-based theoretical models attempt to explain the empirical features in terms of investors' order-splitting or dynamic switching strategies, both of which are frequently used by actual stock investors. However, little theoretical research has discriminated among the behavioral assumptions within a model and compared the impacts of the assumptions on the empirical features. Nor has the research simultaneously replicated the return features and empirical features on market microstructure, such as patterns of order choice. This study constructs an artificial limit order market in which investors split orders into small pieces or use fundamental and trend-following predictors interchangeably over time. We demonstrate that, on one hand, the market that features strategies with order splitting and dynamic predictor selection can independently replicate clustered volatility and fat tails with near-zero return autocorrelations. However, we also show that patterns of order choice do not match those found in certain previous empirical studies in both types of economies. Thus, we conclude that, in reality, the two strategies can work to generate the empirical return features but that investors may also use other strategies in actual stock markets. We also demonstrate that the impact of both strategies on the volatility persistence tends to be greater as the number of traders increases in the market; this finding implies that the order-splitting strategy and dynamic predictor selection are more crucial for the empirical phenomena pertaining to larger capital stocks.
Trading on major financial markets is typically conducted via electronic order books whose state is visible to market participants in real-time. A significant research literature has emerged concerning order book evolution, focussing on characteristics of the order book such as the time series of trade prices, movements in the bid-ask spread and changes in the depth of the order book at each price point. The latter two items can be characterised as order book shape where the book is viewed as a histogram with the size of the bar at each price point corresponding to the volume of shares demanded or offered for sale at that price. Order book shape is of interest to market participants as it provides insight as to current, and potentially future, market liquidity. Questions such as what shapes are commonly observed in order books and whether order books transition between certain shape patterns over time are of evident interest from both a theoretical and practical standpoint. In this study, using high-frequency equity data from the London Stock Exchange, we apply an unsupervised clustering methodology to determine clusters of common order book shapes, and also attempt to assess the transition probabilities between these clusters. Findings indicate that order books for individual stocks display intraday seasonality, exhibit some common patterns, and that transitions between order book patterns over sequential time periods is not random.
This paper examines the pattern of order aggressiveness, and the determinants of this pattern for institutional and retail brokers in the interval around monetary policy announcements. Utilizing a high-frequency dataset, with broker identifiers for each order submitted on the ASX over the period Dec 2007–Dec 2014, I identify a sharp increase in the number of orders submissions in the period following RBA announcements. Orders are more aggressive, and more abundant, when there is less information for investors to digest. On average, retail orders are more aggressive and are exclusively concerned with the likelihood of order execution. The submission decision of institutional brokers is more nuanced and evolves over time as market conditions change and information arrives. I also recognize differences in order aggressiveness attributable to firm-size and industry.
This paper analyzes adverse selection costs and price formation of bid-ask spread, dynamically adjusted by previous state of limit order book in an electronic limit order market. We develop an optimal revision model of price formation in which the market incorporates real-time information into the bid-ask spread to manage the related fluctuations of adverse selection risk and non-execution risk. A two-stage regression approach is applied to examine the dynamic order-submission behavior via market order aggressiveness as a proxy for the information precision of limit order book. Using order book data from the Taiwan Stock Exchange, our empirical analysis corroborates the following findings: (1) the state of the limit order book significantly affects subsequent order aggressiveness; (2) adverse selection cost and the spread are negatively associated with the precision of order book information; and (3) the information effects of limit order book on the bid-ask spread provide strong support for the model.
This thesis explores theoretical and empirical aspects of price formation and evolution at high frequency. We begin with the study of the joint dynamics of an option and its underlying. The high frequency data making observable the realized volatility process of the underlying, we want to know if this information is used to price options. We find that the market does not process this information to fix option prices. The stochastic volatility models are then to be considered as reduced form models. Nevertheless, this study tests the relevance of an empirical hedging parameter that we call effective delta. This is the slope of the regression of option price increments on those of the underlying. It proves to be a satisfactory model-independent hedging parameter. For the price dynamics, we turn our attention in the following chapters to more explicit models of market microstructure. One of the characteristics of the market activity is its clustering. Hawkes processes are point processes with this characteristic, therefore providing an adequate mathematical framework for the study of this activity. Moreover, the Markov property associated to these processes when the kernel is exponential allows to use powerful analytical tools such as the infinitesimal generator and the Dynkin formula to calculate various quantities related to them, such as moments or autocovariances of the number of events on a given interval. We begin with a monovariate framework, simple enough to illustrate the method, but rich enough to enable applications such as the clustering of arrival times of market orders, prediction of future market activity knowing past activity, or characterization of unusual shapes, but nevertheless observed, of signature plot, where the measured volatility decreases when the sampling frequency increases. Our calculations also allow us to make instantaneous calibration of the process by relying on the method of moments. The generalization to the multidimensional case then allow us to capture, besides the clustering, the phenomenon of mean reversion, which also characterizes the market activity observed in high frequency. General formulas for the signature plot are then obtained and used to connect its shape to the relative importance of clustering or mean reversion. Our calculations also allow to obtain the explicit form of the volatility associated with the diffusive limit, therefore connecting the dynamics at microscopic level to the macroscopic volatility, for example on a daily scale. Additionally, modelling buy and sell activity by Hawkes processes allows to calculate the market impact of a meta order on the asset price. We retrieve and explain the usual concave form of this impact as well as its relaxation with time. The analytical results obtained in the multivariate case provide the adequate framework for the study of the correlation. We then present generic results on the Epps effect as well as on the formation of the correlation and the lead lag.
In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bewiesen, dass die Bootstrap-Approximation eine wesentliche Verbesserung ergibt. Der Fall von mehrdimensionalen und diskrete Regressorvariablen wird mit Hilfe einer partiellen linearen Modell behandelt. Das Verfahren wird mithilfe der Arbeitsmarktanalysebeispiel erklärt. Hoch-dimensionale Zeitreihen, die nichtstationäre und eventuell periodische Verhalten zeigen, sind häufig in vielen Bereichen der Wissenschaft, zB Makroökonomie, Meteorologie, Medizin und Financial Engineering, getroffen. Der typische Modelierungsansatz ist die Modellierung von hochdimensionalen Zeitreihen in Zeit Ausbreitung der niedrig dimensionalen Zeitreihen und hoch-dimensionale zeitinvarianten Funktionen über dynamische Faktorenanalyse zu teilen. Wir schlagen ein zweistufiges Schätzverfahren. Im ersten Schritt entfernen wir den Langzeittrend der Zeitreihen durch Einbeziehung Zeitbasis von der Gruppe Lasso-Technik und wählen den Raumbasis mithilfe der funktionalen Hauptkomponentenanalyse aus. Wir zeigen die Eigenschaften dieser Schätzer unter den abhängigen Szenario. Im zweiten Schritt erhalten wir den trendbereinigten niedrig-dimensionalen stochastischen Prozess (stationär).
In this paper, I examine order submissions and cancellations in the Reuters Dealing 3000 Spot Matching System, the main order-driven market for interbank trading of the euro/z 3 oty (EUR/PLN) currency pair. I generalize the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) with respect to more than two competing risks. With the new multistate AACD model, I examine the timing of different order submissions and cancellations that take place on different sides of the market and vary according to their level of aggressiveness. I investigate different liquidity or information-oriented factors that exert an influence on the dynamics of the limit order book.
This paper investigates how the state of the order-book economy influences non-execution and picking-off risks. We utilize data from the limit order book and transactions in individual stocks on the Tokyo Stock Exchange. We demonstrate that, on the one hand, the risk of non-execution increases, while the risk of being picked off, on the other hand, decreases when: 1) the depth on the incoming investor’s side becomes thicker, 2) the bid-ask spread becomes narrower, 3) volatility declines, and 4) the depth on the opposite side to the incoming investor becomes thicker. In addition, we report asymmetric determinants of non-execution and picking-off risks between buy and sell limit orders, as well as among our sample firms. We interpret the asymmetry to be attributed to differences in transaction volume and order book thickness between buy and sell sides of the order book as well as among the firms. More transactions lead to higher quote competitions among limit order traders, increasing the thickness of the order book inside of the spread. It then decreases the rate of executions and of being picked off for limit orders existing outside of the spread. Our results suggest that real-time information on order book and transactions is highly valuable to stock investors, who trade individual securities and manage a portfolio of individual stocks, such as ETFs. Our findings assist real stock investors in reducing the monitoring cost, making more profitable order choices among market and limit orders and exposing/hiding/canceling/revising limit orders, and understanding the price formation process in an order-driven market. They are crucial for investors for better risk management in actual stock markets.
Course Description: This course is an introduction to financial trading strategies based on methods of statistical arbitrage. Topics include methodologies related to high frequency data, momentum strategies, pairs trading, technical analysis, models of order book dynamics and order placement and dynamic trade planning with feedback. Emphasis is on developing and automating the models that reflect the market and behavioral patterns. The course will be balanced between theory and practice with a sufficient theory to understand practical applications. Although the methodologies could be applied to various financial markets, the course will focus on stock and equity markets.
In this paper, we study the contribution to price discovery of market orders and limit orders with differing degrees of aggressiveness, focusing on how these contributions change with the level of volatility. Using intraday data from the Deutsche Mark – U.S. dollar foreign exchange market, we find significant price effects of market orders and both proximal and distant limit orders, with the relative contribution of limit orders, especially distant limit orders, to price formation increasing with volatility. Our analysis provides support for recent dynamic order submission models suggesting that both market and limit orders are used by informed traders, especially in periods of price uncertainty.
This paper analyzes the order aggressiveness and order submission strategies in the Chicago Board Option Exchange (CBOE) during the 2008 credit crisis. Using an ordered probit analysis with a sample of 300 million observations, we find that the investors are aggressive when (i) longer the order processing time, and (ii) the narrower the spread.
We investigate the effect of the removal of broker identities on institutional and individual order submissions on the Australian Stock Exchange (ASX). We document declines in order aggressiveness and effective spreads for both institutional and individual investors after the switch to the anonymous trading system. Institutions are more willing to improve the best quotes than individuals, especially in the anonymous market. Anonymity also reduces the “picked off” risk for individual limit orders. Overall, our findings highlight the benefits of withholding brokers' IDs in the form of lower transaction costs and higher liquidity supply and thus support the ASX's decision to stop disclosing broker identity information.
In this paper we propose an innovative measure for information flows between stock exchanges. We develop an intensity-based information share using Russell’s (1999) autoregressive conditional intensity model. Thereby we maintain the irregular nature of financial high frequency data and use durations and timing of price changes to determine the informationally dominant market. From our empirical application to US-listed Canadian stocks we conclude that the home market mostly reflects information first. On the basis of a cross-sectional analysis we find a positive correlation between the intensity-based information share and liquidity.
Recent years have witnessed a growing importance of quantitative methods in both financial research and industry. This development requires the use of advanced techniques on a theoretical and applied level, especially when it comes to the quantification of risk and the valuation of modern financial products. Applied Quantitative Finance (2nd edition) provides a comprehensive and state-of-the-art treatment of cutting-edge topics and methods. It provides solutions to and presents theoretical developments in many practical problems such as risk management, pricing of credit derivatives, quantification of volatility and copula modelling. The synthesis of theory and practice supported by computational tools is reflected in the selection of topics as well as in a finely tuned balance of scientific contributions on practical implementation and theoretical concepts. This linkage between theory and practice offers theoreticians insights into considerations of applicability and, vice versa, provides practitioners comfortable access to new techniques in quantitative finance. Themes that are dominant in current research and which are presented in this book include among others the valuation of Collaterized Debt Obligations (CDOs), the high-frequency analysis of market liquidity, the pricing of Bermuda options and realized volatility. All Quantlets for the calculation of the given examples are downloadable from the Springer web pages.
The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis. © 2012 Springer-Verlag Berlin Heidelberg. All rights are reserved.
We investigate the effects of competition and signaling in a pure order driven market and examine the trading patterns of agents when walking through the book is not allowed. Our results suggest that the variables capturing the cost of a large market order are not informative for an impatient trader under this market mechanism. We also document that the competition effect is not present only at the top of the book but persistent beyond the best quotes. Moreover, it dominates the signaling effect for both a limit order and a market order trader. Finally, we show that institutional investors' order submission strategies are characterized by only a few pieces of the limit order book information. This is consistent with informed traders placing orders based on their own private valuations rather than the state of the book.
Recent empirical research has documented that the state of the limit order book influences stock investors' strategies. Investors place more aggressive orders when the same side of the order book is thicker, and less aggressive orders when it is thinner. We conjecture and demonstrate that this behavior is related to long memories of trading volume, volatility, and order signs in stock markets. We investigate our conjecture in two types of artificial stock markets: a transparent market, in which agents observe all limit orders on both sides of the book and order volumes at those prices before they trade; and a less transparent market, in which agents observe only the best five bid and ask quotes with the depth available at these limit prices. The first market structure resembles certain actual stock exchanges in the level of pre-trade transparency, such as the Australian Stock Exchange, NYSE OpenBook, and the London Stock Exchange, whereas the second market structure is consistent with stock exchanges such as Euronext Paris, the Toronto Stock Exchange, the Tokyo Stock Exchange, and Hong Kong Exchanges and Clearing. We demonstrate that our long memory results are robust with different levels of pre-trade transparency, implying that the strategy constructed by the state of the order book is key for explaining long memories in many actual stock exchanges.
This article examines the dynamics of buy and sell liquidity provisions in the Tunisian stock exchange (BVMT) and uses high-frequency data from a reconstructed limit-order book. As an order-driven market, the BVMT relies on limit-order submissions to supply liquidity and ensure exchanges. Using an autoregressive conditional duration model, the study focused on the time dimension of the trading process. Applying a parsimonious methodology to the most liquid stocks traded in continuous session, the authors found that renewal frequency of liquidity supply depends on the state of the market, which is characterized by three types of variables: pre-transaction variables. such as spread, depth, limit-order imbalance, and transitory volatility; post-transaction variables, such as realized volatility, traded volume, order satisfaction rate; and pre-opening variables, such as opening volume, opening-closing volatility. The results confirm the microstructure theory predictions: Asymmetric information discourages liquidity provision. Traders consume liquidity when it is abundant and also supply liquidity when an immediate transaction is expensive. However, traders are more attentive to order volatility than to limit-order satisfaction rate.
In this paper we show how to reconstruct the limit order book of the 30 stocks constituting the DAX30 index based on the trading protocol of the Xetra Trading System at the Frankfurt Stock Exchange. The algorithm used is innovative as it captures all trading phases, including auctions, and delivers a reconstruction of the orderbook either from a trader's view or a supervisory view including hidden volume as well. Based on the rebuilt order book, liquidity dynamics are examined. In contrats to findings for dealer markets, past market returns play a minor role in the determination of liquidity and liquidity commonality in Xetra, a pure limit order book market. Consequently, we provide evidence that liquidity provision by multiple sources in Xetra mitigates systemic liquidity risk introduced by the interrelation of return and liquidity.
This paper introduces the logarithmic autoregressive conditional duration (Log-ACD) model and compares it with the ACD model of Engle and Russell [1998]. The logarithmic version allows to introduce in the model additional variables without sign restrictions on their coefficients. We apply the Log-ACD model to price durations relative to the bid-ask quote process of three securities listed on the New York Stock Exchange, and we investigate the influence of some characteristics of the trade process (trading intensity, average volume per trade and average spread) on the bid-ask quote process.
I extend the ACD model of Engle and Russell (1998) to generalized gamma du-rations with a conditional mean that depends on the exponential of the explana-tory variables. This allows for a non-monotonic hazard function taking U-shaped or inverted U-shaped forms. The extension implies that the trading intensity per-sistence is reduced considerably, and that the overall fit of the model is enhanced compared to the ACD model. As a further extension of the model it is shown how to include time-varying covariates in a fully parametric framework. We analyze how transaction rates are affected by the posting of price-quotes and their changes. Besides, a model of the time between price-changes is estimated. This model is, as shown by Engle and Russell (1998), closely linked to the volatility of the stock price, and hence showing why price durations are important for intra-day prediction of volatility. The transaction volume and functions of this are used as regressors in this model and are found to be important. The datasets used in the paper consist of a random sample from the fifty stocks at the NYSE with the highest capitalization value on December 13, 1996.
We model a trader’s decision to supply liquidity by submitting limit orders or demand liquidity by submitting market orders in a limit order market. The best quotes and the execution probabilities and picking off risks of limit orders determine the price of immediacy. The price of immediacy and the trader’s willingness to pay for immediacy determine the trader’s optimal order submission, with the trader’s willingness to pay for immediacy depending on the trader’s valuation for the stock. We estimate the execution probabilities and the picking off risks using a sample from the Vancouver Stock Exchange to compute the price of immediacy. The price of immediacy changes with market conditions — a trader’s optimal order submission changes with market conditions. We combine the price of immediacy with the actual order submissions to estimate the unobserved arrival rates of traders and the distribution of the traders’ valuations. High realized stock volatility increases the arrival rate of traders and increases the number of value traders arriving — liquidity supply is more competitive after periods of high volatility. An increase in the spread decreases the arrival rate of traders and decreases the number of value traders arriving — liquidity supply is less competitive when the spread widens.
This paper shows that the monotonicity of the conditional hazard in traditional ACD models is both econometrically important and empirically invalid. To counter this problem we introduce a more flexible parametric model which is easy to fit and performs well both in simulation studies and in practice. In an empirical application to NYSE price duration processes, we show that non-monotonic conditional hazard functions are indicated for all stocks. Recently proposed specification tests for financial duration models clearly reject the standard ACD models, whereas the results for the new model are quite favorable.
By considering investor order placement strategy, this paper demonstrates that transaction costs cause bid-ask spreads to be an equilibrium property of asset markets. With transaction costs, the probability of a limit order executing does not go to unity as the order is placed infinitesimally close to a counterpart market quote; thus, with certainty of execution at the counterpart market quote, a "gravitational pull" is generated that keeps counterpart quotes from being placed infinitesimally close to each other. An equilibrium spread is defined and its size linked to market thinness; implications are noted for the design of a trading system.
The recent advent of high frequency data provides researchers with transaction by transaction level data. Examples include scanner data from grocery stores or financial transactions data. These new data sets allow researchers to take an unprecedented look at the underlying economic structure of the markets. Often the hypothesis of interest is in the context of multivariate time series data. Analysis of multivariate high frequency data is complicated by the fact that the multiple processes are irregularly spaced in time with different arrival rates. This has lead many investigators to work with aggregated data which can blur the market structure and contaminate the analysis. In this paper we propose a new method of working with the dissaggregated data and develop an econometric model for the arrival rates of multivariate dependent point processes. We apply the model to financial transactions data and estimate models for the bivariate point process of transaction and limit order arrival times. Since limit orders dictate the structure of the limit order book they are a direct determinant of market liquidity. To our knowledge little work has examined the dynamic structure of limit order submissions. Since transactions represent a floor trader or market order demand for liquidity the bivariate model characterizes the dynamic behavior of liquidity supply and demand. The proposed model also allows for marks, or characteristics associated with the arrival times, to influence future arrival rates. For the stock market data analyzed we find strong evidence of codependence in the two processes and both liquidity demand and supply are influenced by past volume and prevailing spreads.
This paper proposes a new statistical model for the analysis of data which arrive at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point processes with dependent arrival rates. The conditional intensity is developed and compared with other self-exciting processes. Because the model focuses on the expected duration between events, it is called the autoregressive conditional duration (ACD) model. Asymptotic properties of the quasi maximum likelihood estimator are developed as a corollary to ARCH model results. Strong evidence is provided for duration clustering for the financial transaction data analyzed; both deterministic time-of-day effects and stochastic effects are important. The model is applied to the arrival times of trades and therefore is a model of transaction volume, and also to the arrival of other events such as price changes. Models for the volatility of prices are estimated with price-based durations, and examined from a market microstructure point of view.
This article provides a game theoretic model of price formation and order placement decisions in a dynamic limit order market. Investors can choose to either post limit orders or submit market orders. Limit orders result in better execution prices but face a risk of non-execution and a winner's curse problem. Solving for the equilibrium of this dynamic game, closed-form solutions for the order placement strategies are obtained. Thus, testable implications for the cross-sectional behavior of the mix between market and limit orders and trading costs in limit order markets are derived. 1999 Elsevier Science B.V. All rights reserved.
We assess the informational content of an open limit order book from three directions: (1) Does the limit order book allow better inferences about a security's value than simply the best bid and offer prices from the first step of the book? If it does, how much additional information can be gleaned from the book? (2) Are imbalances between the demand and supply schedules informative about future price movements? and (3) Does the shape of the limit order book impact traders' order submission strategies? Our empirical evidence suggests that the order book beyond the first step is informative - its information share is about 30%. The imbalance between demand and supply from step 2 to 10 provides additional power in explaining future short-term returns. Finally, traders do use the available information on the state of the book, not only from the first step, but also from other steps, when developing their order submission strategies.
This study derives optimal dynamic order submission strategies for trading problems faced by three stylized traders: an uninformed liquidity trader, an informed trader and a value-motivated trader. Separate solutions are obtained for quote- and order-driven markets. The results provide practicable rules for how to trade small orders and how to manage traders. Transaction cost measurement methods based on implementation shortfall are proven to dominate other methods.
Since investors demand liquidity when they submit market orders and supply liquidity when they submit limit orders, the results improve our understanding of market liquidity. In particular, the models illustrate the role of time in the search for liquidity by characterizing the demand for and supply of immediacy.
This paper discusses performance measures for market and limit orders. We suggest two measures: one for precommitted traders (who must trade) and another for passive traders (who are indifferent to trading). We compute these measures for a sample of NYSE SuperDOT orders. The results suggest that the limit order placement strategies most commonly used by NYSE SuperDOT traders do in fact perform best. Limit orders placed at or better than the prevailing quote perform better than do market orders, even after imputing a penalty for unexecuted orders, and after taking into account market-order price improvement. Unconditional order submission strategies that use SuperDOT to offer liquidity in competition with the specialist do not appear to be profitable.
We offer a statistical model of the order flow and estimate it using high frequency data from the Paris Bourse. Our model jointly explains the duration between two consecutive orders and the relative aggressiveness of the orders, depending upon the past ordes and the state of the book. Our results offer evidence of information and liquidity effects, as put forward by market microstructure theories. /// Dans cet article, nous construisons et estimons un modèle du flux des ordres boursiers. Pour ce faire, nous utilisons des données à haute fréquence décrivant les ordres émis par les participants du marché automatisé de la Bourse de Paris. Notre modèle explique conjointement la durée séparant deux ordres et l'agressivité des ordres émis, en fonction des ordres passés et de l'état du carnet d'ordre. Les résultats confortent un certain nombre d'implications empiriques de la théorie de la microstructure des marchés financiers.
This paper examines the costs and determinants of order aggressiveness. Aggressive orders have larger price impacts but smaller opportunity costs than passive orders. Price impacts are amplified by large orders, small firms, and volatile stock prices. To minimize the implementation shortfall, the optimal strategy is to enter buy (sell) orders at the bid (ask). Aggressive buy (sell) orders tend to follow other aggressive buy (sell) orders and occur when bid–ask spreads are narrow and depth on the same (opposite) side of the limit book is large (small). Aggressive buys are more likely than sells to be motivated by information.
This paper presents a new model that improves upon several inadequacies of the original autoregressive conditional duration (ACD) model considered in Engle and Russell (Econometrica 66(5) (1998) 1127–1162). We propose a threshold autoregressive conditional duration (TACD) model to allow the expected duration to depend nonlinearly on past information variables. Conditions for the TACD process to be ergodic and existence of moments are established. Strong evidence is provided to suggest that fast transacting periods and slow transacting periods of NYSE stocks have quite different dynamics. Based on the improved model, we identify multiple structural breaks in the transaction duration data considered, and those break points match nicely with real economic events.
The microstructure of the Saudi Stock Market (SSM) under the new computerized trading system, ESIS, is described, and order and other generated data sets are used to examine the patterns in the order book, the dynamics of order flow, and the probability of executing limit orders. Although the SSM has a distinct structure, its intraday patterns are surprisingly similar to those found in other markets with different structures. We find that liquidity, as commonly measured by width and depth, is relatively low on the SSM. However, liquidity is exceptionally high when measured by immediacy. Limit orders that are priced reasonably, on average, have a short duration before being executed, and have a high probability of subsequent execution.
I examine the information content of a limit order book in a purely order-driven market. I analyze how the state of the limit order book affects a trader's strategy. I develop an econometric technique to study order aggressiveness and provide empirical evidence on the recent theoretical models on limit order book markets. My results show that patient traders become more aggressive when the own (opposite) side book is thicker (thinner), the spread wider, and the temporary volatility increases. Also, I find that the buy and the sell sides of the book affect the order submission differently.
This paper models quote setting and price formation in a non-intermediated, order driven market where trading occurs because investors differ in their share valuations and the advent of news that is not common knowledge, and tests the model using transaction data on individual stocks in the ParisBourse CAC40 index. As an extension of Foucault (1999), we show that the size of the spread is a function of the differences in valuation among investors and of adverse selection. Both GMM estimation of the model parameters and empirical evidence on spread behavior as the relative proportion of buyers and sellers in the market changes, provide strong support for the model. Our analysis yields further insight into the dynamic process of price formation and into the market clearing process in an order driven market.
We introduce a class of models for the analysis of durations, which we call stochastic conditional duration (SCD) models. These models are based on the assumption that the durations are generated by a dynamic stochastic latent variable. The model yields a wide range of shapes of hazard functions. The estimation of the parameters is performed by quasi-maximum likelihood and using the Kalman filter. The model is applied to trade, price and volume durations of stocks traded at NYSE. We also investigate the relation between price durations, spread, trade intensity and volume.
Thesis (doctoral)--Universität, Konstanz. Includes bibliographical references (p. [273]-283) and index.
Forecasting ability of several parameterizations of ACD models are compared to benchmark linear autoregressions for inter-trade durations. The estimation of parametric ACD models requires both the choice of a conditional density for durations and the specification of a functional form for the conditional mean duration. Our results provide guidance for choosing among different parameterizations and for developing better forecasting models to predict one-step-ahead, multi-step-ahead, and the whole density of time durations. For evaluating density forecasts, we propose a new constructive test, which is based on the series of probability integral transforms. The choice of the conditional distribution for inter-trade durations does not seem to affect the out-of sample performances of the ACD at short, as well as longer, horizons. Yet, this choice becomes critical when forecasting the density.
This paper introduces a new framework for the dynamic modelling of univariate and multivariate point processes. The so-called latent factor intensity (LFI) model is based on the assumption that the intensity function consists of univariate or multivariate observation driven dynamic components and a univariate dynamic latent factor. In this sense, the model corresponds to a dynamic extension of a doubly stochastic Poisson process. We illustrate alternative parameterizations of the observation driven component based on autoregressive conditional intensity (ACI) specifications, as well as Hawkes types models. Based on simulation studies, it is shown that the proposed model provides a flexible tool to capture the joint dynamics of multivariate point processes. Since the latent component has to be integrated out, the model is estimated by simulated maximum likelihood based upon efficient importance sampling techniques. Applications of univariate and bivariate LFI models to transaction data extracted from the German XETRA trading system provide evidence for an improvement of the econometric specification when observable as well as unobservable dynamic components are taken into account.
This article presents a one-tick dynamic model of a limit order market. Agents choose to submit a limit order or a market
order depending on the state of the limit order book. Each trader knows that her order will affect the order placement strategies
of those who follow and the execution probability of her limit order is endogenous. All traders take this into account which,
in equilibrium, generates systematic patterns in transaction prices and order placement strategies even with no asymmetric
information.
This article presents a microstructure model of liquidity provision in which a specialist with market power competes against a competitive limit order book. General solutions, comparative statics and examples are provided first with uninformative orders and then when order flows are informative. The model is also used to address two optimal market design issues. The first is the effect of "tick" size--for example, eighths versus decimal pricing--on market liquidity. Institutions trading large blocks have a larger optimal tick size than small retail investors, but both prefer a tick size strictly greater than zero. Second, a hybrid specialist/limit order market (like the NYSE) provides better liquidity to small retail and institutional trades, but a pure limit order market (like the Paris Bourse) may offer better liquidity on mid-size orders. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.
A continuous time econometric modelling framework for multivariate market event (or 'transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new information arrives. We introduce the class of generalised Hawkes models which allow the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and we show how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time scale. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time bivariate point process model of the timing of trades and mid-quote changes is presented for a NYSE stock and the empirical findings are related to the theoretical and empirical market microstructure literature.
In this paper, we investigate the buy and sell arrival process in a limit order book market. Using an intensity framework allows to estimate the simultaneous buy and sell intensity and to derive a continuous-time measure for the buy-sell pressure in the market. Based on limit order book data from the Australian Stock Exchange (ASX), we show that the buy-sell pressure is particularly influenced by recent market and limit orders and the current depth in the ask and bid queue. We find evidence for the hypothesis that traders use order book information in order to infer from the price setting behavior of market participants. Furthermore, our results indicate that the buy-sell pressure is clearly predictable and is a significant determinant of trade-to-trade returns and volatility.
A new model for the analysis of durations, the stochastic conditional dusration (SCD) model is introduced. This model is based on the assumption that the durations are generated by a latent stochastic factor that follows a first order autoregressive process. The lattent factor is perturbed multiplicatively by an innovation distributed as Weibull or gamma variable.
The authors analyze the rationale for limit order trading. Use of limit orders involves two risks: (1) an adverse information event can trigger an undesirable execution, and (2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. The authors' empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well-balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable. Copyright 1996 by American Finance Association.
Most financial markets allow investors to submit both limit and market orders, but it is not always clear what affects the choice of order type. The authors empirically investigate how the time between order submissions, changes in the state of the order book, and price uncertainty influence the rate of submission of limit and market orders. The authors measure the expected time (duration) between the submissions of orders of each type using an asymmetric autoregressive conditional duration model. They find that the execution of market orders, as well as changes in the level of price uncertainty and market depth, impact the submissions of both best limit orders and market orders. After correcting for these factors, the authors also find differences in behaviour around market openings, closings, and unexpected events that may be related to changes in information flows at these times. In general, traders use more market (limit) orders at times when execution risk for limit orders is highest or the risk of unexpected price movements is highest.
This paper proposes a new statistical model for the analysis of data which arrives at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point processes with dependent arrival rates. The conditional intensity is developed and compared with other self-exciting processes. The model is applied to the arrival times of financial transactions and therefore is a model of transaction volume, and also to the arrival of other events such as price changes. Models for the volatility of prices are estimated, and examined from a market microstructure point of view.
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.
As a centralized, computerized, limit order market, the Paris Bourse is particularly appropriate for studying the interaction between the order book and order flow. Descriptive methods capture the richness of the data and distinctive aspects of the market structure. Order flow is concentrated near the quote, while the depth of the book is somewhat larger at nearby valuations. We analyze the supply and demand of liquidity. For example, thin books elicit orders and thick books result in trades. To gain price and time priority, investors quickly place orders within the quotes when the depth at the quotes or the spread is large. Consistent with information effects, downward (upward) shifts in both bid and ask quotes occur after large sales (purchases). Copyright 1995 by American Finance Association.
Under fairly general conditions, this article derives the equilibrium price schedule determined by the bids and offers in an open limit order book. The analysis shows that the order book has a small-trade positive bid-ask spread, and limit orders profit from small trades; the electronic exchange provides as much liquidity as possible in extreme situations; the limit order book does not invite competition from third market dealers, while other trading institutions do; and, if an entering exchange earns nonnegative trading profits, the consolidated price schedule matches the limit order book price schedule. Copyright 1994 by American Finance Association.
This paper analyzes the relationship between banks’ divergent strategies toward specialization and diversification of financial activities and their ability to withstand a banking sector crash. We first generate market-based measures of banks’ systemic risk exposures using extreme value analysis. Systemic banking risk is measured as the tail beta, which equals the probability of a sharp decline in a bank’s stock price conditional on a crash in a banking index. Subsequently, the impact of (the correlation between) interest income and the components of non-interest income on this risk measure is assessed. The heterogeneity in extreme bank risk is attributed to differences in the scope of non-traditional banking activities: non-interest generating activities increase banks’ tail beta. In addition, smaller banks and better-capitalized banks are better able to withstand extremely adverse conditions. These relationships are stronger during turbulent times compared to normal economic conditions. Overall, diversifying financial activities under one umbrella institution does not improve banking system stability, which may explain why financial conglomerates trade at a discount.
An Empirical Analysis of Trades, Orders, and Cancellations in a Limit Order Market, Discussion paper The ACD Model: Predictability of the Time between Consecutive Trades, Discussion paper Autoregressive conditional duration: a new model for irregularly spaced transaction data
- M Domowitz
M, Domowitz I (2002) An Empirical Analysis of Trades, Orders, and Cancellations in a Limit Order Market, Discussion paper, Duke University Dufour A, Engle RF (2000) The ACD Model: Predictability of the Time between Consecutive Trades, Discussion paper, ISMA Centre, University of Reading Engle RF, Russell JR (1998) Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 66:1127–1162
A Family of Autoregressive Conditional Duration Models, Discussion Paper CORE, Université Catholique de Louvain Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market Is the electronic open limit order book inevitable
- M Grammig
M, Grammig J (2001) A Family of Autoregressive Conditional Duration Models, Discussion Paper 2001/31, CORE, Université Catholique de Louvain Foucault T (1999) Order flow composition and trading costs in a dynamic limit order market. J Financ Mark 2:99–134 Glosten LR (1994) Is the electronic open limit order book inevitable. J Financ 49:1127–1161
An Empirical Analysis of Trades, Orders, and Cancellations in a Limit Order Market, Discussion paper
- M Coppejans
- I Domowitz
Liquidity supply and demand in limit order markets, Discussion paper, Centre for Economic Policy Research
- B Hollifield
- Ra Miller
- P Sandås
- J Slive
What Pieces of Limit Order Book Information are Informative? Discussion Paper
- R Pascual
- D Veredas