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on Market Microstructure |
| By: | Yacine A\"it-Sahalia; Jialin Yu |
| Abstract: | Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable characteristics of the underlying stocks and, in particular, to different financial measures of their liquidity. We find that more liquid stocks based on financial characteristics have lower noise and noise-to-signal ratio measured from their high frequency returns. We then examine whether there exists a common, market-wide, factor in high frequency stock-level measurements of noise, and whether that factor is priced in asset returns. |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0906.1444&r=mst |
| By: | Terrence Hendershott (UC Berkeley); Ryan Riordan (Karlsruhe Institute of Technology) |
| Abstract: | We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is cheap and supply liquidity when it is expensive. AT contribute more to the efficient price by placing more efficient quotes and AT demanding liquidity to move the prices towards the efficient price. |
| Keywords: | Algorithmic trading, information technology, price discovery, market microstructure, price efficiency |
| JEL: | D4 D8 G1 |
| Date: | 2009–03 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:0908&r=mst |
| By: | Paolo Pellizzari (Department of Applied Mathematics, University of Venice); Frank Westerhoff (Department of Economics, Bamberg - Germany) |
| Abstract: | We show that the effectiveness of transaction taxes depends on the market microstructure. Within our model, heterogeneous traders use a blend of technical and fundamental trading strategies to determine their orders. In addition, they may become inactive if the profitability of trading decreases. We find that in a continuous double auction market the imposition of a transaction tax is not likely to stabilize financial markets since a reduction in market liquidity amplifies the average price impact of a given order. In a dealership market, however, abundant liquidity is provided by specialists, and thus a transaction tax may reduce volatility by crowding out speculative orders. |
| Keywords: | Transaction tax, Tobin tax, microstructures, agent-based models, liquidity. |
| JEL: | H20 C63 D44 |
| Date: | 2009–09 |
| URL: | http://d.repec.org/n?u=RePEc:vnm:wpaper:190&r=mst |
| By: | Wolfgang Karl Härdle (Humboldt Universität zu Berlin and National Central University, Taiwan); Nikolaus Hautsch (Humboldt Universität zu Berlin, Quantitative Products Laboratory, Berlin, and CFS); Andrija Mihoci (Humboldt Universität zu Berlin and University of Zagreb, Croatia) |
| Abstract: | We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are modelled using a vector autoregressive model. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model’s forecasting power can be used to improve optimal order execution strategies. |
| Keywords: | Limit Order Book, Liquidity Risk, Semiparametric Model, Factor Structure, Prediction |
| JEL: | C14 C32 C53 G1 |
| Date: | 2009–09–15 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200918&r=mst |
| By: | Wolfgang Karl Härdle; Nikolaus Hautsch; Andrija Mihoci |
| Abstract: | We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are modelled using a vector autoregressive model. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model’s forecasting power can be used to improve optimal order execution strategies. |
| Keywords: | limit order book, liquidity risk, semiparametric model, factor structure, prediction |
| JEL: | C14 C32 C53 G11 |
| Date: | 2009–09 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2009-044&r=mst |
| By: | Gao-Feng Gu; Fei Ren; Xiao-Hui Ni; Wei Chen; Wei-Xing Zhou |
| Abstract: | We study the statistical regularities of opening call auction using the ultra-high-frequency data of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. The distribution of the relative price, defined as the relative difference between the order price in opening call auction and the closing price of last trading day, is asymmetric and that the distribution displays a sharp peak at zero relative price and a relatively wide peak at negative relative price. The detrended fluctuation analysis (DFA) method is adopted to investigate the long-term memory of relative order prices. We further study the statistical regularities of order sizes in opening call auction, and observe a phenomenon of number preference, known as order size clustering. The probability density function (PDF) of order sizes could be well fitted by a $q$-Gamma function, and the long-term memory also exists in order sizes. In addition, both the average volume and the average number of orders decrease exponentially with the price level away from the best bid or ask price level in the limit-order book (LOB) established immediately after the opening call auction, and a price clustering phenomenon is observed. |
| Date: | 2009–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0905.0582&r=mst |
| By: | Manabu Asai (Faculty of Economics, Soka University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Marcelo C. Medeiros (Department of Economics, Pontifical Catholic University of Rio de Janeiro) |
| Abstract: | Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent (modified) realized volatility (RV) estimates of the integrated volatility can contain residual microstructure noise and other measurement errors. Such noise is called "realized volatility error". As such measurement errors ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that: (i) neglecting RV errors can lead to serious bias in estimators due to model misspecification; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and (iii) even the partially corrected R2 recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction of R2 , which can be applied to linear and nonlinear, short and long memory models. An empirical example for &P 500 data is used to demonstrate that neglecting RV errors can lead to serious bias in estimating the model of integrated volatility, and that the new method proposed here can eliminate the effects of the RV noise. The empirical results also show that the full correction for R2 is necessary for an accurate description of goodness-of-fit. |
| Date: | 2009–09 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2009cf669&r=mst |
| By: | Fei Ren; Gao-Feng Gu; Wei-Xing Zhou |
| Abstract: | We perform return interval analysis of 1-min {\em{realized volatility}} defined by the sum of absolute high-frequency intraday returns for the Shanghai Stock Exchange Composite Index (SSEC) and 22 constituent stocks of SSEC. The scaling behavior and memory effect of the return intervals between successive realized volatilities above a certain threshold $q$ are carefully investigated. In comparison with the volatility defined by the closest tick prices to the minute marks, the return interval distribution for the realized volatility shows a better scaling behavior since 20 stocks (out of 22 stocks) and the SSEC pass the Kolmogorov-Smirnov (KS) test and exhibit scaling behaviors, among which the scaling function for 8 stocks could be approximated well by a stretched exponential distribution revealed by the KS goodness-of-fit test under the significance level of 5%. The improved scaling behavior is further confirmed by the relation between the fitted exponent $\gamma$ and the threshold $q$. In addition, the similarity of the return interval distributions for different stocks is also observed for the realized volatility. The investigation of the conditional probability distribution and the detrended fluctuation analysis (DFA) show that both short-term and long-term memory exists in the return intervals of realized volatility. |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0904.1107&r=mst |
| By: | Esteban Moro; Javier Vicente; Luis G. Moyano; Austin Gerig; J. Doyne Farmer; Gabriella Vaglica; Fabrizio Lillo; Rosario N. Mantegna |
| Abstract: | We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power-law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order. |
| Date: | 2009–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0908.0202&r=mst |
| By: | Amparo Baillo |
| Abstract: | Bollerslev et al. (2006) study the cross-covariances for squared returns under the Heston (1993) stochastic volatility model. In order to obtain these cross-covariances the authors use an incorrect expression for the distribution of the squared returns. Here we will obtain the correct distribution of the squared returns and check that, under this new distribution, the result in Appendix A.2 in Bollerslev et al. (2006) still holds. |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0902.0713&r=mst |
| By: | Bence Toth; Janos Kertesz; J. Doyne Farmer |
| Abstract: | We study the dynamics of the limit order book of liquid stocks after experiencing large intra-day price changes. In the data we find large variations in several microscopical measures, e.g., the volatility the bid-ask spread, the bid-ask imbalance, the number of queuing limit orders, the activity (number and volume) of limit orders placed and canceled, etc. The relaxation of the quantities is generally very slow that can be described by a power law of exponent $\approx0.4$. We introduce a numerical model in order to understand the empirical results better. We find that with a zero intelligence deposition model of the order flow the empirical results can be reproduced qualitatively. This suggests that the slow relaxations might not be results of agents' strategic behaviour. Studying the difference between the exponents found empirically and numerically helps us to better identify the role of strategic behaviour in the phenomena. |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0901.0495&r=mst |
| By: | Zoltan Eisler; Jean-Philippe Bouchaud; Julien Kockelkoren |
| Abstract: | While the long-ranged correlation of market orders and their impact on prices has been relatively well studied in the literature, the corresponding studies of limit orders and cancellations are scarce. We provide here an empirical study of the cross-correlation between all these different events, and their respective impact on future price changes. We define and extract from the data the "bare" impact these events would have, if they were to happen in isolation. For large tick stocks, we show that a model where the bare impact of all events is permanent and non-fluctuating is in good agreement with the data. For small tick stocks, however, bare impacts must contain a history dependent part, reflecting the internal fluctuations of the order book. We show that this effect can be accurately described by an autoregressive model on the past order flow. This framework allows us to decompose the impact of an event into three parts: an instantaneous jump component, the modification of the future rates of the different events, and the modification of the future gaps behind the best quotes. We compare in detail the present formalism with the temporary impact model that was proposed earlier to describe the impact of market orders when other types of events are not observed. Finally, we extend the model to describe the dynamics of the bid-ask spread. |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0904.0900&r=mst |
| By: | A. Zaccaria; M. Cristelli; V. Alfi; F. Ciulla; L. Pietronero |
| Abstract: | We show that the statistics of spreads in real order books is characterized by an intrinsic asymmetry due to discreteness effects for even or odd values of the. An analysis of data from the NYSE order book point out that traders' strategies contribute to this asymmetry. We also investigate this phenomenon in the framework of a microscopic model and, by introducing a non-uniform deposition mechanism for limit orders, we are able to quantitatively reproduce the asymmetry found in the experimental data. Monte Carlo simulations of our model also show a realistic dynamics with a sort of intermittent behavior characterized by long periods in which the order book is compact and liquid interrupted by volatile and sparse configurations. |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0906.1387&r=mst |
| By: | Austin Gerig; Javier Vicente; Miguel A. Fuentes |
| Abstract: | Stock prices are known to exhibit non-Gaussian dynamics, and there is much interest in understanding the origin of this behavior. Here, we present a simple model that explains the shape and scaling of the distribution of intraday stock price fluctuations (called intraday returns) and verify the model using a large database for several stocks traded on the London Stock Exchange. We provide evidence that the return distribution for these stocks is non-Gaussian and similar in shape, and that the distribution appears stable over intraday time scales. We explain these results by assuming the volatility of returns is constant intraday, but varies over longer periods such that its inverse square follows a gamma distribution. This produces returns that are Student t-distributed for intraday time scales. The predicted results show excellent agreement with the data for all stocks in our study and over all regions of the return distribution. |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0906.3841&r=mst |
| By: | Daniel J. Fenn; Mason A. Porter; Peter J. Mucha; Mark McDonald; Stacy Williams; Neil F. Johnson; Nick S. Jones |
| Abstract: | We use techniques from network science to study correlations in the foreign exchange (FX) market over the period 1991--2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clustering of the exchange rate time series, we investigate dynamic communities in the network. We show that there is a relationship between an exchange rate's functional role within the market and its position within its community and use a node-centric community analysis to track the time dynamics of this role. This reveals which exchange rates dominate the market at particular times and also identifies exchange rates that experienced significant changes in market role. We also use the community dynamics to uncover major structural changes that occurred in the FX market. Our techniques are general and will be similarly useful for investigating correlations in other markets. |
| Date: | 2009–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:0905.4912&r=mst |