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on Market Microstructure |
| By: | Seungki Min; Costis Maglaras; Ciamac C. Moallemi |
| Abstract: | The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks, and (ii) this correlation increases during the last few hours of the trading session. These observations could be attributed, in part, to the prevalence of portfolio trading activity that is implicit in the growth of ETF, passive and systematic investment strategies; and, to the increased trading intensity of such strategies towards the end of the trading session, e.g., due to execution of mutual fund inflows/outflows that are benchmarked to the closing price on each day. In this paper, we investigate the consequences of such portfolio liquidity on price impact and portfolio execution. We derive a linear cross-asset market impact from a stylized model that explicitly captures the fact that a certain fraction of natural liquidity providers only trade portfolios of stocks whenever they choose to execute. We find that due to cross-impact and its intraday variation, it is optimal for a risk-neutral, cost minimizing liquidator to execute a portfolio of orders in a coupled manner, as opposed to a separable VWAP-like execution that is often assumed. The optimal schedule couples the execution of the various orders so as to be able to take advantage of increased portfolio liquidity towards the end of the day. A worst case analysis shows that the potential cost reduction from this optimized execution schedule over the separable approach can be as high as 6% for plausible model parameters. Finally, we discuss how to estimate cross-sectional price impact if one had a dataset of realized portfolio transaction records that exploits the low-rank structure of its coefficient matrix suggested by our analysis. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.05524&r=all |
| By: | Miko{\l}aj Bi\'nkowski; Charles-Albert Lehalle |
| Abstract: | In this paper we investigate the endogenous information contained in four liquidity variables at a five minutes time scale on equity markets around the world: the traded volume, the bid-ask spread, the volatility and the volume at first limits of the orderbook. In the spirit of Granger causality, we measure the level of information by the level of accuracy of linear autoregressive models. This empirical study is carried out on a dataset of more than 300 stocks from four different markets (US, UK, Japan and Hong Kong) from a period of over five years. We discuss the obtained performances of autoregressive (AR) models on stationarized versions of the variables, focusing on explaining the observed differences between stocks. Since empirical studies are often conducted at this time scale, we believe it is of paramount importance to document endogenous dynamics in a simple framework with no addition of supplemental information. Our study can hence be used as a benchmark to identify exogenous effects. On the other hand, most optimal trading frameworks (like the celebrated Almgren and Chriss one), focus on computing an optimal trading speed at a frequency close to the one we consider. Such frameworks very often take i.i.d. assumptions on liquidity variables; this paper document the auto-correlations emerging from real data, opening the door to new developments in optimal trading. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.03766&r=all |
| By: | Peter Kondor (Centre for Economic Policy Research (CEPR); London School of Economics (LSE)); Gabor Pinter (Bank of England; Centre for Macroeconomics (CFM)) |
| Abstract: | In government bond markets the number of dealers with whom clients trade changes through time. Our paper shows that this time-variation in clients’ connections serves as a proxy for time-variation in private information. Using proprietary data covering close to all dealer-client transactions in the UK government bond market, we show that clients have systematically better performance when trading with more dealers, and this effect is stronger during macroeconomic announcements. Most of the effect comes from clients’ increased ability to predict future yield changes (anticipation component) rather than these clients facing tighter bid-ask spreads (transaction component). To explore the nature of this private information, we find that clients with increased dealer connections can better predict the fraction of the aggregate order flow that is intermediated by dealers they regularly trade with. Positive trading performance is concentrated in those periods when clients have more dealer connections than usual. |
| Keywords: | Government bond market, Private information, Client-dealer Connections |
| JEL: | G12 G14 G24 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:cfm:wpaper:1901&r=all |
| By: | Zhihong Jian; Zhican Zhu; Jie Zhou; Shuai Wu |
| Abstract: | This paper studies the magnet effect of market-wide circuit breakers and examines its possible forms using high-frequency data from the Chinese stock index futures market. Unlike previous studies that mainly analyzed the price trend and volatility, this paper is the first to consider the intraday price jump behavior in studying the magnet effect. We find that when a market-wide trading halt is imminent, the probability of a price decrease and the level of market volatility remain stable. However, the conditional probability of observing a price jump increases significantly, leading to a higher possibility of triggering market-wide circuit breakers, which is in support of the magnet effect hypothesis. In addition, we find a significant increase in liquidity demand and insignificant change in liquidity supply ahead of a market-wide trading halt, suggesting that the deterioration of market liquidity may play an important role in explaining the magnet effect. |
| JEL: | G10 G12 G18 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:win:winwop:2018-01&r=all |
| By: | Kolokolov, Aleksey; Livieri, Giulia; Pirino, Davide |
| Abstract: | Asset transaction prices sampled at high frequency are much staler than one might expect in the sense that they frequently lack new updates showing zero returns. In this paper, we propose a theoretical framework for formalizing this phenomenon. It hinges on the existence of a latent continuous-time stochastic process pt valued in the open interval (0; 1), which represents at any point in time the probability of the occurrence of a zero return. Using a standard infill asymptotic design, we develop an inferential theory for nonparametrically testing, the null hypothesis that pt is constant over one day. Under the alternative, which encompasses a semimartingale model for pt, we develop non-parametric inferential theory for the probability of staleness that includes the estimation of various integrated functionals of pt and its quadratic variation. Using a large dataset of stocks, we provide empirical evidence that the null of the constant probability of staleness is fairly rejected. We then show that the variability of pt is mainly driven by transaction volume and is almost unaffected by bid-ask spread and realized volatility. |
| Keywords: | staleness,idle time,liquidity,zero returns,stable convergence |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:236&r=all |
| By: | Anatoliy Swishchuk; Aiden Huffman |
| Abstract: | In this paper, we study various new Hawkes processes. Specifically, we construct general compound Hawkes processes and investigate their properties in limit order books. With regards to these general compound Hawkes processes, we prove a Law of Large Numbers (LLN) and a Functional Central Limit Theorems (FCLT) for several specific variations. We apply several of these FCLTs to limit order books to study the link between price volatility and order flow, where the volatility in mid-price changes is expressed in terms of parameters describing the arrival rates and mid-price process. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.02298&r=all |
| By: | Ben-zhang Yang; Xinjiang He; Nan-jing Huang |
| Abstract: | In this paper, the Kyle model of insider trading is extended by characterizing the trading volume with long memory and allowing the noise trading volatility to follow a general stochastic process. Under this newly revised model, the equilibrium conditions are determined, with which the optimal insider trading strategy, price impact and price volatility are obtained explicitly. The volatility of the price volatility appears excessive, which is a result of the fact that a more aggressive trading strategy is chosen by the insider when uninformed volume is higher. The optimal trading strategy turns out to possess the property of long memory, and the price impact is also affected by the fractional noise. |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1901.00345&r=all |
| By: | Fr\'ed\'eric Bucci; Michael Benzaquen; Fabrizio Lillo; Jean-Philippe Bouchaud |
| Abstract: | Using a large database of 8 million institutional trades executed in the U.S. equity market, we establish a clear crossover between a linear market impact regime and a square-root regime as a function of the volume of the order. Our empirical results are remarkably well explained by a recently proposed dynamical theory of liquidity that makes specific predictions about the scaling function describing this crossover. Allowing at least two characteristic time scales for the liquidity (`fast' and `slow') enables one to reach quantitative agreement with the data. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.05230&r=all |
| By: | Abdelali Gabih; Hakam Kondakji; Ralf Wunderlich |
| Abstract: | This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Drift estimates are based on Kalman filter techniques and described by the conditional mean and variance of the drift given the observations. We study the filter asymptotics for increasing arrival intensity of expert opinions and prove that the conditional mean is a consistent drift estimator, it converges in the mean-square sense to the hidden drift. Thus, in the limit as the arrival intensity goes to infinity investors have full information about the drift. |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.03453&r=all |
| By: | Hadrien De March; Charles-Albert Lehalle |
| Abstract: | In this paper we propose a mathematical framework to address the uncertainty emergingwhen the designer of a trading algorithm uses a threshold on a signal as a control. We rely ona theorem by Benveniste and Priouret to deduce our Inventory Asymptotic Behaviour (IAB)Theorem giving the full distribution of the inventory at any point in time for a well formulatedtime continuous version of the trading algorithm.Since this is the first time a paper proposes to address the uncertainty linked to the use of athreshold on a signal for trading, we give some structural elements about the kind of signals thatare using in execution. Then we show how to control this uncertainty for a given cost function.There is no closed form solution to this control, hence we propose several approximation schemesand compare their performances.Moreover, we explain how to apply the IAB Theorem to any trading algorithm drivenby a trading speed. It is not needed to control the uncertainty due to the thresholding of asignal to exploit the IAB Theorem; it can be applied ex-post to any traditional trading algorithm. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.03718&r=all |