|
on Market Microstructure |
| By: | Nikolaus Hautsch; Ruihong Huang |
| Abstract: | Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders’ use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ, we propose a simple method to conduct statistical inference on the location of hidden depth and to test economic hypotheses. Analyzing a wide cross-section of stocks, we show that market conditions reflected by the (visible) bid-ask spread, (visible) depth, recent price movements and trading signals significantly affect the aggressiveness of ’dark’ liquidity supply and thus the ’hidden spread’. Our evidence suggests that traders balance hidden order placements to (i) compete for the provision of (hidden) liquidity and (ii) protect themselves against adverse selection, front-running as well as ’hidden order detection strategies’ used by high-frequency traders. Accordingly, our results show that hidden liquidity locations are predictable given the observable state of the market. |
| Keywords: | limit order market, hidden liquidity, high-frequency trading, non-display order, iceberg orders |
| JEL: | G14 C24 C25 G17 |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2012-014&r=mst |
| By: | Rama Cont; Adrien De Larrard |
| Abstract: | We propose a model for the dynamics of a limit order book in a liquid market where buy and sell orders are submitted at high frequency. We derive a functional central limit theorem for the joint dynamics of the bid and ask queues and show that, when the frequency of order arrivals is large, the intraday dynamics of the limit order book may be approximated by a Markovian jump-diffusion process in the positive orthant, whose characteristics are explicitly described in terms of the statistical properties of the underlying order flow. This result allows to obtain tractable analytical approximations for various quantities of interest, such as the probability of a price increase or the distribution of the duration until the next price move, conditional on the state of the order book. Our results allow for a wide range of distributional assumptions and temporal dependence in the order flow and apply to a wide class of stochastic models proposed for order book dynamics, including models based on Poisson point processes, self-exciting point processes and models of the ACD-GARCH family. |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1202.6412&r=mst |
| By: | Pietro Fodra (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot); Mauricio Labadie (Chercheur Indépendant - Aucune) |
| Abstract: | In this paper we extend the market-making models with inventory constraints of Avellaneda and Stoikov "High-frequency trading in a limit-order book", Quantitative Finance Vol.8 No.3 2008) and Lehalle, Gueant and Fernandez-Tapia ("Dealing with inventory risk", Preprint 2011) to the case of a rather general class mid-price processes, under either exponential or linear PnL utility functions, and with an inventory-risk-aversion parameter that penalises the marker-maker if she finishes her day with a non-zero inventory. This general, non-martingale framework allows a market-maker to make directional bets on market trends whilst keeping under control her inventory risk. In order to achieve this, the marker-maker places non-symmetric limit orders that favour market orders to hit her ask (resp. bid) quotes if she expects that prices will go up (resp. down). In the case of a mean-reverting mid-price, we show numerically that the market-maker can increase her PnL between 10% and 25% depending on her buget risk on inventory and PnL distribution (especially variance, skewness, kurtosis and VaR). Moreover, with this inventory-risk-aversion parameter the market-maker has not only direct control on her inventory risk but she also has indirect control on the moments of her PnL distribution. Therefore, this parameter can be seen as a fine-tuning of the marker-maker's risk-reward profile. |
| Keywords: | Quantitative Finance; high-frequency trading; market-making; limit-order book; inventory risk; optimisation; stochastic control; Hamilton-Jacobi-Bellman; PnL distribution |
| Date: | 2012–03–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00675925&r=mst |
| By: | Per A. Mykland; Neil Shephard; Kevin Sheppard |
| Abstract: | High frequency financial data allows us to learn more about volatility, volatility of volatility and jumps. One of the key techniques developed in the literature in recent years has been bipower variation and its multipower extension, which estimates time-varying volatility robustly to jumps. We improve the scope and efficiency of multipower variation by the use of a more sophisticated exploitation of high frequency data. This suggests very significant improvements in the power of jump tests. It also yields efficiency estimates of the integrated variance of the continuous part of a semimartingale. The paper also shows how to extend the theory to the case where there is microstructure in the observations and derive the first nonparametric high frequency estimator of the volatility of volatility. A fundamental device in the paper is a new type of result showing path-by-path (strong) approximation between multipower and the (unobserved) RV based on the continuous part of the process. |
| Keywords: | Bipower variation, Jumps, Market microstructure noise, Multipower variation, Non-parametric analysis, Quadratic variations, Semimartingale, Volatility, Volatility of volatility |
| JEL: | C01 C02 C13 C14 C22 D53 D82 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:593&r=mst |
| By: | Fricke, Christoph |
| Abstract: | This paper shows that order flow determines future bond excess returns. This effect cannot be captured by macroeconomic or forward rate information. To understand how these variables influence future bond excess returns, we decompose excess returns into expected and unexpected excess returns. Expected returns crucially depend on the available information set which is spanned by order flow, forward rates and macroeconomic variables. Thus, the predictability of bond excess returns stems from the strong linkage of expected excess returns to available economic information and order flow. The analysis of unexpected excess returns reveals contemporaneous order flow and changes of the economic environment as main drivers. |
| JEL: | E43 E44 E47 G14 |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-493&r=mst |