Market Microstructure knowledge needed to control an intra-day trading process
Market Microstructure knowledge needed to control an intra-day trading process
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控制日内交易过程所需的市场微观结构知识
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发表时间：
2011
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通讯作者：
Charles
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作者：
Charles
在过去的二十年里，大量的学术和理论工作致力于大订单的最优清算。由于最近全球监管和流动性的演变，市场微观结构的复杂性日益增加，因此，通过时间（“最佳交易调度”）和空间（“智能订单路由”）的订单的最佳分割对从业者来说非常感兴趣。这篇文章是翻译在定量方面这些监管问题，更广泛地说，目前的市场设计。它面对的最佳交易，订单簿模拟和最佳流动性寻求在一个新兴的全球流动性网络的现实交易的最新进展。1市场微观结构建模和收益理解是量化交易的关键要素众所周知，最佳（或量化）交易是在提供流动性以最大限度地减少交易影响和消耗流动性以最大限度地减少市场风险敞口之间找到适当的平衡，同时从潜在的瞬时交易信号中获利，这些信号应该是由流动性效率低下引发的。解决这种优化问题所需的数学框架需要一个模型来描述与流动性相互作用的不同方式的后果（如市场影响模型[Almgren et al.，2005] [Wyart等人，2008年] [Gatheral，2010年]），一个“市场风险”的代理（其中最自然的是高频波动率[Aprilant-Sahalia和Jacod，2007年，Zhang等人，2005年，罗伯特和罗森鲍姆，2011年]）和一个模型，以量化市场的流动性状态的可能性[巴克里等人，2009年，Cont等人，2010年]。效用函数允许整合这些不同的影响，相对于交易者的目标：在价格、期限和交易量限制下，尽量减少大宗交易的影响（典型的经纪交易[Almgren和Chriss，2000年]），在库存限制下提供尽可能多的流动性（典型的做市商[Avellaneda和Stoikov，2008]或[Lehalle-Gueant-Frenandez]），或遵循对市场轨迹的信念（典型的仲裁员[Lehalle，2009]）。法国东方汇理银行（Crédit Agricole Cheuvreux）全球定量研究主管（clehalle@cheuvreux.com）9 Quai Paul Doumer，Paris-La Defense，France
A lot of academic and theoretical works have been dedicated to optimal liquidation of large orders these last twenty years. The optimal split of an order through time (“optimal trade scheduling”) and space (“smart order routing”) is of high interest for practitioners because of the increasing complexity of the market micro structure since recent evolutions of regulations and liquidity worldwide. This article is translating in quantitative terms these regulatory issues and more broadly the current market design. It confronts the recent advances in optimal trading, order-book simulation and optimal liquidity seeking to the reality of trading in an emerging global network of liquidity. 1 Market micro-structure modelling and payoff understanding are key elements of quantitative trading As it is widely known, optimal (or quantitative) trading is about finding the proper balance between providing liquidity to minimise the impact of the trades, and consuming liquidity to minimise the market risk exposure, while taking profit of potential instantaneous trading signals, supposed to be triggered by liquidity inefficiencies. The mathematical framework required to solve this kind of optimisation needs a model of the consequences of the different ways to interact with liquidity (like a market impact model [Almgren et al., 2005] [Wyart et al., 2008] [Gatheral, 2010]), a proxy for the “market risk” (the most natural of them being the high frequency volatility [Aı̈t-Sahalia and Jacod, 2007, Zhang et al., 2005, Robert and Rosenbaum, 2011]) and a model to quantify the likelihood of the liquidity state of the market [Bacry et al., 2009, Cont et al., 2010]. A utility function allows then to consolidate these different effects with respect to the goal of the trader: minimising the impact of large trades under price, duration and volume constraints (typical for brokerage trading [Almgren and Chriss, 2000]), providing as liquidity as possible under inventory constraints (typical for marketmakers [Avellaneda and Stoikov, 2008] or [Lehalle-Gueant-Frenandez]), or following a belief on the trajectory of the market (typical of arbitrageurs [Lehalle, 2009]). ∗Global Head of Quantitative Research (clehalle@cheuvreux.com), Crédit Agricole Cheuvreux 9 Quai Paul Doumer, Paris-La Defense, France