英文标题：
《Liquidity commonality does not imply liquidity resilience commonality: A
  functional characterisation for ultra-high frequency cross-sectional LOB data》
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作者：
Efstathios Panayi, Gareth Peters and Ioannis Kosmidis
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最新提交年份：
2014
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英文摘要：
  We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of explanatory power of the dominant modes of variation of liquidity (extracted through Principal Component Analysis) fails to account for heavy tailed features in the data, thus producing potentially misleading results. We employ Independent Component Analysis, which both decorrelates the liquidity measures in the asset cross-section, but also reduces higher-order statistical dependencies.   To measure commonality in liquidity resilience, we utilise a novel characterisation as the time required for return to a threshold liquidity level. This reflects a dimension of liquidity that is not captured by the majority of liquidity measures and has important ramifications for understanding supply and demand pressures for market makers in electronic exchanges, as well as regulators and HFTs. When the metric is mapped out across a range of thresholds, it produces the daily Liquidity Resilience Profile (LRP) for a given asset. This daily summary of liquidity resilience behaviour from the vast LOB dataset is then amenable to a functional data representation. This enables the comparison of liquidity resilience in the asset cross-section via functional linear sub-space decompositions and functional regression. The functional regression results presented here suggest that market factors for liquidity resilience (as extracted through functional principal components analysis) can explain between 10 and 40% of the variation in liquidity resilience at low liquidity thresholds, but are less explanatory at more extreme levels, where individual asset factors take effect.
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中文摘要：
我们在一个泛欧洲电子股票交易设施的超高频（毫秒时间戳）限额指令簿（LOB）数据集中对资产流动性和弹性的共性进行了大规模研究。我们首先表明，通过主要流动性变化模式（通过主成分分析提取）的解释力程度来量化流动性共性的现有工作未能解释数据中的重尾特征，从而产生潜在的误导性结果。我们采用独立成分分析法，这既能使资产横截面中的流动性指标去相关，又能减少高阶统计相关性。为了衡量流动性弹性的共性，我们使用了一种新的特征描述，作为恢复到阈值流动性水平所需的时间。这反映了流动性的一个维度，而大多数流动性指标都没有捕捉到这一维度，这对于理解电子交易所中做市商、监管机构和高频交易的供求压力具有重要影响。当指标在一系列阈值上绘制出来时，它会生成给定资产的每日流动性弹性曲线（LRP）。这一来自庞大LOB数据集的流动性弹性行为的每日摘要随后可用于功能数据表示。这使得能够通过功能线性子空间分解和功能回归来比较资产横截面中的流动性弹性。本文给出的功能回归结果表明，流动性弹性的市场因素（通过功能主成分分析提取）可以解释低流动性阈值下流动性弹性10%到40%的变化，但在更极端的水平上解释性较差，其中个别资产因素起作用。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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