• DOI:10.1080/14697688.2015.1071075
• Corpus ID: 153978467

Liquidity commonality does not imply liquidity resilience commonality: a functional characterisation for ultra-high frequency cross-sectional LOB data

@article{Panayi2014LiquidityCD,
  title={Liquidity commonality does not imply liquidity resilience commonality: a functional characterisation for ultra-high frequency cross-sectional LOB data},
  author={Efstathios Panayi and Gareth W. Peters and Ioannis Kosmidis},
  journal={Quantitative Finance},
  year={2014},
  volume={15},
  pages={1737 - 1758}
}

• Efstathios Panayi, G. Peters, I. Kosmidis
• Published 20 June 2014
• Economics
• Quantitative Finance

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) data-set 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 proposed by Panayi et al. [Market resilience, 2014] for 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 for a given asset. This daily summary of liquidity resilience behaviour from the vast LOB data-set 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.