The impact of hidden liquidity in limit order books

We report evidence that the presence of hidden liquidity is associated with greater liquidity in the order books, greater trading volume, and smaller price impact. Limit and market order submission behavior changes when hidden liquidity is present consistent with at least some traders being able to detect hidden liquidity. We estimate a model of liquidity provision that allows us to measure variations in the marginal and total payoffs from liquidity provision in states with and without hidden liquidity. Our estimates of the expected surplus to providers of visible and hidden liquidity are positive and typically of the order of one-half to one basis points per trade. The positive liquidity provider surpluses combined with the increased trading volume when hidden liquidity is present are both consistent with liquidity externalities

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

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

The Liquidity Coverage Ratio: The Need for Further Complementary Ratios?

This paper considers components of the Liquidity Coverage Ratio – as well as certain prevailing gaps which may necessitate the introduction of a complementary liquidity ratio. The definitions and objectives accorded to the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) highlight the focus which is accorded to time horizons for funding bank operations. A ratio which would focus on the rate of liquidity transformations and which could also serve as a complementary metric, given certain gaps which currently prevail with the Liquidity Coverage Ratio, as well as existing gaps with other complementary liquidity monitoring tools, is propose

Market Liquidity and Funding Liquidity

We provide a model that links an asset's market liquidity - i.e., the ease with which it is traded - and traders' funding liquidity - i.e., the ease with which they can obtain funding. Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and the margins they are charged, depend on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to "flight to quality", and (v) comoves with the market, and it provides new testable predictions.

Why do contracts differ between VC types? : market segmentation versus corporate governance varieties

We model the impact of bank mergers on loan competition, reserve holdings and aggregate liquidity. A merger changes the distribution of liquidity shocks and creates an internal money market, leading to financial cost efficiencies and more precise estimates of liquidity needs. The merged banks may increase their reserve holdings through an internalization effect or decrease them because of a diversification effect. The merger also affects loan market competition, which in turn modifies the distribution of bank sizes and aggregate liquidity needs. Mergers among large banks tend to increase aggregate liquidity needs and thus the public provision of liquidity through monetary operations of the central bank. JEL Classification: G24, G32, G3

POLICIES OF THE COMMERCIAL BANKS LIQUIDITY MANAGEMENT IN THE CRISIS CONTEXT

The article focuses on liquidity management in Commercial Banks, and presents the steps that a good management has to follow to ensure that the position of the bank is not put into jeopardy following a lack of liquidity. Different management decisions andliquidity management, liquidity strategy, liquidity risk, liquidity risk exposure liquidity risk funding, currency strategy, liquidity planning procedures, alternative scenarios, liquidity crisis management

The impact of hidden liquidity in limit order books

This paper analyzes liquidity in an order driven market. We only investigate the best limits in the limit order book, but also take into account the book behind these inside prices. When subsequent prices are close to the best ones and depth at them is substantial, larger orders can be executed without an extensive price impact and without deterring liquidity. We develop and estimate several econometric models, based on depth and prices in the book, as well as on the slopes of the limit order book. The dynamics of different dimensions of liquidity are analyzed: prices, depth at and beyond the best prices, as well as resiliency, i.e. how fast the different liquidity measures recover after a liquidity shock. Our results show a somewhat less favorable image of liquidity than often found in the literature. After a liquidity shock (in the spread or depth or in the book beyond the best limits), several dimension of liquidity deteriorate at the same time. Not only does the inside spread increase, and depth at the best prices decrease, also the difference between subsequent bid and ask prices may become larger and depth provided at them decreases. The impacts are both econometrically and economically significant. Also, our findings point to an interaction between different measures of liquidity, between liquidity at the best prices and beyond in the book, and between ask and bid side of the market

Time-varying credit risk and liquidity premia in bond and CDS markets

We develop a reduced-form model that allows us to decompose bond spreads and CDS premia into a pure credit risk component, a pure liquidity component, and a component measuring the relation between credit risk and liquidity. CDS liquidity has important consequences for the bond credit risk and liquidity components. Besides the credit risk link, we document a liquidity link between the bond and the CDS market. Liquidity in both markets dries up as credit risk increases, and higher bond market liquidity leads to lower CDS market liquidity. Ignoring CDS liquidity results in partly negative liquidity premia, particularly when CDS liquidity is low. --