Liquidity and Liquidity Value‑at‑Risk

Proposal To carry out a literature review of liquidity and liquidity value-at-risk, as a starting point to developing new liquidity metrics and a liquidity estimator based on support vector estimation of supply and demand functions.

Background There are a number of indicators of liquidity, e.g. bid-ask spreads, volume traded, open offers. However, as discussed in the lecture – “Liquidity: Finance in Motion or Evaporation”– none of these can be taken to be a measure of liquidity because liquidity itself is poorly-defined. In the lecture, the contention is that estimating liquidity is actually about estimating the supply and demand curves (more accurately, the 'non-curves' as in practice there are holes and discontinuous jumps within clouds of probabilities). This study would further understanding of liquidity in traded markets, with a particular emphasis on OTC markets.

There has been significant discussion around an analogy to value-at-risk (VAR) approaches, i.e. liquidity value-at-risk (LVAR). If a robust LVAR methodology existed, it would be useful in allowing organisations both to manage their business and to set capital requirements.

Further, if a robust methodology existed, then guidance could be given on conditions that should trigger increased vigilance, e.g. when conditions are more conducive to market manipulation. This guidance could possibly take the form of “mean, motives, opportunity, environmental conditions and measures”, e.g. providing an outline of trading strategies that might place an organisation at risk of being suspected of manipulation so they can be avoided.

This study would offer a critical review of the literature in order to see if such a methodology might exist, understand the current state of thinking on liquidity and, if appropriate, suggest research which might advance further its understanding of liquidity. The objective of the research can be stated as:

  • provide a critical review of the literature on liquidity, including academic, regulatory and market literature;
  • set out a high-level taxonomy of liquidity measures, and liquidity value-at-risk measures and approaches;
  • set out a research proposal, if there is a potentially useful piece(s) of research to conduct.


At a practical level, Z/Yen’s approach to the research would be to:

  • conduct desk research, in order to produce the historical background, current state of thinking, literature survey and research proposal;
  • conduct to 5 to 15 in-depth interviews (face-to-face or, where relevant, telephone) interviews with senior figures involved in markets to ensure a proper understanding of the issues and prospects;
  • develop a suggested taxonomy structure or critique existing ones;
  • collate, analyse and report the findings in a “Literature Review and Research Proposal” along the following lines:
    • Executive Summary
    • Overview of the Literature
    • Hypotheses
    • Methods & Procedures
    • Instrumentation, Sampling, Data Collection & Analysis
    • Scope & Limitations
    • Significance of the Study
    • Appendices

As indicated above, the research on liquidity is to help establish a baseline for practical applications:

¨ liquidity predictor – we would seek to expand the discussion of liquidity risk as uncertainty around the estimation of the empirical Price/Quantity 'non-curve'. We look towards predicting changes in the height, slope, shape and complexity of the Price/Quantity curve as components for a liquidity `dashboard’. Ultimately, the objective is to predict liquidity `black holes’ such that the dashboard helped one `steer’ around them.

¨ allocation estimator – we could attempt to build a `scorecard’ that used a number of parameters to estimate statistically the likely holdings of regions or firms (by class). While an estimator could build on existing cross-border monetary flow and money supply analysis, various fund holdings statements, etc., validating the model would be challenging. Nevertheless, it could be a start towards a red-amber-green style `dashboard’ for decisions. We would also be interested in linking with the allocation estimator to use biodiversity measures (e.g. fractals, Simpson’s index) to help identify rich pockets of diversity that might be more immune to liquidity crises. The basis for this project, which already identified 'point' rather than 'curve' liquidity, would be our Best Execution Compliance Automation work.

¨ understanding the interactions of discrete, `fixed bucket’ ratings and their changes, versus continuous prices – the complex interaction of ratings with prices at various levels, and the role of the rating agencies, leads one to think that it might be interesting to do a `sensitivity’ analysis of liquidity measures under various scenarios. This might also find pricing opportunities among `simple’ products, and richer combinations of products and holdings.

Next Steps This note is designed to initiate conversation. Z/Yen is interested in exploring these issues further. Please contact Michael Mainelli, Director, Z/Yen Group Limited,

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