The LIX: A Model-Independent Liquidity Index

2000

In a continuous trading market, taking efficiency as given, VarIatIOns in liquidity can be measured by simultaneous changes in both immediacy costs and depth. Past theoretical and empirical microstructure literature has been, however, one-dimensional. This paper introduces a liquidity measure called BLM, specially designed for time-series analyses, that captures simultaneous changes in both liquidity dimensions and provides a simple solution to deal with those situations in which the change in immediacy costs and depth does not lead to an unambiguous shift in liquidity. Using data from the NYSE, it is evidenced that this measure captures short-term liquidity dynamics that cannot be captured by one-dimensional proxies of liquidity. This measure is applied to the study of how liquidity reacts to changes in the market conditions. It is shown that only volatility shifts have an unambiguous effect on liquidity independently of the trading hour.

Pacific-Basin Finance Journal, 2000

This paper documents the systematic overpricing of warrants relative to options. Models are developed in order to explain the cross-sectional variation in the relative pricing of these Ž . securities. Results indicate that relative pricing differences RELDIFF are related to various proxies of liquidity including days-to-maturity, relative trading volume and the mandated presence of market makers in the options market. The identity of warrant-issuers is also found to be significant in explaining relative pricing, possibly reflecting disparate levels of credit risk or it may be a manifestation of the different characteristics relating to the underlying shares upon which the warrants are issued. The paper also documents the impact that the change from floor trading to electronic trading had on the price formation process in the Australian Options Market. q

Relationship between liquidity and asset prices has attracted considerable attention since . A number of proxies have been used to measure liquidity but a direct objective measure is lacking. This paper introduces a directly observable measure of liquidity and compares its performance to the widely used bid ask spread measure of liquidity introduced by Amihud and Mendelson's (1986) seminal work. The results indicate that this measure of liquidity substantially outperforms the traditional measure and may be useful in predicting market level price changes.

SSRN Electronic Journal, 2000

Asset liquidity in modern financial markets is a key but elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant definition of market liquidity has, however, provided to be a difficult task. This paper provides a critical review of the frameworks currently available for modelling and estimating the market liquidity of assets. We consider definitions that stress the role of the bid-ask spread and the estimation of its components that arise from alternative sources of market friction. In this case, intra-daily measures of liquidity appear relevant for capturing the core features of a market, and for their ability to describe the arrival of new information to market participants. JEL Classification No.: G1, G10, G12.

Journal of Financial Economics, 2009

Given the key role of liquidity in finance research, identifying high quality proxies based on daily (as opposed to intraday) data would permit liquidity to be studied over relatively long timeframes and across many countries. Using new measures and widely employed measures in the literature, we run horseraces of annual and monthly estimates of each measure against liquidity benchmarks. Our benchmarks are effective spread, realized spread, and price impact based on both Trade and Quote (TAQ) and Rule 605 data. We find that the new effective/realized spread measures win the majority of horseraces, while the Amihud [2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5, 31-56] measure does well measuring price impact.

2020

This study examines the relationship between funding liquidity and market liquidity using daily data on the S&P 500 index options. We find that options market liquidity is positively correlated with funding liquidity after controlling for market uncertainty. Further analysis reveals that the positive relationship between funding liquidity and market liquidity in the options market is mainly driven by short‐term and deep out‐of‐the‐money options. Our results remain robust after controlling for the confounding effects of the equity market and different data frequencies.

The New York University Salomon Center Series on Financial Markets and Institutions, 2001

Market risk management under normal conditions traditionally has focussed on the distribution of portfolio value changes resulting from moves in the mid-price. Hence the market risk is really in a "pure" form: risk in an idealized market with no "friction" in obtaining the fair price. However, many markets possess an additional liquidity component that arises from a trader not realizing the mid-price when liquidating her position, but rather the mid-price minus the bid-ask spread. We argue that liquidity risk associated with the uncertainty of the spread, particularly for thinly traded or emerging market securities under adverse market conditions, is an important part of overall risk and is therefore an important component to model. We develop a simple liquidity risk methodology that can be easily and seamlessly integrated into standard value-at-risk models, and we show that ignoring the liquidity effect can produce underestimates of market risk in emerging markets by as much as 25-30%. Furthermore, we show that the BIS inadvertently is already monitoring liquidity risk, and that by not modeling it explicitly and therefore capitalizing against it, banks will be experiencing surprisingly many violations of capital requirements, particularly if their portfolios are concentrated in emerging markets. We thank Steve Cecchetti and Edward Smith for helpful comments and suggestions. All remaining errors are ours.

International Journal of Theoretical and Applied Finance, 2010

We use order book data 1 combined with tick data to analyze the supply curve models of liquidity issues in stock and option market trading. We show that supply curves really exist, and further that for highly liquid stocks they are linear. For slightly less liquid stocks the supply curve tends to be jump linear.

2011

This article illustrates the impact of both spot and option liquidity levels on option prices. Using implied volatility to measure the option price structure, our empirical results reveal that even after controlling for the systematic risk of , a clear link remains between option prices and liquidity; with a reduction (increase) in spot (option) liquidity, there is a corresponding increase in the level of the implied volatility curve. The former is consistent with the explanation on hedging costs provided by Cetin, Jarrow, , whilst the latter is consistent with the 'illiquidity premium' hypothesis of . This study also shows that the slope of the implied volatility curve can be partially explained by option liquidity.

2014

The object of the thesis is to investigate, measure and analyse the impact of liquidity on portfolio value, risk and execution. We consider the formalism of [Acerbi and Scandolo, 2008] to value portfolios in markets exposed to illiquidity through the use of Marginal Supply Demand Curves. We show that future portfolio returns become fat-tailed when liquidity risk is introduced. Further, we investigate the market impact model of [Almgren et al., 2005], who estimates supply curves on equity instruments by considering a large database of executed orders. Since such data are highly confidential, we propose to use transaction data to estimate the same supply curves. This may enable more market participants to assess their liquidity risks and costs. Transaction data does not contain the same information as order data. To bridge the information gap between the two data sets, we introduce a ’strategy identifier’. By using regression and filtering techniques we show that using transaction dat...