An Index-Based Measure of Liquidity

SSRN Electronic Journal, 2013

This paper provides a new model-free indicator of liquidity, the so-called LIX index. The computation of the LIX index combines the conic finance theory, which recognizes the twoprice economy and is built upon the concept of indices of acceptability of Cherny & Madan (2010), with the option payoff spanning formula of Breeden & Litzenberger (1978). Matching the conic finance bid and ask prices of the stock with those observed in the market allows us to derive a model-free and unit-less indicator of spot liquidity. Just as the VIX and the SKEW index quantify the volatility and the tail risk perceived by today's investors, the resulting LIX index measures, in a similar market-implied fashion, the liquidity risk. The maximum likelihood estimation of popular mean-reverting processes applied to model-free liquidity time series indicates that spot liquidity tends to dry up during distress periods whereas a global drying-up of liquidity could not be detected during turmoil periods in the option market.

2020

The study examines the liquidity adjusted capital asset pricing model in developed and emerging markets. Amihud measure is used to compute market liquidity. Innovations in Amihud ratio are generated through the autoregressive process to avoid autocorrelation in illiquidity data series. Decile portfolios based on illiquidity cost are formulated for each stock market. Liquidity adjusted betas are calculated at the portfolio level and then stocks as test assets have been used in the regression stage. Panel regression with fixed effect has been employed on LCAPM specifications for explaining the excess stock returns of developed and emerging markets during a period July 2005-June 2017. The findings of the study support that individual and aggregate liquidity risk price in stock markets except for Pakistan. The results of the study suggest that investors institutional or individual should consider liquidity risks for assessing the worth of assets.

2015

This paper provides a new model-free indicator of liquidity, the so-called LIX index. The computation of the LIX index combines the conic finance theory, which recognizes the twoprice economy and is built upon the concept of indices of acceptability of Cherny & Madan (2010), with the option payoff spanning formula of Breeden & Litzenberger (1978). Matching the conic finance bid and ask prices of the stock with those observed in the market allows us to derive a model-free and unit-less indicator of spot liquidity. Just as the VIX and the SKEW index quantify the volatility and the tail risk perceived by today’s investors, the resulting LIX index measures, in a similar market-implied fashion, the liquidity risk. The maximum likelihood estimation of popular mean-reverting processes applied to model-free liquidity time series indicates that spot liquidity tends to dry up during distress periods whereas a global drying-up of liquidity could not be detected during turmoil periods in the op...

Journal of Banking & Finance, 2012

Using index and financial exchange-traded funds (ETFs), this study explores the relation between funding liquidity and equity liquidity during the subprime crisis period. Our empirical results show that a higher degree of funding illiquidity leads to an increase in bid-ask spread and a reduction in both market depth and net buying imbalance. Such findings indicate that an increase in funding liquidity can improve equity liquidity, with a stronger effect for the financial ETFs than for the index ETFs. Our study provides a better overall understanding of the effect of the liquidity-supplier funding constraint during the subprime crisis period.

SSRN Electronic Journal, 2000

Liquidity plays an increasingly important role in empirical asset pricing, market efficiency, and corporate finance. Identifying high quality proxies for liquidity based on daily data only (not intraday data) would permit liquidity to be studied over relatively long timeframes and across many countries. We introduce new liquidity measures. We run horseraces of both monthly and annual liquidity measures. We compare to effective spread, realized spread, and price impact based on both TAQ and Rule 605 data, including the decimals era. We identify the best proxies in each case and find that the new liquidity measures win the majority of horseraces.

The Journal of Trading, 2014

This article seeks to determine the migration of exchange-traded fund (ETF) liquidity and its factor constituents in the U.S. market over time, with the ultimate goal of making the ETF market more efficient and transparent. Using a set of factors commonly thought to impact liquidity, the authors develop a four-factor liquidity scoring algorithm (A-C liquidity score), extending the 2009 study by Agrrawal and Clark. The most liquid ETFs typically have lower bid–ask spreads, higher market capitalizations, lower expense ratios, and higher average trading volumes. The transition of ETF liquidity over the 2009–2014 period indicates that there is liquidity persistence and factor strengthening across all variables. Bid–ask spreads and expense ratios have compressed, which is a good trend for investors. Asset size and trading volume have gone up, which can be indicative of greater market participation and possibly high-frequency trading in the ETF environment. From 2009 to 2014, the ETF asset class has witnessed a phenomenal 34.9% annual growth rate, compared with a 19.8% growth rate in the Russell 3000 (the aggregate market capitalization for the ETF dataset increased from $438 billion in 2009 to $2,112 billion in 2014). The ETF ecosystem seems to be thriving as the new asset class of choice among both institutional and informed retail investors. However, there are two findings of concern. First, the overall beta of the ETFs in the study seems to have transitioned over time and moved up more than 9%. This may be due to increased cross-asset correlations, sector intertwining, global market cointegration, overlapping core constituents in ETFs, or simply a systematic upward drift in overall market risk premium; this limits portfolio diversification benefits. Second, over the five-year period, 25.7% of ETFs in the initial 622 ETF dataset (2009) were liquidated. This is an additional risk that investors and regulators have to keep in mind when evaluating ETFs. The cost of poor liquidity, overlooked by some retail investors, is often in the form of shallow market depth, high expense ratios, and a range of trade execution difficulties, besides potentially high tracking errors with the associated benchmark index. In conclusion, although ETF liquidity has grown significantly over time for most assets, there is some natural pruning, with pockets of concern. Overall, ETFs have rapidly become a viable and efficient instrument in the asset management landscape.

This paper examines the impact of illiquidity and liquidity risk on expected stock returns in the Turkish stock markets. Using daily data of the ISE-100 stock index from 2005 to 2012 and Amihud (2002) illiquidity measure, we test the liquidity-adjusted capital asset pricing model (L-CAPM) of Acharya and Pedersen (2005). Performing cross-sectional regression tests across test portfolios, we find supporting evidence that illiquidity is significantly and positively priced. Specifically, our results indicate that liquidity risk arising from the commonality in liquidity is the most important component of liquidity risk. The strong interrelationship between the market liquidity and the liquidity of individual stocks suggests that market-wide shocks on the Istanbul Stock Exchange might quickly affect every stock in this market. Hence, liquidity commonality might create a systemic risk in which case liquidity shocks can be perfectly correlated across all stocks. Our study is the first to investigate stock liquidity-return relationship at daily frequency and to apply the L-CAPM on the Turkish stock markets. Our findings provide interesting conclusions for investors, risk managers and regulators in emerging economies, and in particular, Turkey. Investors should incorporate liquidity risk into their trading and hedging strategies to improve their risk profile and increase their investment returns. Furthermore, an improved understanding of systemic liquidity is vital for regulatory authorities to design improved regulations against systemic shocks.

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...

2012

We investigate whether liquidity is an important price factor in the US corporate bond market. In particular, we focus on whether liquidity effects are more pronounced in periods of financial crises, especially for bonds with high credit risk, using a unique data set covering more than 20,000 bonds, between October 2004 and December 2008. We employ a wide range of liquidity measures and find that liquidity effects account for approximately 14% of the explained market-wide corporate yield spread changes. We conclude that the economic impact of the liquidity measures is significantly larger in periods of crisis, and for speculative grade bonds.

Journal of Financial Economics, 2012

We investigate whether liquidity is an important price factor in the US corporate bond market. In particular, we focus on whether liquidity effects are more pronounced in periods of financial crises, especially for bonds with high credit risk, using a unique data set covering more than 20,000 bonds, between October 2004 and December 2008. We employ a wide range of liquidity measures and find that liquidity effects account for approximately 14% of the explained market-wide corporate yield spread changes. We conclude that the economic impact of the liquidity measures is significantly larger in periods of crisis, and for speculative grade bonds.