Illiquidity Risk Research Articles

Impact of oil price and market volatility on the relationship between Saudi stock prices and illiquidity

This study examines whether stock price sensitivity to illiquidity shocks changes over time in the Saudi stock market. Using structural break analysis, the research identifies shifts in the sensitivity of stock prices to illiquidity. A Markov switching model is then applied to understand these changes. The results indicate that small firms experience two distinct regimes, with illiquidity shocks reducing stock prices in the first regime ten times more than in the second. For large firms, stock price responses to illiquidity shocks vary across three regimes: in the first, prices decrease; in the second, prices remain stable; and in the third, prices drop sharply. Further analysis shows that higher market volatility significantly increases the impact of illiquidity shocks on small firms, while large firms are more sensitive to illiquidity shocks following periods of negative market performance. The study finds no evidence that changes in oil prices influence the relationship between illiquidity shocks and stock prices. These findings provide valuable insights for investors to predict periods of high illiquidity risk and implement effective investment strategies.

Informed Trading and Return Predictability in China: Research Based on Ensemble Neural Network

ABSTRACT We construct a new informed trading index based on the high-frequency trading data of the Chinese A-share market using the ensemble neural network algorithm. We find that the informed trading index is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R 2 of 5.45% and 3.53%, respectively, which is far greater than the predictive power of other previously studied informed trading indicators and macroeconomic variables. The asset allocation strategy based on our index can generate large economic gains for the mean-variance investors, with annualized CER (certain equivalent return) gains ranging from 10.91% to 7.80% as the investor’s risk appetite varies. The driving force of the predictive power appears to stem from the liquidity provider role that informed traders play, which decreases the market’s illiquidity risk and lowers the risk premium of equity. Our analysis complements the returns predictability study by adding a new predictor on the one hand and informs market timing strategies on the other.

Determining bid-ask prices for options with stochastic illiquidity and applications to index options

Market makers must quote two prices - bid and ask prices - for options. This article provides a GARCH model with stochastic illiquidity risks and gives analytical approximation solutions for the bid and ask prices for options. A joint calibration method is applied for calibrating the implied parameters. The empirical evidence shows an illiquidity smile for short-term calls and a negatively sloped illiquidity smirk for long-term calls. Similar results are also observed in put option prices. The proposed stochastic illiquidity model significantly outperforms the static illiquidity model in the in-sample and out-of-sample tests, particularly when the stochastic volatility is crucial.

Hedge funds, systemic risk and the market for mortgage-backed securities

Abstract In the early 2000s, the market for private-label residential mortgage-backed securities was built upon a pyramid of risk, and at the foundation of this pyramid were hedge funds. Few post-mortems recognise the critical role played by these institutional investors. This paper uncovers this essential piece of the puzzle and demonstrates that it is a natural outgrowth of the inherent operational freedom that defines hedge funds. It breaks these foundational risks down into two components—illiquidity risk and tail risk—and proposes solutions to prevent them from spilling over into systemic crises without eliminating the benefits that society reaps from this unique risk-taking. It concludes with important lessons for securitisation markets to consider during and after the COVID-19 recession.

Exploring illiquidity risk pre and during the COVID-19 pandemic era: Evidence from international financial markets

Research aims: Illiquidity risk is one of the complex issues that institutional investors and market participants continually face over time. It is because the constructs of illiquidity risk are sometimes complicated, robust, and not so evident in secondary markets. Hence, this study aims to empirically explore illiquidity risk before and during the COVID-19 pandemic to understand how much investors were expected to lose if they invested in stock markets during these periods.Design/Methodology/Approach: This study used a GARCH model and the Amihud illiquidity ratio to achieve its objective. Trading volumes and price returns for the JSE, CAC 40, DAX, Nasdaq, BIST 100, and SSE were from June 30, 2017, to June 30, 2019, and January 1, 2020, to December 31, 2021.Research findings: As expected, the findings revealed higher illiquidity risk during periods of financial distress, such as the COVID-19 pandemic. During the financial crisis, investors could lose up to $22268.44 a day in less developed markets, such as the JSE, while the average loss in developed markets ranged between $0.22 to $11.53 in the Nasdaq and DAX, respectively. On average, a much lower figure was observed before the financial crisis. The BIST100, CAC 40, DAX, and Nasdaq are excellent options for those seeking lower-risk premiums.Theoretical and Practitioner/Policy implication: Policies such as adequate market microstructure and greater transparency in trading are strongly recommended for less developed markets, especially during periods of financial distress. Also, the findings of this study provide valuable insight into short-term traders and market participants attracted to liquid markets, where they can easily enter and exit their positions with minimal transaction costs. To the author's knowledge, this paper is the first to model illiquidity risk in stock markets.Research limitation/Implication: It is possible that the current study did not accurately capture the cost of illiquidity in the sampled financial markets and cannot be applied to other financial markets.

Banking against Global Economic Crisis: Comparison of Covid-19 Pandemic and 2008 Recession

In the last twenty years, there has been two major global economic crises in Indonesia with very different origin, the 2008 global financial crisis was induced by failure of international banks while the 2020 global pandemic crisis was induced by economic slowdown due to worldwide infectious disease. Previous researches implied that the financial crisis had more tendency to deplete capital without slowing down credit distribution thus facing bigger risk of illiquidity while it had the opposite effect on the pandemic crisis where banks withhold their capital too much and distributed too little leading to less profitability. We found that the persistence of changes of CAR, LDR, NPL, and ROA as capital, liquidity, and profitability measures during both crises don’t differ significantly. Keywords: Banking, pandemic, economic crisis.

A Theory of Liquidity in Private Equity

We develop a model of private equity capturing two fundamental features of this market: the fund structure and illiquidity. A fund structure with sequential capital calls arises as an optimal solution to fund managers’ (GPs) moral hazard problem but exposes investors (LPs) to illiquidity risk. Funds with more illiquidity-tolerant LPs realize higher returns, leading to different expected returns across both funds and LPs in equilibrium. GPs may inefficiently accelerate drawdowns to avoid default by LPs on capital commitments. With a secondary market for LP claims, differences in fund returns are attenuated but differences in LP returns remain. The model can rationalize several empirical findings on primary and secondary private equity markets. This paper was accepted by Bruno Biais, finance. Funding: V. Maurin thanks the Swedish House of Finance for financial support. P. Strömberg thanks the NASDAQ Nordic Foundation, Söderberg Professorship in Economics, and Swedish House of Finance for financial support. D. Robinson thanks the Bertil Danielsson Professorship and Erling Persson Professorship for financial support. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4612 .

Spreads and Volatility in House Returns

Underlying idiosyncratic and illiquidity risks are suppressed in infrequently reported indexes of house prices and rents. Idiosyncratic risks result from bid–ask spreads for prices and rents. Time series autocovariances generate a distribution of prices and rents. Capital gains and rent-price ratios are transforms of these distributions, generating cross-sectional idiosyncratic volatility. Housing data are infrequent and usually made available every month. The monthly–quarterly volatility ratios of house prices and rents and their spreads estimate unobserved daily fluctuations and illiquidity risks. Including idiosyncratic and illiquidity risks, a U.S. house has a standard deviation in returns of 8.7% annually for three decades after 1990. With a mean excess return of 3.7%, the Sharpe ratio of 0.42 is comparable to the S&P 500. Excluding spreads, the house Sharpe ratio is 0.69. House returns respond to liquidity. A 1% increase in volume raises returns by 0.8%.

Corporate Liquidity in Coronacrisis: Experience of Serbian Economy

The appearance of coronavirus in the spring of 2020 has significantly revalued risk exposures in the business environment, which required new approaches in the measurement of financial risks. This paper defines and explains a new approach to the measurement of liquidity risk in companies in a time of an economic crisis. This approach is more responsive to stressful circumstances in the business environment and measures the time in an economic crisis when a company can still pay maturing liabilities out of its own inventory of liquid assets, where sales of a company on the market are limited or completely prohibited, as this was the case for some industries during the first wave of Covid -19 in spring 2020. The objective of this paper was to define a new metric for the measurement of corporate liquidity, which is sensitive to this environment and shows the propensity of a company to the risk of illiquidity if an economic crisis appears. This paper, in the next step, leverages the newly defined metric of corporate liquidity and calculates the average liquidity of all companies, as well as the average liquidity by business sectors in the Serbian economy, to discuss corporate liquidity in a crisis. The results show that the average liquidity of the Serbian economy in crisis at the end of 3Q 2020 was 78,02 days, i.e., the average company in the Serbian economy was able to survive 78,02 days in stress by the end of 3Q 2020 if its sales on the market were prohibited. Implications for Central European audience: The model in this paper is in the interest of every company and bank to measure the liquidity risk of business partners in times of an economic crisis and to predict which business partners may have liquidity problems in crisis and may therefore not be able to pay their open liabilities. The contribution of this paper to liquidity risk measurement in companies in times of stress is high as the currently available literature does not offer an alternative approach.

The asymmetry of the Amihud illiquidity measure on the European markets: The evidence from Extreme Value Theory

This paper explores the extreme illiquidity for a sample of stocks listed on the European stock markets. The extreme value theory tools such as an extremal index as a measure of illiquidity dependence and a return level as a measure of illiquidity risk are applied. We obtain Amihud illiquidity separately for positive and negative returns and verify if there is an asymmetry in the extreme illiquidity. This study finds that the extreme illiquidity clusters more for negative returns than positive ones. Moreover, the clustering of extreme illiquidity is observed more often on the emerging markets than on the developed ones. For stocks listed on the emerging markets, the risk of extreme illiquidity is higher in the case of negative returns than of positive ones, while for stocks listed on developed markets no such phenomena is found. Our robustness check conducted with volatility over volume as an illiquidity measure confirms results obtained for the extremal indices, but not for the return level.

Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach

The subprime crisis was quite damaging for hedge funds. Using the local projection method (Jordà 2004, 2005, 2009), we forecast the dynamic responses of the betas of hedge fund strategies to macroeconomic and financial shocks—especially volatility and illiquidity shocks—over the subprime crisis in order to investigate their market timing activities. In a robustness check, using TVAR (Balke 2000), we simulate the reaction of hedge fund strategies’ betas in extreme scenarios allowing moderate and strong adverse shocks. Our results show that the behavior of hedge fund strategies regarding the monitoring of systematic risk is highly nonlinear in extreme scenarios—especially during the subprime crisis. We find that countercyclical strategies have an investment technology which differs from procyclical ones. During crises, the former seek to capture non-traditional risk premia by deliberately increasing their systematic risk while the later focus more on minimizing risk. Our results suggest that the hedge fund strategies’ betas respond more to illiquidity uncertainty than to illiquidity risk during crises. We find that illiquidity and VIX shocks are the major drivers of systemic risk in the hedge fund industry.

Are conditional illiquidity risks priced in China? A cross-sectional test

In this study, we theoretically derive conditional illiquidity risks from the conditional liquidity-adjusted capital asset pricing model (CLCAPM) that we propose by incorporating funding illiquidity into the LCAPM, and we examine whether they are priced empirically in China's A-share market. We provide new evidence of the positive premiums of conditional illiquidity risks even after controlling for mispricing signals and sentiment. The finding suggests that conditional illiquidity risk could be an alternative channel to explain the cross-section of stock returns. Moreover, investors could obtain higher premiums as compensation for their tolerance of more highly conditional illiquidity risks during high market volatility (low market returns) periods.

Systematic illiquidity, characteristic illiquidity, and stock returns: Timeseries analysis

The objective of this research is to investigate the relationship between illiquidity and stock prices on the Tunisian stock exchange. While previous researches tended to focus on one form of illiquidity to examine this relationship, our study unifies three forms of illiquidity at the same time. Indeed, we simultaneously consider illiquidity as systematic risk, as a characteristic of the market, and as a characteristic of the stock. The aggregate illiquidity of the market is the average of individual stock illiquidity. The illiquidity risk is the sensitivity of the stock price to illiquidity shocks. Shocks of market illiquidity are estimated by the innovations in the expected market illiquidity. Results show that investors on the Tunisian stock exchange do not require higher returns when they expect a rise of market illiquidity, whereas investors on U.S markets are compensated for higher expected market illiquidity. In addition, shocks of market illiquidity provoke a fall in stock prices of small caps, while large caps are not sensitive to market illiquidity shocks. This differs slightly from results based on U.S. data where illiquidity shocks reduce all stock prices but most notably those of small caps. Robustness tests validate our findings. Our results are consistent with previous studies which reported that the “zero-return” ratio predicts significantly the return-illiquidity relationship on emerging markets.

A Novel Modeling Technique for the Forecasting of Multiple‐Asset Trading Volumes: Innovative Initial‐Value‐Problem Differential Equation Algorithms for Reinforcement Machine Learning

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear‐cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one either, but we attempt to derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple‐asset portfolio(s), as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial‐value‐problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple‐asset portfolio(s) during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed‐form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half‐life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things (IoT) data analytics, expert systems in finance, FinTech, and within big data ecosystems.

Illiquidity risk and equity trading performance: A statistical analysis

Subject. The article considers the development of special statistical methods for estimating and analyzing the liquidity risk. Objectives. The purpose is to improve the methodology for statistical appraisal and analysis of risk in equity trading. Methods. The study rests both on well-known methods for equity risk analysis and my own development results (calculation of equity annual illiquidity ratio, formation of illiquidity risk factor). The data analyzed in this paper come from the Moscow Exchange (MOEX) and cover January 2011 to May 2021. The sample included all common stocks traded on MOEX and issuers’ financial statements. I also apply analysis and synthesis, induction and deduction, and methods of comparison and grouping. Results. I calculated monthly illiquidity factor as zero-investment long-short portfolio. I examined the impact of illiquidity risk on the return dynamics of size, book-to-market ratio sorted portfolios. Conclusions. The study shows that expected equity returns are related cross-sectionally to the illiquidity factor. The evidence strongly supports the hypothesis that the illiquidity risk factor is priced. The premium for this risk is positive and offers higher expected returns in equities with strong illiquidity. However, for liquid equities no significant premium is revealed. The offered approach to the factor equity risk analysis based on illiquidity risk enables a true picture of how the risks impact the equity trading performance and how they can be improved in the future.

Money, privacy, anonymity: What do experiments tell us?

The attention paid to the role of money as a store of privacy is increasing. In a monetary transaction, full privacy protection coincides with anonymity. In such situations, an empirical question arises: Is anonymity relevant in shaping the demand for money? We attempt to answer this question through laboratory experiments. The results show that anonymity matters and increases the overall appeal of a medium of payment, and that this effect is stronger for risk-prone individuals. Moreover, the trade-off between the two properties of liquidity and return is relatively high – to accept higher illiquidity risks, individuals require a more-than-proportional increase in the expected return. In general, the experiments suggest that the future attractiveness of alternative currencies depends on whether the three properties of money are mixed in a way that is consistent with the individual’s preferences.

Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets

The paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson methodology in which we utilize the curve-fitting error as an indicator of financial system illiquidity. We empirically apply our method to a set of 10 divergent Central and Eastern Europe countries—Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia—in the period of 2006–2020. The results show three periods of increased risk in the sample period: the global financial crisis, the European public debt crisis, and the COVID-19 pandemic. They also allow us to identify three divergent sets of countries with different systemic liquidity risk characteristics. The analysis also illustrates the impact of the introduction of the euro on systemic illiquidity risk. The proposed methodology may be of consequence for financial system regulators and macroprudential bodies: it allows for contemporaneous monitoring of discussed risk at a minimal cost using well-known models and easily accessible data.

Money in the Equilibrium of Banking1

AbstractMoney is both the medium for transaction and the most liquid asset for banks. We examine both roles of money in an integrated framework, where banks are subject to aggregate illiquidity risk. An active central bank can replicate the constrained efficient allocation, which, however, cannot be implemented in the market equilibrium: due to moral hazard problems, banks invest excessively in illiquid assets, forcing the central bank to provide liquidity at low interest rates. An interest rate policy aiming to reduce systemic liquidity risk is dynamically inconsistent. Instead, the constrained efficiency can be achieved by imposing ex ante liquidity coverage requirement.

Illiquidity Risks in Lending Protocols (LPs): Evidence from Aave Protocol

Illiquidity Risks in Lending Protocols (LPs): Evidence from Aave Protocol

Inflexible Hedging in the Presence of Illiquidity and Jump Risks

Inflexible Hedging in the Presence of Illiquidity and Jump Risks