Journal of Accounting and Investment Vol. 24 No. 3, September 2023 Article Type: Research Paper Exploring illiquidity risk pre and during the COVID-19 pandemic era: Evidence from international financial markets Samuel Tabot Enow* Abstract 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. Keywords: Illiquidity risk; Financial markets; GARCH; Trading volume; Risk premium Introduction Illiquidity in financial markets has made headlines in recent years by springing out many surprises to unsurprising market participants. Apart from making headlines, illiquidity risk and the lack of liquidity are among the most important aspects of portfolio formation and financial market analysis (Pedersen, 2018). AFFILIATION: Research Associate, IIE Varsity College, Durban, South Africa *CORRESPONDENCE: enowtabot@gmail.com DOI: 10.18196/jai.v24i3.18139 CITATION: Enow, S. T. (2023). Exploring illiquidity risk pre and during the COVID-19 pandemic era: Evidence from international financial markets. Journal of Accounting and Investment, 24(3), 672-682. ARTICLE HISTORY Received: 11 Mar 2023 Revised: 21 Mar 2023 Accepted: 02 May 2023 This work is licensed under a Creative Commons Attribution-Non-Commercial- No Derivatives 4.0 International License JAI Website: https://scholar.google.com/citations?user=paOOAfAAAAAJ&hl=en https://www.varsitycollege.co.za/ https://www.varsitycollege.co.za/ mailto:enowtabot@gmail.com http://dx.doi.org/10.18196/jai.v24i3.18139 https://crossmark.crossref.org/dialog/?doi=10.18196/jai.v24i3.18139&domain=pdf https://journal.umy.ac.id/index.php/ai/article/view/18139 Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era: … Journal of Accounting and Investment, 2023 | 673 During the past decade, several liquidity crunches in financial markets have been observed, ranging from the credit crunch in 2007 to the Argentinian crisis in 2002, where investors faced serious liquidity problems. In addition, illiquidity risk can be best understood when considering listed and unlisted securities. Listed securities are perceived to be liquid compared to their counterparts due to the ease with which buyers and sellers can easily exit or enter a position (Enow, 2023). Hence, illiquidity risk normally arises when there are insufficient market participants to trade. It can be on a daily, intra- day, or even monthly basis, where the ability to exit or enter a market quickly is severely impaired. Also, the search time to execute or settle a trade lengthens, which bears little reality regarding financial market trading. It is well documented (Brunnermeier & Pedersen, 2008; Jiao & Sarkissian, 2021) that illiquidity risk in security indexes is sometimes a global issue in which traders face greater security discounts or fire sales. These illiquidity discounts may be as high as 20% (Albuquerque & Schroth, 2015). Several authors also contend that illiquidity risk results from liquidity mismatch between buyers and sellers in financial markets (Sarr & Lybek, 2002; Keating et al., 2016; Marozva, 2017). However, the lack of a price discovery mechanism is unanimously agreed as the main factor (Virgilio, 2022). In essence, market participants cannot rely on quoted prices because they are not fair and efficient, which results in asymmetric information and disequilibrium. Other reasons, such as increased uncertainty in the financial system, insufficient market infrastructure, and inadequate settling mechanisms, have also been attributed to illiquidity risk. Whatever the cause, this type of exposure presents a great danger for market participants, especially when trading large volumes of securities. Since illiquidity risk is not static but varies with time, this study explores the following research questions: 1) What is the state of illiquidity risk before and during financial distress periods? 2) What are the expected exposures regarding illiquidity risk before and during financial distress? 3) What is the expected dollar loss associated with trading 1,000,000 shares of the security index? In providing answers to the above questions, as per the author’s knowledge, this study is the first to empirically explore illiquidity risk and the associated exposures in international financial markets. The findings of this study may be relevant not only for financial market participants but also for institutional traders who engage in trading large positions daily; hence, it is a notable contribution. Literature Review and Hypotheses Development The theoretical underpinning of illiquidity risk stems from Black’s (1971) liquidity preference theory. Black (1971) proposes that illiquidity risk in financial markets arises from slow price recovery mechanisms, mainly from uninformative shocks. Consequently, Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era … Journal of Accounting and Investment, 2023 | 674 the volatility of illiquid markets is exacerbated, resulting in price disequilibrium and may further enhance disparities between an asset's fundamental value and market value. In essence, price volatility is significantly affected by the order flow of trading volumes due to distortions in equilibrium prices (Black, 1971). Moreover, the main source of illiquidity risk is the inability to trade quickly or settle out of a position without significantly moving the market price (Enow, 2023). Essentially, an optional sale or a purchase strategy will involve a considerable amount of time, increasing both the transaction and participation costs required to execute a trade. The above mentioned limitations also characterize asymmetric information due to information disparity between market participants (Crawford, Pavanini & Schivardi, 2018). Market imperfections are another illiquidity risk source, where some market participants have greater influence to move the market by trading out large positions (Acharya & Pedersen, 2019). These large buy or sell positions can move the market price considerably, resulting in a liquidity freeze. In portfolio and fund management, many anomalies increase illiquidity risk: survivorship bias, selection bias, and infrequent trading (Eling & Faust, 2010). Survivorship bias occurs when portfolios with low returns are not included in estimating the total fund’s performance (MacGregor, Schulz & Zhao, 2021). Consequently, the portfolio’s performance is overestimated, which results in misguided investment decisions. Meanwhile, selection bias occurs when the returns of a portfolio are only presented when the fund's value is high (Eling & Faust, 2010). On the other hand, infrequent trading, as the name suggests, occurs when the beta of the security or portfolio is biased due to infrequent trading (Hollstein, Prokopczuk & Simen, 2019). Illiquidity risk can therefore be understood vividly by considering the market depth and breadth level. Therefore, the first set of hypothesis to be examined are; H0: There is no difference in illiquidity risk before or during the financial distress period. H1: Illiquidity risk during the financial distress period is higher than normal. As mentioned, market depth involves trading large positions without any price change (Enow, 2023), while market breadth describes the width of interest, considering the number of participants actively trading. Where there are wider spreads due to large bids and ask prices, financial markets occasionally cease and shut down, as seen in Hong Kong in 1987. The Figure 1 depicts a liquidity framework in financial markets. Prior literature mainly focused on liquidity risk management in asset pricing and financial markets (Jelena & Evica, 2018; Gaurav & Misra, 2019; Guijarro, Moya-Clemente & Saleemi, 2019; King & Lewis, 2020; Enow, 2023). Nevertheless, research on illiquidity risk exposures in financial markets is almost inexistent. Thus, this study also examines the following hypotheses: H2: There is no difference in expected exposures during or before financial distress. Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era: … Journal of Accounting and Investment, 2023 | 675 H3: The expected exposures during the financial distress period are higher than normal due to higher illiquidity risk. As per the author’s knowledge, no prior literature study estimated the illiquidity cost. Considering that illiquidity risk is synonymous with heightened volatility, the market value of a security trading in an illiquid market may be grossly mispriced. 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. Therefore, in providing answers to the above hypotheses, this study significantly contributes to the literature on illiquidity and liquidity management. The section below highlights the research method applied. Figure 1 Illiquidity Risk Framework Research Method To achieve its objectives, this study used a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and the Amihud (2002) illiquidity ratio. The GARCH model was employed to estimate the illiquidity level before and during the pandemic. As stated in the literature, illiquidity risk is present when a significant relationship exists between price returns and trading volumes. Accordingly, the GARCH blueprints provide meaningful time dependency and clustering effects between price returns and trading volumes (Bollerslev, 1986). In essence, the autoregressive properties of these models can be used to predict the unquoted illiquidity in financial markets using the actual values of market price and trading volumes (Paul, Walther & Küster-Simic, 2022). Although these models have been widely utilized for volatility estimates (Enow, 2023; Zahid, Iqbal & High market impact Illiquidity risk Low trading volume Low turnover High transaction cost Low trade s High execution cost Low market capitalization High spread Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era … Journal of Accounting and Investment, 2023 | 676 Koutmos, 2022), they are also suitable for exploring the time-varying properties of illiquidity risk. As Bollerslev (1986) states, a GARCH model can be expressed mathematically. 𝑝𝑡 = α + ϕ𝑃𝑡−1 + β𝑉𝑡−1 2 (1) Where P_t = price returns, α = intercept ϕ = ARCH term, β = GARCH coefficient, and V= trading volume. Amihud’s ratio was used to relate the absolute return of the stock index valuation to its daily volume. Basically, Amihud’s (2002) ratio reveals the amount of illiquidity risk that stock markets cannot absorb. It is explained through the dynamic relationship between price returns and trading volumes, where the number of stocks that needs to be traded to initiate a price move is presented. Considering that illiquidity risk is simply the possibility of losing on price change, the more responsive the absolute return to changes in volume, the more illiquid the market is. Amihud’s (2002) ratio is given by the following. 𝐼𝑙𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 (%) = ∑ |𝑅𝑡| 𝑇𝑉𝑡 𝑛 𝑖=1 (2) Where 𝑅𝑡 is the daily price returns, and TV is the daily trading volume. The daily prices and trading volumes were retrieved from Yahoo Finance for a sample period from June 30, 2017, to June 30, 2019, for the pre-pandemic period and January 1, 2020, to December 31, 2021, for the COVID-19 pandemic. Six financial markets were randomly considered, including the Johannesburg Stock Exchange (JSE), the French Stock Market Index (CAC 40), the German blue-chip companies (DAX) and the Nasdaq Index, the Borsa Istanbul 100 (BIST 100), and Shanghai Stock Exchange (SSE). These markets represent the largest stock indexes in each continent. Then, the section below highlights the findings and discussion of the data analyzed. Results and Discussion This section outlines the findings of the data analyzed. The Table 1 presents the findings regarding illiquidity in financial markets before the COVID-19 pandemic. As expected, no major liquidity concerns were observed in the sampled financial markets. Specifically, all the financial markets under consideration portrayed strong signs of liquidity except for Nasdaq, where a significant relationship between price and trading volume was observed. In essence, trading volumes in the Nasdaq played a major role in determining the market price, giving rise to illiquidity risk. In all the financial markets, the ARCH and GARCH terms were statistically significant at 5%, and their sums were less than one, satisfying the stability conditions (Enow, 2021). The sum of the ARCH and GARCH coefficients also indicates the absence of illiquidity risk during bullish periods. Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era: … Journal of Accounting and Investment, 2023 | 677 Table 1 ARCH and GARCH Output Before the COVID-19 Pandemic JSE Variable Coefficient Std. Error z-Statistic Prob. C -0.000435 0.001148 -0.37938 0.7044 VOLUME 6.46E-09 3.54E-09 1.825683 0.0679 Variance Equation C 5.44E-06 1.44E-06 3.786749 0.0002 ARCH term -0.010177 0.003776 -2.69489 0.007* GARCH term 0.990804 0.00406 244.0619 0.000* CAC 40 C 0.002556 0.001172 2.180934 0.0292 VOLUME -2.34E-11 1.23E-11 -1.90228 0.0571 Variance Equation C 3.99E-06 1.50E-06 2.666836 0.0077 ARCH term 0.120141 0.03094 3.883098 0.0001* GARCH term 0.819849 0.04197 19.534 0.000* DAX C 0.001763 0.001058 1.667041 0.0955 VOLUME -1.71E-11 9.39E-12 -1.81917 0.0689 Variance Equation C 1.71E-06 1.08E-06 1.58162 0.1137 ARCH term 0.057506 0.020447 2.812371 0.0049* GARCH term 0.922256 0.028709 32.12425 0.000* Nasdaq C 0.009289 0.002148 4.324946 0.000* VOLUME -3.81E-12 9.15E-13 -4.16885 0.000* Variance Equation C 6.28E-06 1.58E-06 3.987278 0.0001 ARCH term 0.193338 0.039743 4.864738 0.000* GARCH term 0.763215 0.041493 18.3936 0.000* BIST100 C 0.002461 0.002424 1.015481 0.3099 VOLUME -1.94E-12 1.91E-12 -1.01712 0.3091 Variance Equation C 1.68E-05 8.15E-06 2.058374 0.0396 ARCH term 0.100665 0.030376 3.313935 0.0009* GARCH term 0.805422 0.063685 12.64696 0.000* SSE C -0.000949 0.001107 -0.8576 0.3911 VOLUME 5.76E-09 4.99E-09 1.155699 0.2478 Variance Equation C 1.46E-06 4.81E-07 3.040109 0.0024 ARCH term 0.088921 0.013806 6.440907 0.000* GARCH term 0.910311 0.012703 71.65924 0.000* Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era … Journal of Accounting and Investment, 2023 | 678 Table 2 The Results of Multiple Linear Regression Analysis JSE Variable Coefficient Std. Error z-Statistic Prob. C -0.001072 0.001179 -0.909146 0.3633 VOLUME 3.63E-09 3.91E-09 0.930214 0.3523 Variance Equation C 6.82E-06 3.10E-06 2.204693 0.0275* Arch term 0.051075 0.012829 3.981267 0.0001* GARCH term 0.927408 0.019135 48.46611 0.0000* CAC 40 C 0.005725 0.001038 5.515057 0.0000* VOLUME -6.95E-11 1.08E-11 -6.407809 0.0000* Variance Equation C 7.84E-06 1.54E-06 5.097527 0.0000* Arch term 0.142399 0.024093 5.910482 0.0000* GARCH term 0.819423 0.027645 29.64086 0.0000* DAX C 0.006617 0.001359 4.867457 0.0000* VOLUME -8.81E-11 1.38E-11 -6.363572 0.0000* Variance Equation C 7.26E-06 1.55E-06 4.685315 0.0000* Arch term 0.139982 0.023673 5.913246 0.0000* GARCH term 0.829761 0.025847 32.10240 0.0000* Nasdaq C 0.004749 0.001614 2.941873 0.0033* VOLUME -8.06E-13 3.16E-13 -2.547730 0.0108* Variance Equation C 1.38E-05 4.27E-06 3.220988 0.0013* Arch term 0.223328 0.051878 4.304883 0.0000* GARCH term 0.729801 0.050935 14.32821 0.0000* BIST100 C -0.004814 0.013404 -0.359113 0.7195 VOLUME 1.25E-12 3.30E-12 0.379708 0.7042 Variance Equation C 0.000993 0.002003 0.495889 0.6200 Arch term -0.001486 0.002921 -0.508771 0.6109 GARCH term 0.568798 0.870502 0.653414 0.5135 SSE C 0.001211 0.001751 0.691518 0.4892 VOLUME -2.44E-09 5.31E-09 -0.459770 0.6457 Variance Equation C 1.21E-05 3.58E-06 3.388871 0.0007* Arch term 0.190789 0.019362 9.853939 0.0000* GARCH term 0.723687 0.035193 20.56309 0.0000* The findings of Table 2 are unsurprising as illiquidity in financial markets is expected to increase during periods of distress, such as the COVID-19 pandemic. The CAC 40, DAX and Nasdaq showed illiquidity because of the significant relationship between market price returns and trading volumes. Hence, it was more difficult to trade during the COVID-19 pandemic than before due to wider bid and ask spreads and the difficulty of locating other Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era: … Journal of Accounting and Investment, 2023 | 679 market participants. A potential reason for the increase in illiquidity was the prevalence of asymmetric information, where information was filtered through the market in sequential order. Consequently, the information disparity between buyers and sellers increased. Combining the above findings in Tables 1 and 2 confirm that illiquidity risk in financial markets increased during periods of financial distress. Table 3 outlines the expected cost of illiquidity before and during the COVID-19 market crisis. Table 3 Cost of Illiquidity in Financial Markets Before the COVID-19 pandemic Illiquidity risk (%) Trading Volume Illiquidity Cost (In US dollars) JSE 1.15% 1000000 11540 CAC 40 0.001428% 1000000 14.28 DAX 0.000619% 1000000 6.19 Nasdaq 0.000034% 1000000 0.34 BIST100 0.000005% 1000000 0.05 SSE 1.26% 1000000 12620 During the COVID-19 pandemic JSE 2.2268% 1000000 22268 CAC 40 0.00107% 1000000 10.7 DAX 0.00115% 1000000 11.53 Nasdaq 0.000022% 1000000 0.22 BIST100 0.000003% 1000000 0.03 SSE 0.69% 1000000 6860 On average, the cost of illiquidity risk was higher during a financial crisis than in the pre- crisis era. From Table 3, investors lost around $11,540 for one million JSE traded securities, with a significantly high amount of $12620 in the SSE before the pandemic. However, the illiquidity costs in the BIST100, DAX, and Nasdaq were exceptionally low for each one million batches traded before the pandemic. This finding suggests that market participants trading in the BIST100, DAX, and Nasdaq were very optimistic about their positions, resulting in the low cost of illiquidity. Hence, market participants could easily enter and exit the market. During the pandemic, the cost of illiquidity in the JSE and DAX almost doubled the amount before the pandemic. However, the amount in the SSE reduced significantly during the pandemic. These lower margins were also seen in the CAC 40, Nasdaq, and BIST 100. Therefore, the null and third hypotheses were rejected, and hypotheses one and two were accepted. Therefore, short-term traders and market participants attracted to liquid markets could not easily enter and exit their positions without incurring significant transaction costs. Also, this significant illiquidity risk may result in heightened volatility, enhancing mispricing in financial markets. In essence, market participants in the JSE and SSE will experience increasing price volatility where a small number of transactions will have a significant impact on asset prices. This will create an environment of uncertainty and make it difficult for investors to accurately value assets, leading to larger price swings and potential market distortions. Investors in the JSE and SSE will be more susceptible to market manipulation and information asymmetry as well as a cascade of selling pressures and a domino effect due to the heightened illiquidity risk. Enow Exploring illiquidity risk pre and during the COVID-19 pandemic era … Journal of Accounting and Investment, 2023 | 680 Conclusion An important concept in portfolio management is holding a well-diversified portfolio that can be easily traded with minimal losses or “very low illiquidity risk.” Analyzing illiquidity risk cannot be overlooked as investors tend to withdraw from illiquid markets with low trading prospects. Therefore, this study aimed to empirically explore illiquidity risk in financial markets before and during the COVID-19 pandemic. The findings of this study revealed that illiquidity risk tended to increase in stock indexes during distress periods, increasing illiquidity cost. More specifically, less developed markets, such as the JSE, experienced high illiquidity costs than developed markets, such as the Nasdaq. Policies, such as adequate market microstructure and greater transparency in trading, are strongly recommended for less developed markets, particularly during financial distress periods. Also, with lower risk premiums, investors should strongly consider the BIST100, CAC 40, DAX, and Nasdaq. Different financial markets exhibit varying levels of liquidity and liquidity dynamics. This study assumes a homogenous market structure, which may not capture the nuances of illiquidity risk across different asset classes, sectors, or regions. Future research could explore how to incorporate market heterogeneity into the estimation models, considering factors such as asset-specific characteristics, market microstructure features, and regulatory environments. Also, Illiquidity risk is not necessarily linear and may exhibit time-varying characteristics. Also, the model used in this study to estimate illiquidity risk assumes linearity and constant liquidity measures, which may oversimplify the complexities of illiquidity risk dynamics in the selected financial markets. Future research could investigate nonlinear models that capture changing liquidity regimes, regime shifts, or nonlinear relationships between liquidity measures and asset prices. References Acharya, V. V., & Pedersen, L. H. (2019). Economics with Market Liquidity Risk. Critical Finance Review, 8(1-2), 111-125. https://doi.org/10.1561/104.00000083 Albuquerque, R., & Schroth, E. (2015). The Value of Control and the Costs of Illiquidity. 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