Limited Time Offer: Save 10% on all 2022 Premium Study Packages with promo code: BLOG10

Illiquid Assets

After completing this reading, you should be able to:

• Explain the essential features of illiquid markets.
• Explain the effects of market imperfections to illiquidity.
• Assess the effects of biases on the reported illiquid assets returns.
• Explain the Geltner-Ross-Zisler unsmoothing process and state its properties.
• Compare liquidity premiums across as well as within asset categories.
• Explain portfolio choice decisions on the incorporation of illiquid assets.

Illiquid assets are the assets for which the optimal sale or purchase strategy entails a time-consuming search. One measure of illiquidity is the average time to sell under optimal pricing.

Characteristics of Illiquid Markets

All assets are Illiquid, only that some assets are more illiquid than others. Infrequent trading, small amounts being traded, and low turnover are some of the manifestations of illiquidity.
For public equities class of assets, the average time between transactions is seconds with an annualized turnover of over 100% while for corporate bonds, the average time between transactions is within a day with turnover in the range of 25-35%.

However, for institutional real estate, the average time between transactions ranges anywhere between 8-11 years with an annualized turnover of approximately 7%. This implies that institutional real estate is more illiquid relative to the other two.

The following are the main characteristics of illiquid markets:

1. Most asset classes are illiquid: One feature of most asset classes are long periods between trades and low turnover except for public equities and fixed income. These include some sub-asset classes of stocks and bonds taking a week or more between transactions and an annual turnover of less than 10%.
2. Illiquid asset markets are significant: The public, liquid markets of stocks, and bonds are smaller than the wealth held in illiquid assets. In 2012, for example, the market capitalization of NYSE and NASDAQ was approximately $17 trillion relative to$16 trillion held in US residential real estate and \$9 trillion held in the institutional real estate market.
3. Investors hold lots of illiquid assets: Illiquid markets dominate most investor’s portfolios. For individuals, illiquid assets represent 90% of their total wealth. The share of illiquid assets in institutional portfolios has increased over the years. The average endowment held a portfolio weight of around 5% in the early 1990s, whereas in 2011, it was more than 25%.
4. Liquidity dries up: Liquidity tends to dry up during periods of severe market distress. During these times, most liquid markets become illiquid. For instance, during the 2008-2009 financial crisis, the market for commercial papers (usually very liquid) experienced “buyers strike” by investors unwilling to trade at any price.

Effects of Market Imperfections

The following are market imperfections that lead to illiquidity:

1. Participation costs: Investors incur costs of market participation, e.g., time and energy, to be able to gain the necessary skills to carry out transactions, monitor market movements, and have ready access to a financial exchange.
2. Transaction cost: To carry out a transaction, one would have to spend money on paying taxes, commission, the costs of due diligence, title transfers, among others.
3. Search frictions: One needs to find the appropriate buyer and seller for many assets. There is no centralized market, which may result in long waiting times before finding a counterparty. For instance, most investors do not have sufficient funds to buy skyscrapers. There are long periods of waiting, and thus prices are negotiated, which might mean that the bid-ask spread will be extended due to lack of competition.
4. Asymmetric information: In a perfect market, all investors have the same information about the payoff of the risky asset; this, however, is not the case in real-life practice. Different investors have different information either because they have various sources of information or have different abilities to process information from the same source. Investors have the reluctance to engage in trade if one of the investors is more knowledgeable than his/her counterparts. In this case, the concern of liquidity suppliers about trading against better-informed agents influences the supply of liquidity.
5. Imperfect competition: In a perfect market setting, all investors are equally competitive and do not affect prices; this is not always the case in practice as some investors are very influential and may have an effect on prices.
6. Demand pressure and inventory risk: When an investor wants to sell an amount of stock, there may not necessarily be any buyers. In perfect markets, a market maker will then buy the asset from the investor, and will then require compensation for the risks that he faces due to warehousing the stock.

Effects of Biases on The Reported Illiquid Assets Returns

Andrew Ang (2014) summarises many of the critical issues with illiquid asset return data. The following biases contribute to illiquid asset returns being flawed:

1. Survivorship bias
2. Selection bias

i. Survivorship Bias

Survivorship bias is the tendency to view the excellent performance of some stocks or funds in the market as a representative sample overlooking those that have not performed. Survivorship bias results in the overestimation of the historical performance of a fund or market index, often leading to investors making misguided investment decisions based on published investment fund return data.

ii. Selection Bias

Sampling selection bias occurs when returns on assets are reported only when they are high and overlooked when they are low. This selection bias is witnessed in private equity, where companies are only taken out when stock values are high.

Andrew Ang (2014) argued that when one uses the reported returns to compute estimates of risk with infrequent trading, he or she is likely to underestimate the risks (volatilities, correlations, and betas). For instance, if the returns are sampled quarterly rather than daily, then the information obtained is not an accurate representation of the real returns. Therefore, this misrepresentation leads to the wrong estimation of the risks. By simulation, Andrew Ang (2014) obtained the following graphs:

$$\textbf{Figure 1 – Same Asset Return – Quarterly Reporting (left) vs. Daily Reporting (right)}$$

Geltner-Ross-Zisler Unsmoothing Process and its Properties

The risks and performance of illiquid assets is unknown due to the difficulty in measuring these quantities with standard techniques. Usually, the reported returns partially reflect past changes in economic values when reported. However, economic values differ due to infrequent trading. This smoothing effect creates bogus return autocorrelation and invalidates traditional measures of risk and performance, Couts, Gonçalves, and Rossi (2019).

Andrew Ang (2014) compares unsmoothing to moving from infrequent (e.g., quarterly) sampling to daily sampling. As observed in Figure 1, the quarterly sampling on the left looks a bit smooth, whereas the graph on the right looks unsmooth. In practice, returns are noisier and, therefore, don’t always look smooth.

Let’s now look at the Geltner-Ross-Zisler unsmoothing process. Denote the actual return at the end of the period $$t$$ as $$\text r_{\text t}^{*}$$ which is unobservable and the reported return as $$\text r_{\text t}^{*}$$ which is observable. Suppose the observable returns follow:

$${\text r }_{\text t }^{ * }=\text C+\phi {\text r }_{ \text t-1 }^{ * }+{ \varepsilon }_{\text t }\quad \quad (1)$$

Where:

$$\phi$$ is the autocorrelation coefficient and is less than 1 in absolute value;

C is drift term; and

$$\varepsilon_{\text t}$$ an error term.

The above equation is an autoregressive process in which the current value is based on the immediately preceding value, the autoregressive process of order 1, AR (1). The equation is used to invert out the actual returns when the observed returns are functions of current and lagged actual returns. If the smoothing process involves only the averaging returns for this period and the prior period, then the observed returns can be filtered to estimate the actual returns, from observed returns, $$\text r_{\text t}^{*}$$ using:

$${ \text r }_{\text t }=\cfrac { 1 }{ 1-\phi } {\text r }_{\text t }^{ * }-\cfrac { \phi }{ 1-\phi } {\text r}_{ \text t-1 }^{ * } \quad \quad (2)$$

Equation (2) unsmooths the observed returns. If the assumption on the transfer function is correct, then the observed returns obtained by (2) will have zero autocorrelation. We should note that the variance of the actual returns is higher than that of the observed returns:

$$\text {var}\left( { \text r }_{\text t } \right) =\cfrac { 1+{ \phi }^{ 2 } }{ 1+{ \phi }^{ 2 } } \text {var}\left( { \text r }_{\text t }^{ * } \right) \ge \text {var}\left( {\text r }_{ \text t }^{ * } \right) \quad \quad (3)$$

Unsmoothed returns at time $$t$$, $$\text r_{\text t}^{*}$$ is a weighted average of the actual return at time $$t$$, $$\text r_{\text t}$$ and the lagged unsmoothed return in the previous period, $$\text r_{\text t-1}^{*}$$.

Couts, Gonçalves, and Rossi (2019), on the other hand, argued that these previous techniques represented a crucial first step in measuring the risks of illiquid assets, but did not fully unsmooth the systematic component of returns, and thus understate the importance of risk factors in explaining illiquid assets returns. They provided an adjustment to return unsmoothing techniques to deal with that issue.

Characteristics of Unsmoothing Process

• Unsmoothing only affects risk estimates and not expected returns: Estimates of the mean require only the first and last price observation. The first and the last observations are unchanged by infrequent sampling, thus unsmoothing only changes the volatility estimates.
• Unsmoothing does not affect uncorrelated observed returns: In many cases, reported illiquid asset returns are autocorrelated because illiquid asset values are appraised. The appraisal process induces smoothing because appraisers use both the most recent and comparable sales together with past appraised values. Illiquid asset markets, e.g., real estate, private equity, timber plantations, among others, are markets where information is not available to all participants, and capital cannot be immediately deployed into new investments. Persistent returns characterize informationally inefficient markets with slow-moving capital.
• Unsmoothing is an art: The Geltner-Ross-Zisler unsmoothing uses the simplest possible autocorrelated process, an AR (1), to describe reported returns. Most illiquid assets have more than first-order lag effects. The real estate, for example, has a well-known fourth-order lag arising from many properties being reappraised only annually. A suitable unsmoothing procedure takes a time-series model; this requires excellent statistical skills. It also requires underlying economic knowledge of the structure of the illiquid market to interpret what is a reasonable lag structure.

The illiquidity risk premium is the additional return demanded by investors for assuming the risk of illiquidity. Illiquidity risk premiums compensate investors for the inability to access capital immediately as well as for the withdrawal of liquidity during the illiquidity crisis. The illiquidity risk premium is a natural feature of private assets, for which investors are generally compensated over the cycle. However, in public asset markets, illiquidity risk investors may not always be compensated. The delay in liquidizing an asset at a reasonable price brings about the risk of illiquidity.

The four ways an asset owner can capture illiquidity premiums, according to Andrew Ang (2014) are:

1. Setting a passive allocation to illiquid asset classes;
2. Choosing securities within an asset that is more liquid by engaging in liquidity security selection;
3. Acting as a market maker at the individual security level; and
4. Engaging in dynamic strategies at the aggregate portfolio level.

According to economic theory, bearing illiquidity risk should attract a premium, though small.

Illiquidity Risk Premiums Across Asset Classes

Schroders (2015) identified four key issues with quantifying the illiquidity premium. They include:

1. Difficult in isolating the illiquidity premium from other risk premia: An asset will contain various risks that deserve to be rewarded. Corporate bonds, for example, are exposed to duration, inflation, and credit risk. There is a challenge in determining which part of the overall return is associated with each risk.
2. Illiquid asset return data is flawed: According to Andrew Ang (2014), “reported illiquid asset returns are not returns.” Ang claims that people overstate the expected returns and understate the risk of illiquid assets, which he attributes to three fundamental biases: selection bias, survivorship bias, and infrequent sampling; this poses problems for accurately quantifying the illiquidity premium.
3. The risk of illiquid assets is difficult to measure: The risk of illiquid assets is often underestimated.
4. Illiquidity is not constant: Assets often become harder to sell in times of crisis. Assets that are typically reasonably liquid may see liquidity dry up in the time of crisis, as discussed previously with the example of the commercial papers in 2008-2009.

Most market participants assume that there is a reward for bearing illiquidity across asset classes. However, the following are reasons as to why this is not true:

1. Illiquidity biases: We have looked at various illiquidity biases, including survivorship bias, infrequent sampling, and selection bias. These biases result in the expected returns of illiquid asset classes being overstated using raw data.
2. Ignores risk: Illiquid asset classes contain more than just illiquidity risk. Adjusting for these risks makes illiquid asset classes far less compelling.
3. Lack of “market index” for illiquid asset classes.
4. Manager selection: The dispersion between managers is much higher for investments in hedge funds than for investments in listed equities. Since there is no predefined consensus on the existence of an illiquidity premium, the decision to invest in illiquid asset classes and how successful this is will depend majorly on the ability to select top-performing managers, according to Swensen (2009).

Illiquidity Risk Premiums Within Asset Classes

Within all the major asset classes, more illiquid securities have higher returns, on average than their more liquid counterparts. We consider a few of these classes in the section that follows.

US Treasuries

A well-known liquidity phenomenon in the U.S. Treasury market is the “on-the-run/off-the-run bond spread.” Newly auctioned Treasuries (on the run) are more liquid and have higher prices, and hence lower yields, than seasoned Treasuries (off the run). There is a variance in the spread of these two types of bonds time, reflecting time-varying liquidity conditions in Treasury markets.

A Treasury bond initially carrying a 20-year maturity is the same as a Treasury note. During the financial crisis, Treasury bonds traded lower than Treasury notes by more than 5% on otherwise identical securities. This goes to show that in one of the world’s most essential and liquid markets, these are substantial illiquidity effects.

Corporate Bonds

Within the corporate bond world, there is evidence to suggest that less liquid bonds often have higher returns. Dick-Nielsen, Feldhutter, and Lando (2012) show that the liquidity level premium before the financial crisis was 4 bp for investment-grade and 58 bp for high yield. After the crisis, these premiums went up to 40 to 90 bp for investment-grade securities and 200 basis points for high yield bonds.

The most significant part of the total liquidity premium in this market comes from the liquidity level premium rather than the liquidity risk premium. This liquidity premium in corporate bond markets varies considerably over time, and there may be significant differences in bull and bear markets.

Public Equity

Stocks with low liquidity levels tend to earn higher returns than liquid stocks in equity markets. Illiquidity results in higher returns for private equity, according to Franzoni, Nowak, and Phalippou (2012). However, these premiums have diminished in the recent past, according to Ben-Rephael, Kadan, and Wohl (2015).

Illiquidity risk can help explain the cross-section of equity returns during the crisis in 2008. Some liquid stocks had more significant drawdowns during this period than the more illiquid stocks with lower exposure to illiquidity risks

Illiquid Assets

Franzoni, Nowak, and Phalippou (2012) showed that illiquidity results in higher returns for private equity; this is the same for hedge funds, as demonstrated by Khandani and Lo (2011), and also for real estate as shown by Liu and Qian (2012).

Concerning hedge funds, the risk-adjusted illiquidity risk premiums for some illiquid categories were sometimes as high as 10% per year, for example, in the period 1986-2006. Illiquidity premiums for equity market neutral funds have declined significantly for several reasons, including lower volatility and higher demand for hedge funds over the period 2002-2006.

Portfolio Choice Decisions on the Incorporation of Illiquid Assets

Illiquidity risk affects portfolio choice decisions. According to Ang, Papanikolaou, and Westerfield (2013), there are two ways in which this happens:

1. Liquid and illiquid wealth are imperfect substitutes: For one to meet his/her obligations, either in consumption or payout, there is a need to have liquid assets; otherwise, one will not be able to meet these crucial obligations. The availability of liquid assets ensures that the investor does not get to a position where his/her investment funds are insolvent (or can’t meet his/her immediate expenses); this results in underinvestment in illiquid assets.
2. Fluctuations in the share of illiquid assets: The investor’s ability to fund intermediate obligations depends on his/her liquid assets, and this leads to changes in the share of illiquid assets. The investor will try to balance between liquid and illiquid assets. In one way or another, he/she will be in a situation where there are fewer and some other times more illiquid assets relative to the Merton benchmark; this induces a time-varying risk aversion.

We should note the following:

• Transaction costs models assume that by meeting a particular cost, trade is always possible; this, however, is not true for private equity, real estate, infrastructure, etc. Over a short horizon, there may be no opportunity to find a buyer. Even after finding a buyer, you need to wait for due diligence and complete a legal transfer. Many liquid assets also experienced liquidity freezes during the financial crisis, where no trading was possible because of a lack of counterparties.
• If a risky asset can be traded on average every six months,  the optimal holding of the illiquid asset contingent on the arrival of the liquidity event is 44%. When the average interval between trades is five years, the optimal allocation is 11%. For ten years, this reduces to 5%. As such, illiquidity risk has a tremendous effect on portfolio choice.
• There are no illiquidity “arbitrages.” Investors should not load up on illiquid assets because these assets have illiquidity risk and cannot be continuously traded to construct an “arbitrage.”
• Investors must demand high illiquidity risk premiums. Andrew Ang (2014) came up with a way of calculating the illiquidity premium. He argues that when liquidity events arrive every six months, on average, then an investor should demand an extra 70 basis points and approximately 1% when liquidity comes once a year, on average. When the waiting period is ten years, on average, to exit an investment, one should demand a 6% illiquidity premium.

Practice Question

Illiquid assets can generate excess return because:

A. They allow the transfer of idiosyncratic risk from liquid markets.

B. They have lower transaction costs.

C. They generate arbitrage opportunities.

D. They require large investments.

Liquid asset markets are information efficient. Information regarding the assets is freely available and every participant has equal access. This makes the generation of alpha (excess return) in such markets a challenging task.

However, the market for illiquid assets markets is fraught with information asymmetry. This information asymmetry can be exploited to generate excess returns. Thus, the idiosyncratic risk of liquid assets can be transferred to illiquid assets and help generate excess returns.

Featured Study with Us
CFA® Exam and FRM® Exam Prep Platform offered by AnalystPrep

Study Platform

Learn with Us

Subscribe to our newsletter and keep up with the latest and greatest tips for success
Online Tutoring
Our videos feature professional educators presenting in-depth explanations of all topics introduced in the curriculum.

Video Lessons

Daniel Glyn
2021-03-24
I have finished my FRM1 thanks to AnalystPrep. And now using AnalystPrep for my FRM2 preparation. Professor Forjan is brilliant. He gives such good explanations and analogies. And more than anything makes learning fun. A big thank you to Analystprep and Professor Forjan. 5 stars all the way!
michael walshe
2021-03-18
Professor James' videos are excellent for understanding the underlying theories behind financial engineering / financial analysis. The AnalystPrep videos were better than any of the others that I searched through on YouTube for providing a clear explanation of some concepts, such as Portfolio theory, CAPM, and Arbitrage Pricing theory. Watching these cleared up many of the unclarities I had in my head. Highly recommended.
Nyka Smith
2021-02-18
Every concept is very well explained by Nilay Arun. kudos to you man!