Risk Culture
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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.
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:
The following are market imperfections that lead to illiquidity:
Andrew Ang (2014) summarises many of the critical issues with illiquid asset return data. The following biases contribute to illiquid asset returns being flawed:
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.
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:
$$ \textbf{Figure 1 – Same Asset Return – Quarterly Reporting (left) vs. Daily Reporting (right)} $$
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.
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:
According to economic theory, bearing illiquidity risk should attract a premium, though small.
Schroders (2015) identified four key issues with quantifying the illiquidity premium. They include:
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:
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.
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.
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.
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
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.
Illiquidity risk affects portfolio choice decisions. According to Ang, Papanikolaou, and Westerfield (2013), there are two ways in which this happens:
We should note the following:
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.
The correct answer is A.
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.