A new way to measure liquidity risk using exchange-traded funds seeks to improve what has been notoriously difficult to calculate accurately. This new method removes extraneous factors and helps separate the signal from the noise to fully extract the liquidity premium. As the financial crisis revealed, many large financial institutions were not accurately measuring liquidity risk and its substantial negative impact on their balance sheets, so enhanced liquidity risk measurement should be welcomed.
How Is This Research Useful to Practitioners?
The recent financial crisis demonstrated that many financial institutions were using inadequate measures of liquidity risk.
Historically, measuring liquidity risk has been challenging. Previous research in this area has shown that liquidity risk is difficult to measure accurately because it is a latent factor and cannot be observed directly. The authors’ goals in developing a new measure of liquidity risk are to minimize measurement error, which can be caused by extraneous factors, and to produce a liquidity measure that is less contaminated than previous measures by isolating liquidity risk from all other risk factors. Using exchange-traded funds (ETFs) and their underlying securities, the authors construct long–short portfolios of identically weighted securities. Absent any frictions, the construction of these long–short portfolios should always produce zero returns. The authors’ findings show a systematic pricing disparity, however, between various ETFs and their underlying components.
There are two plausible explanations for this pricing disparity: (1) The market is inefficient and pricing discrepancies represent arbitrage opportunities, or (2) the long–short positions differ in their level of liquidity. If no systematic arbitrage opportunities are possible, the differences must be the result of a liquidity risk premium. Because the ETF has a higher level of liquidity than the underlying securities, the underlying securities will be priced at a discount to compensate for the lower level of liquidity. With fewer than 50% of the securities in the credit markets trading in any given year and fewer than 1% trading every day, a significant amount of liquidity risk is widely thought to be in the credit markets. Understandably, the larger the discrepancy between the portfolios, the greater the illiquidity measured.
The authors’ empirical analysis reveals several important findings: (1) In the fixed-income markets, there is a high correlation between illiquidity measures but return correlations are low; (2) the new measure of bond illiquidity correlates highly with two prior measures; (3) the new illiquidity measure is positively correlated with market volatility (VIX) and the TED (Treasury–Eurodollar) spread, both of which are known generators of illiquidity (as volatility increases, so does illiquidity); (4) the bond illiquidity measure is significantly correlated with the latent liquidity measure even though they are derived from very distinct methodologies; (5) a principal components analysis of the bond illiquidity explains two-thirds of the common variation, which supports the existence of systematic illiquidity in the bond markets; (6) bond illiquidity correlates significantly with equity market illiquidity, with the latter tending to lead the former; and (7) bond illiquidity explains the returns of bond indexes even after controlling for several other asset-pricing factors.
Interestingly, using the same methodology, the authors test a widely held view that the performance of some hedge funds is related to the liquidity risk in their portfolios and find that liquidity risk is a systematic risk factor that explains many hedge fund strategy returns. The data further reveal that many hedge funds have reduced their liquidity risk exposure since the 2008–09 global financial crisis.
How Did the Authors Conduct This Research?
Analyzing 15 ETFs and their underlying securities, the authors use 14 bond ETFs and 1 equity ETF to measure the liquidity premium. The 14 bond ETFs cover a broad range of credit quality and duration. Although a typical index ETF aims to track its underlying index as closely as possible, how it actually does so is a bit more nuanced. The “full” ETF holds 90% or more of the members of the index, whereas the “optimized” ETF holds a representative sample of the index (i.e., less than 90% of the index members). The “derivatives” version uses swaps and futures to synthetically replicate the index return. Finally, the “blended” version uses a combination of derivatives and index members. To extract the illiquidity premium most directly, the authors use the full ETF; for comparison, they use the optimized type. Their sample is split equally between the two types of ETF.
Under a contingent claims approach, the illiquidity measured is the value of an option to exchange the ETF for the underlying securities—basically, the difference between the ETF and the underlying securities upon exercising the option; thus, the value of this option is the value of the liquidity. Because the bid–ask spread can be used as an alternative to the level of liquidity in a market, the authors use the value of the call option plus the value of the put option to measure market liquidity.
The authors’ novel and useful approach to finding the value of the liquidity premium across various fixed-income markets should allow risk managers to adequately measure, better understand, and potentially hedge their financial positions to minimize future market shocks more successfully than during the global financial crisis.
Although I find the results encouraging as a better way to measure the liquidity premium, I would like to see a longer period of analysis. Another topic for future research is how we might analyze such illiquid markets as private equity, venture capital, and private real estate. Sometimes the areas of the market that one cannot measure become tomorrow’s issues.