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We thank without implication Francis Longstaff, Pete Kyle, and participants to the 7th Oxford Finance Symposium, the October 2008 Bundesbank-CESIfo Conference on “Liquidity: Concepts and Risks”, and the 2008 IMF Annual Research Conference, for comments and suggestions.
See IMF (2007) for a review. For various citations about a world “awash with liquidity”, see the introductory section of Rueffer and Stracca (2006) and Moody’s (2007). Brunnermeier (2008) attempts to rationalize the meaning of “being awash with liquidity” by defining “funding liquidity” as “the ease with which expert investors and arbitrageurs can obtain funding……Funding liquidity is high—and markets are said to be “awash with liquidity’’—when it is easy to borrow money, either uncollateralized or with assets as collateral (our italics)”. A complementary rationalization is in Adrian and Shin (2007, 2008), where fluctuations in financial institutions’ balance sheets are associated with expansions or contraction of credit to the economy.
However, progress in the macroeconomic area is underway. For example, the incomplete markets modeling framework proposed by Kiyotaki and Moore (2008) appears promising in integrating an essential role for money in standard dynamic macroeconomic models, and in delivering testable implications and theory-based measurement of liquidity in an aggregate context.
The finance microstructure literature (reviewed in Hasbrouck, 2007) has focused on modeling and measuring illiquidity costs, starting from the seminal contributions of Roll (1984) and Kyle (1985), among others. Notable recent contributions on market liquidity include Morris and Shin (2003), Pastor and Staumbaugh (2003), Acharya and Pedersen (2005), Brunnermeier and Pedersen (2007), An excellent survey of this work as related to asset pricing is in Amihud, Mendelson and Pedersen (2005).
In an asset allocation context, Longstaff (2001) shows that an increase in liquidity can reduce the risk faced by investors in allocating their wealth in a portfolio of assets. For given levels of risk tolerance, investment opportunities become less risky as liquidity increases. As a result, a larger portion of investors’ wealth may be invested in “risky” assets even though risk tolerance has not changed. This is because the liquidity risk component of each asset has decreased.
For a similar illustration of several types of microstructure models, see Biais, Glosten and Spatt (2005).
In the equilibrium of Kyle’s strategic trade model, λ is an increasing function of the ratio of a measure of fundamental value uncertainty divided by the variance of noise trading.
Illiquidity generating negative serial correlation in returns is also obtained in the models by Ho and Stoll (1981), Grossman and Miller (1988), Campbell, Grossman, and Wang (1993), and Huang and Wang (2008).
Similarly, Chan et al (2006) document illiquidity of hedge funds portfolios looking at their returns autocorrelations.
We simply claim that our measure has the advantage of simplicity and inclusiveness relative to other measures used in the literature. We do not claim superiority in any dimension. Assessing the relative performance of our measure relative to others would require a detailed comparison which is outside the scope of this paper.
The Australasian industrial countries are: Australia, New Zealand, Korea and Singapore. The Emerging Market countries are: Mexico, Argentina, Brazil, Chile, Colombia and Peru (Latin America); China, India, Indonesia, Malaysia, Pakistan, Philippines, and Thailand (Asia); Czech Republic, Hungary, and Poland (Europe).
For other measures of systematic liquidity components see, for example Chorida et al. (2000) and Huberman and Halka (2001)
Assessing the extent to which global components drive spreads appears particularly important in light of the finding of a common liquidity component by Korajczyk and Sadka (2008) for several measures of liquidity across US stock markets. Globally, a counterpart to this result might be found in international financial markets that are sufficiently integrated.