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Illiquidity Premia in Asset Returns: An Empirical Analysis of Hedge Funds, Mutual Funds, and US Equity Portfolios

Author

Listed:
  • Amir E. Khandani

    (Morgan Stanley, MIT Laboratory for Financial Engineering, USA)

  • Andrew W. Lo

    (MIT Sloan School of Management, 100 Main street, E62-618, Cambridge, MA, 02142, USA;
    MIT Laboratory for Financial Engineering, AlphaSimplex Group, LLC, USA)

Abstract

We establish a link between illiquidity and positive autocorrelation in asset returns among a sample of hedge funds, mutual funds, and various equity portfolios. For hedge funds, this link can be confirmed by comparing the return autocorrelations of funds with shorter vs. longer redemption-notice periods. We also document significant positive return-autocorrelation in portfolios of securities that are generally considered less liquid, e.g., small-cap stocks, corporate bonds, mortgage-backed securities, and emerging-market investments. Using a sample of 2,927 hedge funds, 15,654 mutual funds, and 100 size- and book-to-market-sorted portfolios of US common stocks, we construct autocorrelation-sorted long/short portfolios and conclude that illiquidity premia are generally positive and significant, ranging from 2.74% to 9.91% per year among the various hedge funds and fixed-income mutual funds. We do not find evidence for this premium among equity and asset-allocation mutual funds, or among the 100 US equity portfolios. The time variation in our aggregated illiquidity premium shows that while 1998 was a difficult year for most funds with large illiquidity exposure, the following four years yielded significantly higher illiquidity premia that led to greater competition in credit markets, contributing to much lower illiquidity premia in the years leading up to the Financial Crisis of 2007–2008.

Suggested Citation

  • Amir E. Khandani & Andrew W. Lo, 2011. "Illiquidity Premia in Asset Returns: An Empirical Analysis of Hedge Funds, Mutual Funds, and US Equity Portfolios," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 205-264.
  • Handle: RePEc:wsi:qjfxxx:v:01:y:2011:i:02:n:s2010139211000080
    DOI: 10.1142/S2010139211000080
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    References listed on IDEAS

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    1. Carey, Mark & Stulz, René M. (ed.), 2007. "The Risks of Financial Institutions," National Bureau of Economic Research Books, University of Chicago Press, number 9780226092850, November.
    2. Baquero, Guillermo & ter Horst, Jenke & Verbeek, Marno, 2005. "Survival, Look-Ahead Bias, and Persistence in Hedge Fund Performance," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(3), pages 493-517, September.
    3. Vikas Agarwal & Naveen D. Daniel & Narayan Y. Naik, 2011. "Do Hedge Funds Manage Their Reported Returns?," The Review of Financial Studies, Society for Financial Studies, vol. 24(10), pages 3281-3320.
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    3. Eduard Baitinger & Christian Fieberg & Thorsten Poddig & Armin Varmaz, 2015. "Liquidity-driven approach to dynamic asset allocation: evidence from the German stock market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(4), pages 365-379, November.
    4. Ghent, Andra C., 2021. "What’s wrong with Pittsburgh? Delegated investors and liquidity concentration," Journal of Financial Economics, Elsevier, vol. 139(2), pages 337-358.
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    7. Abidi, Nordine & Miquel-Flores, Ixart, 2018. "Who benefits from the corporate QE? A regression discontinuity design approach," Working Paper Series 2145, European Central Bank.
    8. Daniel Barth & Phillip Monin, 2020. "Illiquidity in Intermediate Portfolios: Evidence from Large Hedge Funds," Working Papers 20-03, Office of Financial Research, US Department of the Treasury.
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    10. Laila Taskeen Qazi & Atta Ur Rahman & Saleem Gul, 2015. "Which Pairs of Stocks should we Trade? Selection of Pairs for Statistical Arbitrage and Pairs Trading in Karachi Stock Exchange," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 54(3), pages 215-244.
    11. Roberto Casarin & Domenico Sartore & Marco Tronzano, 2018. "A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 101-114, January.
    12. Masset, Philippe & Weisskopf, Jean-Philippe, 2018. "Wine indices in practice: Nicely labeled but slightly corked," Economic Modelling, Elsevier, vol. 68(C), pages 555-569.

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