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Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues

Author

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  • Langedijk, Sven
  • Monokroussos, George
  • Papanagiotou, Evangelia

Abstract

We revisit a central task of the extant liquidity literature, which is to identify effective measures of liquidity. We critically assess the influential practice of identifying the best liquidity measures based on monthly correlations by comparing and contrasting correlations between monthly and daily averages of high-frequency benchmarks and low-frequency proxies of liquidity, as well as by examining the coherences between such measures. Furthermore, we propose MIDAS regressions as a way of investigating the bilateral relationships between benchmarks and proxies without averaging out potentially valuable high-frequency information, as is common practice. We conclude that the empirical correlations between benchmarks and proxies in general become weaker as the frequency over which these relationships are examined becomes higher, and that standard practices may lead to misleading conclusions. One implication of our results is that any liquidity measure needs to be assessed against the relevant timeframe for conversion into cash.

Suggested Citation

  • Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2015. "Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues," MPRA Paper 61865, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:61865
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    References listed on IDEAS

    as
    1. Aitken, Michael & Comerton-Forde, Carole, 2003. "How should liquidity be measured?," Pacific-Basin Finance Journal, Elsevier, vol. 11(1), pages 45-59, January.
    2. Alessandro Beber & Michael W. Brandt & Kenneth A. Kavajecz, 2009. "Flight-to-Quality or Flight-to-Liquidity? Evidence from the Euro-Area Bond Market," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 925-957.
    3. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    4. Kremer, Manfred & Lo Duca, Marco & Holló, Dániel, 2012. "CISS - a composite indicator of systemic stress in the financial system," Working Paper Series 1426, European Central Bank.
    5. repec:ecb:ecbwps:20111426 is not listed on IDEAS
    6. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    8. Roll, Richard, 1984. "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-1139, September.
    9. Goyenko, Ruslan Y. & Holden, Craig W. & Trzcinka, Charles A., 2009. "Do liquidity measures measure liquidity?," Journal of Financial Economics, Elsevier, vol. 92(2), pages 153-181, May.
    10. Holden, Craig W., 2009. "New low-frequency spread measures," Journal of Financial Markets, Elsevier, vol. 12(4), pages 778-813, November.
    11. Dániel Holló, 2012. "A system-wide financial stress indicator for the Hungarian financial system," MNB Occasional Papers 2012/105, Magyar Nemzeti Bank (Central Bank of Hungary).
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    Cited by:

    1. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2018. "Benchmarking liquidity proxies: The case of EU sovereign bonds," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 321-329.
    2. Csóka, Péter & Havran, Dániel & Váradi, Kata, 2016. "Konferencia a pénzügyi piacok likviditásáról. Sixth Annual Financial Market Liquidity Conference, 2015 [Conference on the liquidity of financial markets. Sixth Annual Financial Market Liquidity Con," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(4), pages 461-469.

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    More about this item

    Keywords

    Liquidity; Market Microstructure; High-Frequency Data; Sovereign Bonds; MIDAS; Coherence.;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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