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New low-frequency spread measures

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  • Holden, Craig W.
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    Abstract

    I develop new spread proxies that pick up on three attributes of the low-frequency (daily) data: (1) price clustering, (2) serial price covariance accounting for midpoint prices on no-trade days, and (3) the quoted spread that is available on no-trade days. I develop and empirically test two different approaches: an integrated model and combined models. I test both new and existing low-frequency spread measures relative to two high-frequency benchmarks (percent effective spread and percent quoted spread) on three performance dimensions: (1) higher individual firm correlation with the benchmarks, (2) higher portfolio correlation with the benchmarks, and (3) lower distance relative to the benchmarks. I find that on all three performance dimensions the new integrated model and the new combined model do significantly better than existing low-frequency spread proxies.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Financial Markets.

    Volume (Year): 12 (2009)
    Issue (Month): 4 (November)
    Pages: 778-813

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    Handle: RePEc:eee:finmar:v:12:y:2009:i:4:p:778-813

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    Web page: http://www.elsevier.com/locate/finmar

    Related research

    Keywords: Liquidity Effective spread Transaction cost Asset pricing Market efficiency;

    References

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    Cited by:
    1. Hagströmer, Björn & Hansson, Björn & Nilsson, Birger, 2013. "The components of the illiquidity premium: An empirical analysis of US stocks 1927–2010," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4476-4487.
    2. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2013. "Liquidity measurement in frontier markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 1-12.
    3. Hagströmer, Björn & Nilsson, Birger & Hansson, Björn, 2011. "The components of the illiquidity premium: An empirical analysis of U.S. stocks 1927-2010," Working Papers 2011:24, Lund University, Department of Economics.
    4. Erten, Irem & Okay, Nesrin, 2012. "Deciphering Liquidity Risk on the Istanbul Stock Exchange," MPRA Paper 56148, University Library of Munich, Germany, revised 2012.
    5. Michael Bleaney & Zhiyong Li, 2013. "The performance of bid-ask spread estimators under less than ideal conditions," Discussion Papers 13/05, University of Nottingham, School of Economics.
    6. Chung, Kee H. & Zhang, Hao, 2014. "A simple approximation of intraday spreads using daily data," Journal of Financial Markets, Elsevier, vol. 17(C), pages 94-120.
    7. Gow-Cheng Huang & Kartono Liano & Ming-Shiun Pan, 2011. "REIT Stock Splits and Liquidity Changes," The Journal of Real Estate Finance and Economics, Springer, vol. 43(4), pages 527-547, November.
    8. Darshana D. Palkar & Niranjan Tripathy, 2011. "Seasoned equity offerings: stock market liquidity and duration of the completion cycle," Managerial Finance, Emerald Group Publishing, vol. 37(4), pages 380-405, April.
    9. Lin, Chien-Chih, 2014. "Estimation accuracy of high–low spread estimator," Finance Research Letters, Elsevier, vol. 11(1), pages 54-62.
    10. Dey, Malay K. & Wang, Chaoyan, 2012. "Return spread and liquidity: Evidence from Hong Kong ADRs," Research in International Business and Finance, Elsevier, vol. 26(2), pages 164-180.
    11. Karstanje, Dennis & Sojli, Elvira & Tham, Wing Wah & van der Wel, Michel, 2013. "Economic valuation of liquidity timing," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5073-5087.

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