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The bond event study methodology since 1974

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Abstract

In the spirit of methodology reviews for stock event studies, like the one prepared by Binder (Rev Quant Financ Account 11:111–137, 1998), this paper discusses the development of the event study methodology for corporate bonds since its first application with Katz (J Financ 29:551–559, 1974). The motivation to conduct this review stems from two sources: First, the methodology utilized for stocks cannot simply be applied to bonds, as bonds present several features that strongly distinguish them from stocks. An erroneous model could lead to false conclusions about the impact of new information on a firm’s debt. Second, the availability of new sources for bond data enables the application of bond event studies for an increasing number of research frameworks. Thus, future research ought to be interested in the selection of the proper methodology. Consequently, this paper illustrates past and present event study methods utilized to calculate abnormal bond returns and reviews the applied parametric and non-parametric test statistics. Besides, insight on how the availability of corporate bond data has evolved through the last four decades, as well as the impact on prevailing methodology is provided. Altogether, this paper provides a first extensive snapshot of the current bond event study methodology and offers guidance for future research.

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Notes

  1. Primary market for bond event studies are the United States.

  2. Journal of Business, Journal of Finance, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, and Review of Financial Studies.

  3. The journal has to have a 5-Year Impact Factor of 1.5 or higher in the 2013 JCR Social Science Edition to count as a top journal.

  4. The amount of price reversal as captured by the negative of the autocovariance of price changes.

  5. See MacKinlay (1997) for calculation details.

  6. Bessembinder et al. (2009) name this method also factor model while according to Eger (1983) it belongs to the market model approach. This thesis follows the understanding of Eger (1983) and defines them as two-index market models.

  7. Option t : is the difference in return between the Lehman Brothers Government National Mortgage Association index and a government bond series with the same duration.

  8. Ellul et al. (2011) call it a “simple market model”. However, since the regression contains more than just market returns, we list it under the section “factor models”.

  9. Corrado (2011) provides the same explanation for stock returns.

  10. “Stationarity is the central assumption of the mean adjusted returns model” (Handjinicolaou and Kalay 1984).

  11. The Aaa sample was too small to split into three subsets. Instead, only two maturity categories were used.

  12. See Ederington et al. (2015) for further details.

  13. When calculated from market model residuals denominator is multiplied by

    \(\sqrt {1 + \frac{1}{{T_{i} }} + \frac{{\left( {R_{mE} - \bar{R}_{m} } \right)^{2} }}{{\mathop \sum \nolimits_{t = 1}^{{T_{i} }} \left( {R_{mt} - \bar{R}_{m} } \right)^{2} }}}\). See Patell (1976), Boehmer et al. (1991) and Corrado (2011) for further details.

  14. See Ederington et al. (2015) for examples.

  15. Ederington et al. (2015) calculate abnormal standardized returns over the (t − 2, t + 2), (t − 3, t + 3), (t − 4, t + 4), and (t − 5, t + 5) window.

  16. Matrix prices are discussed later in greater detail with the Lehman Brother Bond Database.

  17. Data from the Lehman Brothers Fixed Income Database was distributed by Warga (1998) (e.g., Elton et al. 2001).

  18. Trades of $100,000 par value equal 100 bonds with $1000 par value.

  19. For instance, May (2010) calculates accrued interests relying on the raw return for a day on which the bond did not trade.

  20. Chen et al. (2007) argue that daily prices are more regularly available through Datastream after 1995.

  21. Since 2009 Bank of America Merrill Lynch.

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Acknowledgments

This paper is based on the dissertation by Daniel Maul which was created under the supervision of Prof. Dirk Schiereck at the Technische Universität Darmstadt.

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Maul, D., Schiereck, D. The bond event study methodology since 1974. Rev Quant Finan Acc 48, 749–787 (2017). https://doi.org/10.1007/s11156-016-0562-4

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