Review of Quantitative Finance and Accounting

, Volume 47, Issue 3, pp 673–697 | Cite as

Short-horizon event study estimation with a STAR model and real contaminated events

  • Panayiotis C. Andreou
  • Christodoulos Louca
  • Christos S. Savva
Original Research

Abstract

We propose a test statistic for nonzero mean abnormal returns based on a Smooth Transition Auto Regressive (STAR) model specification. Estimation of STAR takes into account the probability of contaminated events that could otherwise bias the parameters of the market model and thus the specification and power of the test statistic. Using both simulated and real stock returns data from mergers and acquisitions, we find that the STAR test statistic is robust to contaminated events occurring in the estimation window and in the presence of event-induced increase in return variance. Under the STAR test statistic the true null hypothesis is rejected at appropriate levels. Moreover, it exhibits the highest levels of power when compared with other test statistics that are widely and routinely applied in short-horizon event studies.

Keywords

Event studies Test statistics Contaminated events Markov switching regression model Smooth Transition Auto Regressive model 

JEL Classification

G14 G34 

References

  1. Ahern KR (2009) Sample selection and event study estimation. J Bank Financ 16:466–482Google Scholar
  2. Aktas N, De Bodt E, Cousin JG (2007a) Event studies with a contaminated estimation period. J Corp Financ 13:129–145CrossRefGoogle Scholar
  3. Aktas N, De Bodt E, Cousin JG (2007b) Assessing the power and the size of the event study method through the decades. In Finance International Meeting AFFI-EUROFIDAI, Paris, DecemberGoogle Scholar
  4. Aktas N, De Bodt E, Roll R (2009) Learning, hubris and corporate serial acquisitions. J Corp Financ 15:543–561CrossRefGoogle Scholar
  5. Arora RK, Das H, Jai PK (2009) Stock returns and volatility: evidence from selected emerging markets. Rev Pacific Basin Financ Mark Polic 12:567–592CrossRefGoogle Scholar
  6. Ball CA, Torous WN (1988) Investigating security-price performance in the presence of event-date uncertainty. J Financ Econ 22:123–153CrossRefGoogle Scholar
  7. Bhagat S, Dong M, Hirshleifer D, Noah R (2005) Do tender offers create value? New methods and evidence. J Financ Econ 76:3–60CrossRefGoogle Scholar
  8. Boehmer E, Masumeci J, Poulsen AB (1991) Event-study methodology under conditions of event-induced variance. J Financ Econ 30:253–272CrossRefGoogle Scholar
  9. Brown SJ, Warner JB (1980) Measuring security price performance. J Financ Econ 8:205–258CrossRefGoogle Scholar
  10. Brown SJ, Warner JB (1985) Using daily stock returns: the case of event studies. J Financ Econ 14:3–31CrossRefGoogle Scholar
  11. Cam MA, Ramiah V (2014) The influence of systematic risk factors and econometric adjustments in catastrophic event studies. Rev Quant Financ Acc 42:171–189CrossRefGoogle Scholar
  12. Campbell JY, Lettau M, Malkiel BG, Xu Y (2001) Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. J Financ 56:1–43CrossRefGoogle Scholar
  13. Campbell CJ, Cowan AR, Salotti V (2010) Multi-country event-study methods. J Bank Financ 34:3078–3090CrossRefGoogle Scholar
  14. Chang WC, Weiss DE (1991) An examination of the time series properties of beta in the market model. J Am Stat Assoc 86:883–890CrossRefGoogle Scholar
  15. Chiang T, Tan L, Li J, Nelling E (2013) Dynamic herding behavior in Pacific-basin markets: evidence and implications. Multinatl Financ J 17:165–200CrossRefGoogle Scholar
  16. Corrado CJ (1989) A nonparametric test for abnormal security-price performance in event studies. J Financ Econ 23:385–395CrossRefGoogle Scholar
  17. Corrado CJ, Zivney TL (1992) The specification and power of the sign test in event study hypothesis tests using daily stock returns. J Financ Quant Anal 27:465–478CrossRefGoogle Scholar
  18. Cyree KB, DeGennaro RP (2002) A generalized method for detecting abnormal returns and changes in systematic risk. Rev Quant Financ Acc 19:399–416CrossRefGoogle Scholar
  19. Davidson R, MacKinnon JG (1998) Graphical methods for investigating the size and power of hypothesis tests. Manch Sch 66:1–26CrossRefGoogle Scholar
  20. Délèze F, Hussain S (2014) Information arrival, jumps and cojumps in European financial markets: evidence using tick by tick data. Multinatl Financ J 18:169–213CrossRefGoogle Scholar
  21. Deschamps PJ (2008) Comparing smooth transition and Markov switching autoregressive models of US unemployment. J Appl Econom 23:435–462CrossRefGoogle Scholar
  22. Dombrow J, Rodriguez M, Sirmans CF (2000) A complete nonparametric event study approach. Rev Quant Financ Acc 14:361–380CrossRefGoogle Scholar
  23. Fama E, Fisher L, Jensen M, Roll R (1969) The adjustment of stock prices to new information. Int Econ Rev 10:1–21CrossRefGoogle Scholar
  24. Fuller K, Netter J, Stegemoller M (2002) What do returns to acquiring firms tell us? Evidence from firms that make many acquisitions. J Financ 57:1763–1793CrossRefGoogle Scholar
  25. Guo JM, Petmezas D (2012) What are the causes and effects of M&As? The UK evidence. Multinatl Financ J 16:21–47CrossRefGoogle Scholar
  26. Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384CrossRefGoogle Scholar
  27. Hamilton JD (1994) Time series analysis. Princeton University Press, PrincetonGoogle Scholar
  28. Hansen B (2011) Threshold autoregression in economics. Stat Interface 4:123–127CrossRefGoogle Scholar
  29. Harford J (2005) What drives merger waves? J Financ Econ 77:529–560CrossRefGoogle Scholar
  30. Harrington SE, Shrider DG (2007) All events induce variance: analyzing abnormal returns when effects vary across firms. J Financ Quant Anal 42:229–256CrossRefGoogle Scholar
  31. Hays PA, Upton DE (1986) A shifting regimes approach to the stationarity of the market model parameters of individual securities. J Financ Quant Anal 21:307–321CrossRefGoogle Scholar
  32. Karafiath I (1988) Using dummy variables in the event methodology. Financ Rev 23:351–357CrossRefGoogle Scholar
  33. Kenourgios D, Dimitriou D, Christopoulos A (2013) Asset markets contagion during the global financial crisis. Multinatl Financ J 17:49–76CrossRefGoogle Scholar
  34. Klein A, Rosenfeld J (1987) The influence of market conditions on event-study residuals. J Financ Quant Anal 22:345–351CrossRefGoogle Scholar
  35. Kolari JW, Pynnönen S (2010) Event study testing with cross-sectional correlation of abnormal returns. Rev Financ Stud 23:3996–4025CrossRefGoogle Scholar
  36. Kolari JW, Pynnönen S (2011) Nonparametric rank tests for event studies. J Empir Financ 18:953–971CrossRefGoogle Scholar
  37. Kothari SP, Warner JB (2007) Econometrics of Event Studies. In: Eckbo BE (ed) Handbooks of corporate finance: empirical corporate finance, vol 1. Elsevier/North-Holland, AmsterdamGoogle Scholar
  38. Laopodis NT (2010) Dynamic linkages between monetary policy and the stock market. Rev Quant Financ Acc 35:271–293CrossRefGoogle Scholar
  39. Moeller SB, Schlingemann FP, Stulz RM (2004) Firm size and the gains from acquisitions. J Financ Econ 73:201–228CrossRefGoogle Scholar
  40. Salinger M (1992) Standard errors in event studies. J Financ Quant Anal 27:39–53CrossRefGoogle Scholar
  41. Savickas R (2003) Event-induced volatility and tests for abnormal performance. J Financ Res 26:165–178CrossRefGoogle Scholar
  42. Sharpe WF (1963) A simplified model for portfolio analysis. Manag Sci 9:277–293CrossRefGoogle Scholar
  43. Terasvirta T (1994) Specification, smooth transition autoregressive models. J Am Stat Assoc 89:208–218Google Scholar
  44. Terasvirta T, Anderson HM (1992) Characterizing nonlinearities in business cycles using smooth transition autoregressive models. J Appl Econom 7:119–136CrossRefGoogle Scholar
  45. van Dijk D, Franses PH (1999) Modelling multiple regimes in the business cycle. Macroecon Dyn 3:311–340CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Panayiotis C. Andreou
    • 1
    • 2
  • Christodoulos Louca
    • 1
    • 2
  • Christos S. Savva
    • 1
    • 3
  1. 1.Department of Commerce, Finance and ShippingCyprus University of TechnologyLemesosCyprus
  2. 2.Durham University Business SchoolMill Hill LaneDurhamUK
  3. 3.Centre for Growth and Business Cycle ResearchUniversity of ManchesterManchesterUK

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