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Maximum Likelihood Estimates of Regression Coefficients with alpha-stable residuals and Day of Week effects in Total Returns on Equity Indices

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  • John C. Frain

    ()
    (Department of Economics, Trinity College Dublin)

Abstract

This Paper summarizes the theory of Maximum Likelihood Estimation of regressions with alpha-stable residuals. Day of week effects in returns on equity indices, adjusted for dividends (total returns) are estimated and tested using this and traditional OLS methodology. I find that the alpha-stable methodology is feasible. There are some differences in the results from the two methodologies. The conclusion remains that if individual coefficients are of interest and the residuals have fat tails and a possible alpha-stable distribution, the results can be checked for robustness using methods such as those employed here.

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File URL: http://www.tcd.ie/Economics/TEP/2008/TEP0108.pdf
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Bibliographic Info

Paper provided by Trinity College Dublin, Department of Economics in its series Trinity Economics Papers with number tep0108.

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Length: 26 pages
Date of creation: May 2008
Date of revision: May 2008
Handle: RePEc:tcd:tcduee:tep0108

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Postal: Trinity College, Dublin 2
Phone: (+ 353 1) 6081325
Fax: 6772503
Web page: http://www.tcd.ie/Economics/
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Keywords: alpha stable distribution; regression; day of week effects;

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  1. Gibbons, Michael R & Hess, Patrick, 1981. "Day of the Week Effects and Asset Returns," The Journal of Business, University of Chicago Press, vol. 54(4), pages 579-96, October.
  2. Peter Hansen & Asger Lunde, 2003. "Testing the Significance of Calendar Effects," Working Papers 2003-03, Brown University, Department of Economics.
  3. Aleksander Janicki & Aleksander Weron, 1994. "Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook9401.
  4. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2001. "Dangers of data mining: The case of calendar effects in stock returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 249-286, November.
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