Hostname: page-component-5b777bbd6c-2hk6m Total loading time: 0 Render date: 2025-06-22T07:57:44.621Z Has data issue: false hasContentIssue false
  • English
  • Français

An Examination of the Robustness of the Weekend Effect

Published online by Cambridge University Press:  06 April 2009

Robert A. Connolly
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper analyzes the robustness of the day-of-the-week (DOW) and weekend effects to alternative estimation and testing procedures. The results show that sample size can distort the interpretation of classical test statistics unless the significance level is adjusted downward. Specification tests reveal widespread departures from OLS assumptions. Hypothesis tests results are reported using robust econometric methods and a GARCH model. The strength of the DOW and weekend effect evidence appears to depend on the estimation and testing method. Both effects seem to have disappeared by 1975.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 24 , Issue 2 , June 1989 , pp. 133 - 169
Copyright
Copyright © School of Business Administration, University of Washington 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bassett, G. Jr, and Koenker, R.. “Asymptotic Theory of Least Absolute Error Regression.” Journal of the American Statistical Association, 73 (09. 1978), 618–622.CrossRefGoogle Scholar
Bassett, G. Jr, and Koenker, R. “An Empirical Quantile Function for Linear Models with iid Errors.” Journal of the American Statistical Association, 77 (06 1982), 407–415.Google Scholar
Belsley, D. A.; Kuh, E.; and Welsch, R. E.. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: Wiley&Sons (1980).CrossRefGoogle Scholar
Bera, A. K., and Jarque, C. M.. “Model Specification Tests: A Simultaneous Approach.” Journal of Econometrics, 20 (10. 1982), 58–82.CrossRefGoogle Scholar
Blattberg, R. C., and Gonedes, N. J.. “A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices.” Journal of Business, 41 (04 1974), 244–280.CrossRefGoogle Scholar
Bollerslev, T. “A Conditionally Heteroscedastic Time Series Model for Speculative Prices and Rates of Return.” Review of Economics and Statistics, 69 (08. 1987), 542–547.CrossRefGoogle Scholar
Bollerslev, T. “Generalized Autoregressive Conditional Heteroscedasticity.” Journal of Econometrics, 31 (04 1986), 307–327.CrossRefGoogle Scholar
Bollerslev, T. “Integrated ARCH and Cointegration in Variance.” Working Paper, Northwestern Univ. (04 1988).Google Scholar
Chesher, A., and Jewitt, I.. “The Bias of a Heteroscedasticity Consistent Covariance Matrix Estimator.” Econometrica, 55 (09. 1987), 1217–1222.CrossRefGoogle Scholar
Connolly, R. A. “A Posterior Odds Analysis of the Weekend Effect.” Working Paper, Univ. of California, Irvine (06 1988a).Google Scholar
Connolly, R. A. “GARCH Models of Equity and Bond Market Volatility.” Working Paper, Univ. of California, Irvine (10. 1988b).Google Scholar
Connolly, R. A., and McMillan, H.. “Time Conditional Variances and Event Studies: The Case of Capital Structure Changes.” Working Paper, Univ. of California, Irvine (03 1989).Google Scholar
Cornell, B., and Dietrich, J. K.. “Mean-Absolute-Deviation Versus Least Squares Regression Estimation of Beta Coefficients.” Journal of Financial and Quantitative Analysis, 13 (03 1978), 123–131.CrossRefGoogle Scholar
Engle, R. F. “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U. K. Inflation.” Econometric, 50 (07 1982), 987–1008.CrossRefGoogle Scholar
Engle, R. F., and Bollerslev, T.. “Modelling the Persistence of Conditional Variances.” Econometric Reviews, 5 (1986), 1–50.CrossRefGoogle Scholar
Engle, R. F.; Hendry, D. F.; and Trumble, D.. “Small-Sample Properties of ARCH Estimators and Tests.” Canadian Journal of Economics, 18 (02. 1985), 66–93.CrossRefGoogle Scholar
Fama, E. F. “Mandlebrot and the Stable Paretian Hypothesis.” Journal of Business, 36 (10. 1963), 420–429.CrossRefGoogle Scholar
Fama, E. F. “The Behavior of Stock Market Prices.” Journal of Business, 38 (01. 1965), 34–105.CrossRefGoogle Scholar
Flannery, M., and Protopapadakis, A.. “From T-Bills to Common Stocks: Investigating the Generality of Intra-Week Return Seasonality.” Journal of Finance, 43 (06 1988), 431–450.Google Scholar
French, K. R. “Stock Returns and the Weekend Effect.” Journal of Financial Economics, 8 (03 1980), 55–69.CrossRefGoogle Scholar
French, K., and Roll, R.. “Stock Return Variances: The Arrival of Information and the Reaction of Traders.” Journal of Financial Economics, 17 (09. 1986), 5–26.CrossRefGoogle Scholar
Gibbons, M. R., and Hess, P.. “Day of the Week Effects and Asset Returns.” Journal of Business, 54 (10. 1981), 579–596.CrossRefGoogle Scholar
Gilstein, C. Z., and Leamer, E. E.. “Robust Sets of Regression Estimates.” Econometrica, 51 (03 1983), 321–334.CrossRefGoogle Scholar
Gultekin, M. N., and Gultekin, N. B.. “Stock Market Seasonality: International Evidence.” Journal of Financial Economics, 12 (12. 1983), 469–481.CrossRefGoogle Scholar
Harris, L. “A Transaction Data Study of Weekly and Intradaily Patterns in Stock Returns.” Journal of Financial Economics, 16 (05 1986), 99–117.CrossRefGoogle Scholar
Huber, P. J. “Robust Estimation of a Location Parameter.” Annals of Mathematical Statistics, 35 (1964), 73–101.CrossRefGoogle Scholar
Huber, P. J. “Robust Regression: Asymptotics, Conjectures, and Monte Carlo.” The Annals of Statistics, 1 (09. 1973), 799–821.CrossRefGoogle Scholar
Huber, P. J.. Robust Statistics. New York: John Wiley & Sons (1981).CrossRefGoogle Scholar
Jaffe, J., and Westerfield, R.. “The Week-End Effect in Common Stock Returns: The International Evidence.” Journal of Finance, 41 (06 1985), 433–454.Google Scholar
Joiner, B. L., and Hall, D. L.. “The Ubiquitous Role of f/f in Efficient Estimation of Location.” The American Statistician, 37 (05 1983), 128–133.Google Scholar
Judge, G. G.; Griffiths, W. E.; Carter, R. C.; Lutkepohl, H.; and Lee, T. -C.. The Theory and Practice of Econometrics, 2nd ed.New York: John Wiley & Sons (1985).Google Scholar
Keim, D. B., and Stambaugh, R. F.. “A Further Investigation of the Weekend Effect in Stock Returns.” Journal of Finance, 39 (07 1984), 819–840.CrossRefGoogle Scholar
Klein, R. W., and Brown, S. J.. “Model Selection when There Is Minimal Prior Information.” Econometrica, 52 (09. 1984), 1291–1312.CrossRefGoogle Scholar
Koenker, R. “Robust Methods in Econometrics.” Econometric Reviews, 1 (1982), 213–255.Google Scholar
Koenker, R., and Basset, G. Jr, “Regression Quantiles.” Econometrica, 46 (01. 1978), 33–50.CrossRefGoogle Scholar
Lakonishok, J., and Levi, M.. “Weekend Effects on Stock Returns: A Note.” Journal of Finance, 37 (06 1982), 883–889.CrossRefGoogle Scholar
Leamer, E. E. “Global Sensitivity Results for Generalized Least Squares Estimates.” Journal of the American Statistical Association, 79 (12. 1984), 867–870.CrossRefGoogle Scholar
Leamer, E. E. “Sets of Posterior Means with Bounded Variance Priors.” Econometrica, 50 (05 1982), 725–736.CrossRefGoogle Scholar
Leamer, E. E.. Specification Searches: Ad-hoc Inference with Non-experimental Data. New York: John Wiley & Sons (1978).Google Scholar
Lehmann, E. L.Testing Statistical Hypotheses. New York: John Wiley & Sons (1959).Google Scholar
Lindley, D. V. “A Statistical Paradox.” Biometrika, 44(06 1957), 187–192.CrossRefGoogle Scholar
Lindley, D. V., and Scott, W. F.. New Cambridge Elementary Statistical Tables. Cambridge: Cambridge Univ. Press (1984).Google Scholar
Maddala, G. S.Econometrics. New York: McGraw-Hill Book Co. (1977).Google Scholar
Mandlebrot, B. B. “The Variation of Certain Speculative Prices.” Journal of Business, 36 (10. 1963), 394–419.CrossRefGoogle Scholar
McGill, R.; Tukey, J. W.; and Larsen, W. E.. “Variations of Box Plots.” The American Statistician, 32 (02. 1978), 12–16.CrossRefGoogle Scholar
Mclnish, T. H., and Wood, R. A.. “Intraday and Overnight Returns and Day-of-the-Week Effects.” Journal of Financial Research, 8 (Summer 1985), 119–126.CrossRefGoogle Scholar
Poirier, D. J.; Tello, M. D.; and Zin, S. E.. “A Diagnostic Test for Normality within the Power Exponential Family,” Journal of Business & Economic Statistics, 4 (07 1986), 359–373.CrossRefGoogle Scholar
Rogalski, R. J. “New Findings Regarding Day-of-the-Week Returns over Trading and Non-Trading Periods: A Note.” Journal of Finance, 39 (12. 1984), 1603–1614.Google Scholar
Ruppert, D., and Carroll, R. J.. “Trimmed Least Squares Estimation in the Linear Model.” Journal of the American Statistical Association, 75 (12. 1980), 828–838.CrossRefGoogle Scholar
Shanken, J. “A Bayesian Approach to Testing Portfolio Efficiency.” Journal of Financial Economics, 18 (12. 1987), 195–215.CrossRefGoogle Scholar
Smirlock, M., and Starks, L.. “Day of the Week Effects in Stock Returns: Some Intraday Evidence.” Journal of Financial Economics, 17 (09. 1986), 197–210.CrossRefGoogle Scholar
Tukey, J. W.Exploratory Data Analysis. Reading, MA: Addison-Wesley (1977).Google Scholar
Velleman, P. F., and Hoaglin, D. C.. Applications, Basics and Computing of Exploratory Data Analysis. Boston: Duxbury Press (1981).Google Scholar
Wallace, T. D. “Weaker Criteria and Tests for Linear Restrictions in Regression.” Econometrica, 40 (07 1972), 689–698.CrossRefGoogle Scholar
White, H. “A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity.” Econometrica, 48 (05 1980), 817–838.CrossRefGoogle Scholar
Yohai, V. J., and Maronna, R. A.. “Asymptotic Behavior of M-Estimators for the Linear Model.” The Annals of Statistics, 7 (03 1979), 258–268.CrossRefGoogle Scholar
Zellner, A.Basic Issues in Econometrics. Chicago: The Univ. of Chicago Press (1984).Google Scholar