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Modelling day-of-the-week seasonality in the S&P 500 index

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  • Philip Hans Franses
  • Richard Paap

Abstract

A time series model is proposed that describes the day-of-the-week seasonality in returns as well as in volatility of the daily S&P 500 index. The model is a periodic autoregression with periodically integrated GARCH [PAR-PIGARCH]. With this statistically adequate model, positive (negative) autocorrelation is found in the returns on Monday (Tuesday). Day-of-the-week variation in the persistence of volatility is also found.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 10 (2000)
Issue (Month): 5 ()
Pages: 483-488

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Handle: RePEc:taf:apfiec:v:10:y:2000:i:5:p:483-488

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Cited by:
  1. Wessel Marquering & Johan Nisser & Toni Valla, 2006. "Disappearing anomalies: a dynamic analysis of the persistence of anomalies," Applied Financial Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 16(4), pages 291-302.
  2. Theissen, Erik, 2005. "An analysis of private investors' stock market return forecasts," CFR Working Papers 05-16, University of Cologne, Centre for Financial Research (CFR).
  3. Christian Francq & Roch Roy & Abdessamad Saidi, 2011. "Asymptotic Properties of Weighted Least Squares Estimation in Weak PARMA Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(6), pages 699-723, November.
  4. Eduardo Rossi & Dean Fantazzini, 2012. "Long memory and Periodicity in Intraday Volatility," DEM Working Papers Series 015, University of Pavia, Department of Economics and Management.
  5. Christos S. Savva & Denise R. Osborn & Len Gill, 2006. "Periodic Dynamic Conditional Correlations between Stock Markets in Europe and the US," Centre for Growth and Business Cycle Research Discussion Paper Series 77, Economics, The Univeristy of Manchester.
  6. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2007. "The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts," Tinbergen Institute Discussion Papers 07-036/4, Tinbergen Institute.
  7. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(4), pages 707-724.
  8. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2007. "The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts," Tinbergen Institute Discussion Papers 07-036/4, Tinbergen Institute.

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