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.

Additional Metadata
Keywords autoregression (statistics), stock price indexes, time series analysis
Persistent URL dx.doi.org/10.1080/096031000416352, hdl.handle.net/1765/2176
Series ERIM Article Series (EAS)
Journal Applied Financial Economics
Citation
Franses, Ph.H.B.F, & Paap, R. (2000). Modeling changing day-of-the-week seasonality in the S&P500 index. Applied Financial Economics, 483–488. doi:10.1080/096031000416352