Resumen de Low-Frequency Components and the weekend Effect Revisited: Evidence from Spectral Analysis
We revisit the well-known weekend anomaly (Gibbons and Hess, 1981; Harris, 1986;
Smirlock and Straks, 1986; Connolly, 1989; Giovanis, 2010) using an established macroeconometric technique known as spectral analysis (Granger, 1964; Sargent, 1987). Our findings show that using regression analysis with dichotomous variables, spectral analysis helps establishing the robustness of the estimated parameters based on a sample of the S&P500 for the 1972-1973 period. As further evidence of cycles in financial times series, we relate our application of spectral analysis to the recent literature on low-frequency components in asset returns (Barberis et al., 2001; Grüne and Semmler, 2008; Semmler et al., 2009). We suggest investment practitioners to consider using spectral analysis for establishing the �stylized facts� of the financial time series under scrutiny and for regression models validation purposes
