21 Pages Posted: 26 Jul 2013
Date Written: July 24, 2013
This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of predictability in S&P 500 returns even when the confidence intervals are constructed using model-free methods based on sub-sampling.
Keywords: Predictive regressions, semiparametric methods, local-to-unity, long memory, long-horizon regressions, subsampling
JEL Classification: C12, C14, G12, G14, E47
Suggested Citation: Suggested Citation
Sizova, Natalia, A Frequency-Domain Alternative to Long-Horizon Regressions with Application to Return Predictability (July 24, 2013). Available at SSRN: https://ssrn.com/abstract=2297934 or http://dx.doi.org/10.2139/ssrn.2297934
By Andrew Ang