Abstract

http://ssrn.com/abstract=2297934
 


 



A Frequency-Domain Alternative to Long-Horizon Regressions with Application to Return Predictability


Natalia Sizova


Rice University

July 24, 2013


Abstract:     
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 con fidence intervals are constructed using model-free methods based on sub-sampling.

Number of Pages in PDF File: 21

Keywords: Predictive regressions, semiparametric methods, local-to-unity, long memory, long-horizon regressions, subsampling

JEL Classification: C12, C14, G12, G14, E47


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Date posted: July 26, 2013  

Suggested Citation

Sizova, Natalia, A Frequency-Domain Alternative to Long-Horizon Regressions with Application to Return Predictability (July 24, 2013). Available at SSRN: http://ssrn.com/abstract=2297934 or http://dx.doi.org/10.2139/ssrn.2297934

Contact Information

Natalia Sizova (Contact Author)
Rice University ( email )
6100 South Main Street
Houston, TX 77005-1892
United States
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