A frequency-domain alternative to long-horizon regressions with application to return predictability

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 subsampling.

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Year of publication:	2014
Authors:	Sizova, Natalia
Published in:	Journal of Empirical Finance. - Elsevier, ISSN 0927-5398. - Vol. 28.2014, C, p. 261-272
Publisher:	Elsevier
Subject:	Predictive regression \| Semiparametric method \| Local-to-unity \| Long memory \| Long-horizon regression \| Subsampling

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Type of publication:	Article
Classification:	C12 - Hypothesis Testing ; C14 - Semiparametric and Nonparametric Methods ; G12 - Asset Pricing ; G14 - Information and Market Efficiency; Event Studies ; E47 - Forecasting and Simulation
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010939524