Conditional variance forecasts for long-term stock returns

Enno Mammen (), Jens Perch Nielsen (), Michael Scholz () and Stefan Sperlich ()
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Enno Mammen: University of Heidelberg, Germany
Jens Perch Nielsen: Cass Business School, City, University of London, UK
Michael Scholz: University of Graz, Austria
Stefan Sperlich: Universite de Geneve, Switzerland

No 2019-08, Graz Economics Papers from University of Graz, Department of Economics

Abstract: In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and inflation. In particular, we apply and implement in a two-step procedure a fully nonparametric smoother with the covariates and the smoothing parameters chosen via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realized conditional variance for both the one-year and five-year horizon.

Keywords: Benchmark; Cross-validation; Prediction; Stock return volatility; Long-term forecasts; Overlapping returns; Autocorrelation (search for similar items in EconPapers)
JEL-codes: C14 C53 C58 G17 G22 (search for similar items in EconPapers)
Date: 2019-08
New Economics Papers: this item is included in nep-fmk, nep-for, nep-ore and nep-rmg
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Citations: View citations in EconPapers (8)

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