Conditional Variance Forecasts for Long-Term Stock Returns

Enno Mammen, Jens Perch Nielsen, Michael Scholz and Stefan Sperlich
Additional contact information
Enno Mammen: Institute for Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
Jens Perch Nielsen: Faculty of Actuarial Science and Insurance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UK
Michael Scholz: Department of Economics, University of Graz, Universitätsstraße 15/F4, 8010 Graz, Austria
Stefan Sperlich: Geneva School of Economics and Management, Université de Genève, Bd du Pont d’Arve 40, 1211 Genève, Switzerland

Risks, 2019, vol. 7, issue 4, 1-22

Abstract: In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters 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 realised 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: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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