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Conditional Variance Forecasts for Long-Term Stock Returns

1
Institute for Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
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Faculty of Actuarial Science and Insurance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UK
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Department of Economics, University of Graz, Universitätsstraße 15/F4, 8010 Graz, Austria
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Geneva School of Economics and Management, Université de Genève, Bd du Pont d’Arve 40, 1211 Genève, Switzerland
*
Author to whom correspondence should be addressed.
Risks 2019, 7(4), 113; https://doi.org/10.3390/risks7040113
Received: 20 August 2019 / Revised: 21 October 2019 / Accepted: 29 October 2019 / Published: 5 November 2019
(This article belongs to the Special Issue Machine Learning in Insurance)
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. View Full-Text
Keywords: benchmark; cross-validation; prediction; stock return volatility; long-term forecasts; overlapping returns; autocorrelation benchmark; cross-validation; prediction; stock return volatility; long-term forecasts; overlapping returns; autocorrelation
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Mammen, E.; Nielsen, J.P.; Scholz, M.; Sperlich, S. Conditional Variance Forecasts for Long-Term Stock Returns. Risks 2019, 7, 113.

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