Conditional variance forecasts for long-term stock returns
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 procedur...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| In: |
Risks
Year: 2019, Jahrgang: 7, Heft: 4/113, Pages: 1-22 |
| ISSN: | 2227-9091 |
| DOI: | 10.3390/risks7040113 |
| Schlagworte: | |
| Online-Zugang: | Resolving-System, kostenfrei: https://doi.org/10.3390/risks7040113 Verlag, kostenfrei: https://www.mdpi.com/2227-9091/7/4/113/pdf Resolving-System, kostenfrei: http://hdl.handle.net/10419/257951 Verlag, Terms of use: https://creativecommons.org/licenses/by/4.0/ 
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| Verfasserangaben: | Enno Mammen, Jens Perch Nielsen, Michael Scholz and Stefan Sperlich |
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| 520 | |a 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. | ||
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