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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/7/4/113 |
| _version_ | 1818479088252223488 |
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| author | Enno Mammen Jens Perch Nielsen Michael Scholz Stefan Sperlich |
| author_facet | Enno Mammen Jens Perch Nielsen Michael Scholz Stefan Sperlich |
| author_sort | Enno Mammen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T09:56:37Z |
| format | Article |
| id | doaj.art-07a0f5157e934d27bf9ff6d8c5a3ab02 |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-12-10T09:56:37Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-07a0f5157e934d27bf9ff6d8c5a3ab022022-12-22T01:53:28ZengMDPI AGRisks2227-90912019-11-017411310.3390/risks7040113risks7040113Conditional Variance Forecasts for Long-Term Stock ReturnsEnno Mammen0Jens Perch Nielsen1Michael Scholz2Stefan Sperlich3Institute for Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, GermanyFaculty of Actuarial Science and Insurance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UKDepartment of Economics, University of Graz, Universitätsstraße 15/F4, 8010 Graz, AustriaGeneva School of Economics and Management, Université de Genève, Bd du Pont d’Arve 40, 1211 Genève, SwitzerlandIn 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.https://www.mdpi.com/2227-9091/7/4/113benchmarkcross-validationpredictionstock return volatilitylong-term forecastsoverlapping returnsautocorrelation |
| spellingShingle | Enno Mammen Jens Perch Nielsen Michael Scholz Stefan Sperlich Conditional Variance Forecasts for Long-Term Stock Returns Risks benchmark cross-validation prediction stock return volatility long-term forecasts overlapping returns autocorrelation |
| title | Conditional Variance Forecasts for Long-Term Stock Returns |
| title_full | Conditional Variance Forecasts for Long-Term Stock Returns |
| title_fullStr | Conditional Variance Forecasts for Long-Term Stock Returns |
| title_full_unstemmed | Conditional Variance Forecasts for Long-Term Stock Returns |
| title_short | Conditional Variance Forecasts for Long-Term Stock Returns |
| title_sort | conditional variance forecasts for long term stock returns |
| topic | benchmark cross-validation prediction stock return volatility long-term forecasts overlapping returns autocorrelation |
| url | https://www.mdpi.com/2227-9091/7/4/113 |
| work_keys_str_mv | AT ennomammen conditionalvarianceforecastsforlongtermstockreturns AT jensperchnielsen conditionalvarianceforecastsforlongtermstockreturns AT michaelscholz conditionalvarianceforecastsforlongtermstockreturns AT stefansperlich conditionalvarianceforecastsforlongtermstockreturns |