Volatility forecasting for low-volatility investing
Low-volatility investing often involves sorting and selecting stocks based on retrospective risk measures, for example, the historical standard deviation of returns. In contrast, we employ volatility forecasts from various volatility models to sort, select, and estimate portfolio weights on the 500...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
April-June 2026
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| In: |
International journal of forecasting
Year: 2026, Volume: 42, Issue: 2, Pages: 570-586 |
| ISSN: | 0169-2070 |
| DOI: | 10.1016/j.ijforecast.2025.08.006 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ijforecast.2025.08.006 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0169207025000743 
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| Author Notes: | Christian Conrad, Onno Kleen, Rasmus Lönn |
| Summary: | Low-volatility investing often involves sorting and selecting stocks based on retrospective risk measures, for example, the historical standard deviation of returns. In contrast, we employ volatility forecasts from various volatility models to sort, select, and estimate portfolio weights on the 500 largest US stocks. We find that exploiting a large set of time-series models delivers large, significant economic gains compared to traditional benchmarks. After accounting for transaction costs, a low-volatility portfolio based on volatility forecasts from a panel heterogeneous autoregression model and a portfolio based on forecast combinations perform best and can be easily implemented in real time. |
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| Item Description: | Online verfügbar: 7. Oktober 2025, Artikelversion: 17. Februar 2026 Gesehen am 25.03.2026 |
| Physical Description: | Online Resource |
| ISSN: | 0169-2070 |
| DOI: | 10.1016/j.ijforecast.2025.08.006 |